VFAST Transactions on Software Engineering
The VFAST Transactions on Software Engineering is a peer-reviewed scientific journal published by the VFAST-Research Platform. It was established in 2013 and covers the areas of software engineering and its applications. The aim of this journal is to provide an international platform for engineers and academicians all over the world to promote, share, and discuss various new issues and development in the field of software engineering and its application in Machine learning, Bioinformatics, Image processing, Robotics, Artificial Intelligence, and data science. The Journal has started 4 issues per year from 2021 (January-March, April-June, July-September, and October-December) Editor-in-Chief: Prof. Hejiao Huang, Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China Dr. Farooq Ahmad, Associate Professor, COMSATS Lahore, Pakistan <span class="csspropertycolor" style="box-sizing: inherit; color: red; font-family: Consolas, Menlo, 'courier new', monospace; font-size: 15px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">Important Links REGISTER SUBMISSION LATEX TEMPLATE EDITORIAL BOARD The Journal is indexed by: HEC-Y Category Google Scholar Repindex Worldcat ZMATH Research Gate Index Copernicus Journal Tocs Sciences Po Publons ISSN + ROAD Ulrich’s Periodicals Directory/ProQuest <a href="http://sfx.princeton.edu:9003/sfx_pul/az?param_letter_group_save=&param_chinese_checkbox_type_save=Pinyin&param_current_view_save=table&param_langcode_save=en&param_pattern_save=VAWKUM+Transaction+on+Computer+Sciences&param_services2filter_save=getFullTxt&param_jumpToPage_save=&param_chinese_checkbox_save=0&param_starts_with_browse_save=0&param_sid_save=b2d32284a5f202b8c792b440b6535c2f&param_type_save=textSearch&param_lang_save=eng&param_textSearchType_save=startsWith&param_perform_save=searchTitle&param_letter_group_script_save=&param_type_value=textSearch&param_jumpToPage_value=&param_pattern_value=vfast&param_textSearchType_value=startsWith&param_starts_with_browse_value=0&not_first_load_indicator=1&param_ui_control_scripts_value=&param_chinese_checkbox_value=0">Princeton University Library University of Illinois Library World of Periodicals Ouci Dntb
Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). This work is licensed under a Creative Commons Attribution License CC BY
Optimized Classification of Cardiovascular Disease Using Machine Learning Paradigms
Nearly 19 million people die each year from cardiovascular and chronic respiratory diseases, which are a global threat. It is necessary to address the causes of these diseases because of the high death rate. The investigation uncovered a number of causes, but the inability to forecast these diseases symptoms is by far the most significant. In this work, we developed a method for anticipating these diseases crucial symptoms, which will aid in early disease diagnosis and allow patients to begin treatment. This research will introduce a new computational medicine research using machine learning (ML) paradigms to forecast cardiovascular disease (CVD). Data were processed by methods in sequence with various parameters. different models created that predicts CVD risk based on individual age, gender, ethnicity, body mass etc., and lifestyle factors. The research will also focus on performing complete comparison of ML models. We will apply Five ML based algorithems such as Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBOOST and Random Forest and evaluate these models on the basis of Training and Testing and also calculated the Presicion Recall and F1-Score for each model. Naïve Bayes and XGBOOST Classifier perform better with accuracy of 92.31 and 92.34 percent as compared to other models.
Decision Support System for early Diagnosis of Heart Diseases
Heart disease remains a leading cause of death worldwide, emphasizing the critical need for early diagnosis to enable effective treatment and management. Decision support systems (DSS) have the potential to significantly enhance the accuracy and efficiency of heart disease diagnosis. An advanced DSS designed for early diagnosis can provide healthcare professionals with essential information, expert advice, and treatment recommendations based on comprehensive patient data. By gathering information from various sources including medical history, risk factors, and test results, the DSS analyzes and processes the data using established diagnostic criteria and the latest medical research. The DSS then generates a diagnosis or a list of possible diagnoses, along with appropriate treatment recommendations, empowering healthcare professionals to make well-informed decisions and deliver more effective patient care. Incorporating machine learning algorithms into the DSS can further enhance its accuracy and efficiency. By training the DSS on extensive patient datasets, it can identify patterns and make predictions based on new patient data, ultimately leading to improved decision-making and better patient outcomes. In conclusion, the utilization of a DSS for heart disease diagnosis holds the potential to revolutionize the field by providing healthcare professionals with vital information, expert guidance, and treatment recommendations, thereby enhancing the accuracy, efficiency, and overall outcomes of heart disease diagnosis and management.
Azure-Based Multi-Sensor IoT Network for Smart Rice-Nursery Field
Rice farmers rely on nurseries for various reasons, as they play a pivotal role in cultivating high-quality rice crops. These nurseries provide essential seedlings for subsequent transplantation to the paddy fields. The success of rice cultivation hinges on maintaining a robust and thriving nursery, resulting in improved yields and superior grain quality. To enhance nursery management, a novel method proposes real-time monitoring of environmental conditions. A trial study evaluated the system's performance, demonstrating a significant increase in precision and efficiency, leading to higher crop yields and reduced production costs. This innovative approach has the potential to revolutionize rice nursery practices, promoting sustainability and effectiveness. The study introduces an Internet of Things (IoT)-based real-time monitoring system implemented in a rural area of Sindh, Pakistan. Utilizing a network of sensors, the system gathers vital environmental data that impacts rice nursery growth. The recorded information is then analyzed on the Azure cloud platform, and data visualization is achieved through Power BI. Additionally, an email notification component alerts farmers and agricultural experts based on the sensor data, facilitating timely actions and informed decision-making.
Diabetic Retinopathy Identification from Eye Fundus images using Deep Features
Diabetes mellitus can cause diabetic retinopathy (DR), which affects the blood vessel networks in the retina of the eye. The symptoms of diabetic retinopathy may be missing or minimal. For the majority of diabetes patients, a serious problem can damage their vision. It takes a lot of effort for competent doctors to identify lesions in the color-fundus pictures that can be used to accurately diagnose the illness required for diabetic retinopathy diagnosis. The existing Diabetic retinopathy therapies can only slow or delay vision degeneration, highlighting the significance of routine scanning with very effective automatic detection techniques to identify patients early. Therefore, early symptom detection may help prevent blindness. The proposed work aimed to create an automated model for recognizing the initial stages of DR detection using retinal pictures. This research paper presents a novel approach to the multi-classification of Diabetic Retinopathy (DR) based on a combination of deep learning and machine learning techniques. Our proposed model incorporates a Convolutional Neural Network (CNN) with an attention mechanism, allowing for the assignment of weights to extracted features depending on their relevance to the classification task. We employ Non-Negative Matrix Factorization (NMF) to further optimize feature selection to identify the most informative features from weighted representations. Then, the input features are classified using a machine learning classifier based on severity levels. The proposed model is assessed using four distinct ML classifiers (Support Vector Machine (SVM), Decision Tree, Naive Bayes, and KNN) and two publicly accessible databases (DDR and APTOS-Kaggle). For model evaluation, FPR, Specificity, Sensitivity, Precision, false positive rate, and Accuracy are selected. The proposed model's accuracy on DDR is 89.29%, whereas Aptos Kaggle's accuracy on DR grading is 84.1%. KNN classifier performed better than all other classifiers on DDR and Aptos Kaggle datasets with an average accuracy of 89.55%, and 85.78 %respectively for multiclass classification. The findings of the evaluation and experimentation demonstrate that the suggested approach is effective for DR grading accuracy and disease diagnosis at an early stage.
An empirical study of performance of block cipher algorithms in cloud computing environment
The security of private and sensitive data stored in the public domain is a major problem. It is critical for the user that data be safe both in transit and even after it has been stored on the server. The data owner must be guaranteed that the data held on the service provider site is safeguarded against data theft from outsiders, and the data must be protected even from the service providers. The secret key generation is one of the most important factors for the security of any cryptographic system because the length of the key directly affects the performance and prevents various cryptographic attacks such as brute force attacks. At the application level, our developed system efficiently secures sensitive, private, and personally identifiable information by ensuring privacy and confidentiality of data at rest in the public domain. This study also compares the performance of block cipher algorithms DES, 3DES, Blowfish, and AES. It was deduced from the result that AES consumes less time when compared to other symmetric algorithms with small consistent behavior for various cryptographic operations with small, medium and big datasets..
Using Machine Learning Models for The Prediction of Coronary Arteries Disease
Globally, the leading cause of mortality among both men and women is coronary heart disease. This disease is widely recognized as the primary killer worldwide, and its early detection poses a significant challenge. Given the current state of affairs, it is crucial to promptly identify heart disease in its initial stages to ensure successful patient treatment. Despite numerous attempts by various researchers to develop hybrid and ensemble models for early detection, the desired outcomes have not been achieved. Consequently, the machine learning and algorithmic research community has directed its focus towards improving these methodologies. In this particular study, six supervised machine learning classifiers, namely Random_Forest, extreme gradient boost, Logistic of Regression, Decision_Tree, KNN, and N-Bayes, were employed. The UCI repository dataset was utilized as the sample data, comprising attributes and corresponding values. Data preprocessing techniques were employed to eliminate any missing values. An ensemble model incorporating three algorithms, namely DT (decision-tree), RF (random-forest), and XGB, was constructed. Remarkably, the ensemble model achieved an impressive accuracy rate of 95.33% for predicting coronary heart disease.
Smart Agricultural Genetic Divergence Pattern Estimation of Morphological Traits in Cotton
Cotton (Gossypium hirsutum L.), an important agricultural fiber crop belonging to the Malvaceae family, exhibits wide genetic diversity that requires thorough investigation for the development of climate-smart cotton. This study aimed to assess the genetic variation of cotton varieties in relation to yield-related characteristics. A total of fifty genotypes were sown at the Cotton Research Institute (CRI) in Multan using a randomized complete block design (RCBD) with two replications. The row-to-row and plant-to-plant distances were maintained at 75 cm and 23 cm, respectively. Data were collected for various morphological traits, including plant height (PH), monopodial branches per plant (MO), sympodial branches per plant (SY), number of nodes (NO), boll length (BL), boll width (W), boll weight (BW), total boll weight per plant (TB), and seed cotton yield (SCY). Maximum values were recorded for PH (109.40 cm), MO (8.0500 branches/plant), SY (25.100 branches/plant), NO (41.550 nodes), BL (41.750 mm), W (41.300 mm), BW (3.9500 mg), TB (33.750 g), and SCY (95.400 g). ANOVA results indicated significant differences among all the genotypes. Positive and significant correlations were observed between PH, SCY, and BL, demonstrating the successful utilization of selection criteria based on these traits to improve cotton yields. Cladogenesis studies revealed that class I, II, and III were represented by FH-183, VH-281, and AGC-2, respectively, exhibiting superior genetic potential in terms of morphological traits. Principal component analysis (PCA) demonstrated that 81.88% of the total variance was primarily attributed to traits such as SY, TB, PH, SCY, and BW, with the first five components having eigenvalues greater than 1. These findings provide breeders with valuable insights into selecting desirable characteristics for cotton varieties.
Brain Tumor Segmentation using Deep Learning
In addition to helping doctors discover and measure tumors, it also helps them develop better recovery and treatment plans. Recent MRI brain tumor segmentation algorithms have focused on U-Net design to combine high-level and low-level features for improved accuracy. Fully convolutional networks, which are also used for this purpose, are unable to successfully reconstruct the image through the decoder path because of the insufficient and low-level information from the encoder path. More effort needs to be done to optimise the low-level information flow from the encoder path to the decoder path in order to improve image reconstruction. In this study, we suggested a transfer learning residual U-Net model that combines the U-Net and VGG-16 architectures. To improve image reconstruction, VGG-16 is combined with the encoder. Additionally, a residual path in skipping connection is included to highlight key feature details while muting noisy and unnecessary feature replies. It is trained using The Cancer Imaging Achieve (TCIA) and Brats 2018 datasets, and It makes it easier to segment small brain tumors. When compared to previous brain tumor segmentation techniques, the suggested model performs competitively.
Review of Artificial Intelligence-based COVID-19 Detection and A CNN-based Model to Detect Covid-19 from X-Rays and CT images
Various diseases are rising in the world in different regions. Each disease is diagnosed through its signs, & symptoms, and is cured accordingly. Some persons have immunity to fight against such diseases, but most of the persons become the victim of these diseases. The epidemic in China triggered by a novel coronavirus (Covid-19) presents an unprecedented danger to general safety, worldwide. Covid-19 has a more rapid transmission rate. A speedy symptomatic standard check to identify the infectious disease is required to prevent its spread. In an existing situation, testing kits of Covid-19 are available in less quantity and they require significant time to produce outcomes. The purpose of this research is to explore recently reported techniques for automated identification of Covid-19 from medical images and to report an efficient method for the detection of Covid-19 from digital X-Ray and computed tomography images. The proposed model can assist in the identification of Covid-19 at its initial level in lesser time. Publically available and locally developed datasets have been used for research and experiments. The highest classification accuracy achieved through the reported model is 99.40%.
Mobility and Health Monitoring in People with Different Abilities: A Prototype Enhancing Independence: Innovating an IoT-Integrated Wheelchair for
Wheelchair is an essential tool for people with disabilities, enabling them to move around independently and participate fully in society. They come in different types, such as manual wheelchairs, power wheelchairs, sports wheelchairs, and pediatric wheelchairs among others. Certain types of disabilities such as Monoplegia, Hemiplegia, Paraplegia, and Quadriplegia pose difficulties in using conventional power wheelchairs. To overcome these hurdles and provide ease to differently-abled individuals, an Advance Monitoring and Assistive Wheelchair (AMAW) is proposed in this work. The Prototype includes a voice-controlled system for controlling the movement of a wheelchair, an IoT-based real-time health monitoring system to monitor the vitals of the patient remotely, a fall detection system for detecting falls, a tracking system for position and location, and an alarm system to alert caretaker in case of a fall. The real-time embedded monitoring system allows the monitoring of the user’s vital signs like temperature, pulse rate and oxygen saturation and the assistive part allows the wheelchair to move around electronically either through voice or through mobile application. With the assistance of various sensors, the data can easily be monitored remotely by the caretaker at regular intervals of the time. The data display on the LCD fitted onto the wheelchair and in the designed mobile application. Furthermore, the whereabouts of the user are sent via the alert system that notifies the caretaker through GSM in case of changes in parameters and if the user has lost the balance. The vitals through the sensors on the prototype has undergone testing on number of individuals with precise outcomes. In comparison to typical joystick-controlled wheelchairs, this project excels in several aspects, such as its ability to stop or turn using voice commands and avoid collisions with people, furniture, fixed objects, and walls. The user friendly AMAW prototype with real-time monitoring, assistance and alert system may serve as a cost-effective solution in maintaining and providing an independent quality life to differently-abled individuals.
Detecting Monkeypox in humans using deep learning
The monkeypox virus is an orthopox virus that causes a contagious illness of the same name. The most visible symptom, along with fever, headache, and muscular pains, is a broad rash that develops into fluid-filled blisters. In the event of a monkeypox outbreak, swift response and efficient public health management depend on an early and accurate diagnosis. In this study, the feasibility of using deep keep learning techniques to diagnose monkeypox in humans is investigated. Long short-term memory (LSTM) networks are used to analyse time-series recordings of symptoms or patient data, whereas convolutional neural networks (CNNs) are used to process medical images of skin lesions. These models need to be trained on a large and reliable data set so that they can identify patterns and attributes that are specific to monkeypox.
The Design and Evaluation of Novel Ananimated CAPTCHA Schemes Based on Humans’ Natural Vision Capabilities
CAPTCHAs are ubiquitously found on the web these days. It is most commonly used security mechanism against bots. Numerous design variants of static text-based CAPTCHAs were proposed and implemented on the internet over the years. However due to advancements in machine learning and image processing techniques they were proved vulnerable against automated attacks. In this research a new and robust animated CAPTCHA scheme is presented which is based on human’s natural vision capabilities. The newly designed CAPTCHAs are user friendly for humans but extremely hard for bots. The security and usability is kept in mind while designing these CAPTCHAs. Various types of characters, patterns and filters are used to protect them against automated attacks. Overall, 20 types of animated CAPTCHAs are designed in this work. Further in this research, a group of participants have evaluated the usability of newly designed CAPTCHAs and solved with an average success rate of 76.85%. The analysis has proved that the proposed scheme is very much usable for humans and extremely difficult for bots.
Evaluating Accuracy of Pathogenicity Prediction Methods for Single Nucleotide Polymorphisms
Pathogenicity of single nucleotide polymorphism is the potential ability to produce disease. Testing each of the SNPs separately can lead to an erroneous measurement of the effect of the SNPs on the disease risk. In this research analysis of seven most popular tools for predicting the deleteriousness of single nucleotide polymorphisms namely SIFT, SNPs&GO, I Mutant, MUPro, Fathmn, PANTHER, and PhD-SNP was conducted. The ClinVar database was used to retrieve the pathogenic and benign SNPs, and the UniProt database to get protein sequences respectively. The SIFT, PhD-SNP, and SNP&Go outperformed all of the other prediction algorithms based on accucy and Matthews Correlation Coefficient with scores of (0.68,0.38), (0.66, 0.33) and (0.64, 0.29) respectively with highlighting error rates and recommended to avoid the use of MuPro for predicting the pathogenic variants. To improve the performance and accuracy of pathogenicity predictors the tools must be considered to upgrade.
A Novel Method for Ranking a Website Rating via Search Engine Optimization
Everyone uses the internet and a search engine (SE) to find what they need, making SE indispensable. Search engines are used to find any sort of information, which is why most companies want to rank well in SE result pages in order to reach their ideal clients. Without a website, companies nowadays have little chance of competing in the industry. As a consequence, the companies have prioritized and funded efforts to improve their website SE rankings. There are a plethora of methods that may be used to optimize a website for search engines. These methods come under a heading, namely, search engine optimization (SEO) by webmasters. In this research, we propose a methodology that combines on-page and off-page SEO strategies to improve user search and website ranking over SE. The proposed method outperforms in terms of website ranking, whereby a website ranking is improved from fifth to second position on Google SE. This significant shift illustrates that on and off page SEO can work together to boost a website visibility over SE result page.
Identification and Classification of Extremist by Topic Modeling Sentiment Analysis
Social Media forums were formerly seen as the promised land for amusement, education, and boosting purposes that energize the evolution of text analysis to acknowledge the complications of real world. A seedbed for toxic conduct, fanatical content, and political propaganda, they have now become a sump. YouTube is one of the finest platforms where millions of comments thrown per day which consists of valuable and misleading information. Comments may contain slang, foreign and misspelled words that’s very laborious to perform natural language processing (NLP) with those informal languages. Most preceding research diffused the analysis of gigantic unstructured data that comes in different formats and not simply classify in current databases. This study aims to analyze dynamic textual data from randomly selected channels on YouTube. For this ambition YouTube information API v3 was used to scrape a variety of data from YouTube videos. We investigated a few hot topics that are having mordant remarks about racism, LGBTQ, sub teen life aspect, and women and girls are just some of the areas where bullying is ordinary. In this study we summarize a custom dataset of 50 video’s comments related to rap songs and other social events to perform topic modelling sentiment analysis that dwindle the cost of data labeling and annotating. The main objective of our research is to identify and classify each word with their probability from the dataset by applying model hyper parameters alpha, beta, gamma and examine the topic coherence score to measure the semantic similarity of words from extracted topics. This study’s experimental results reveal that the analysis was able to achieve significant sentiment analysis efficiency at both the document and word levels.
Predicting the Karachi Stock Price index with an Enhanced multi-layered Sequential Stacked Long-Short-Term Memory Model
The study proposes the use of a stacked Long-Short-Term Memory (LSTM) model to predict the KSE-100 stock exchange trend and provides a comprehensive review of the literature on deep learning models and time series forecasting in the stock market. The study's findings suggest that the stacked LSTM model outperforms other models in terms of prediction accuracy. The study's contribution lies in its approach to improving the accuracy of stock price prediction using deep learning models. The stacked LSTM model architecture is a novel approach that provides better results than other traditional time series forecasting models. Furthermore, the study's use of hyper-parameter optimization techniques demonstrates the importance of model tuning for improving performance intended for accurate time series forecasting in the financial market. The study's results have practical implications for investors, who can use the stacked LSTM model to make informed decisions about buying or selling stocks in the KSE-100. The model's ability to predict stock prices accurately can help investors maximize their profits and minimize their losses. Hence, the proposed stacked LSTM model can effectively predict stock prices in the KSE-100 and can assist investors in making informed decisions in the stock market.
An Efficient approach for Firearms Detection using Machine Learning
Each year, there is a significant number of people impacted by gun-related violence globally. To address this issue, we have created a computer-based system that can automatically identify firearms, specifically pistol. Recent advancements in machine learning has shown success in the fields of recognition and object detection. Our system utilizes the You Only Look Once (YOLO V3) object detection model, which was trained on a personalized dataset. Our training results indicate that YOLO V3 outperforms both traditional convolutional neural network (CNN) models and YOLO V2. Notably, our approach did not require high computation resources or intensive GPUs to train our model. By incorporating this YOLO V3 model into our security system, we hope to rescue lives and decrease the occurrence of manslaughter or mass killings. Moreover, detecting weapons or other dangerous materials and preventing harm or risk to human life could be accomplished by integrating this system into sophisticated surveillance and security robots.
LCCNet: A Deep Learning Based Method for the Identification of Lungs Cancer using CT Scans
Lung cancer is a highly lethal disease affecting both males and females nowadays. It is essential to identify cancer accurately at the initial stage of lung cancer. However, accurately diagnosing cancer remains a challenging task for pathologists. Among the various techniques available, CT Scan plays a crucial role in the early identification and treatment of lung cancer. For the classification of lung cancer, lots of developing techniques are used in the medical research field. Unfortunately, these techniques achieve less classification accuracy due to poor learning rate, class imbalance, data overfitting, and vanishing gradient. It is essential to develop an accurate, faster, and well-organized system for the classification of lung cancer. To address these issues, an efficient framework called LCCNet is presented, which is transfer learning applied to the pre-trained Densely Connected Convolutional Networks (DenseNet-121) CNN model. LCCNet is used to accurately classify lung cancer. The most common transfer learning and data augmentation approaches are used to deal with a large dataset. LCCNet utilized CT Scans for the accurate classification of lung cancer. To assess the performance, the model utilizes various evaluation metrics such as accuracy, F1-score, precision, and recall along with a confusion matrix to validate the efficiency of the model for lung cancer classification. Furthermore, this study also compares several current studies with the proposed LCCNet model in terms of accuracy measures, showing that the proposed LCCNet model attained the greatest accuracy of 99% when compared to the various existing research fields of study. To the best of our knowledge, the proposed methodology performs efficiently.
Identification of Lungs Cancer by using Watershed Machine Learning Algorithm
The most dangerous and quickly spreading form of cancer in the world is lung cancer. In terms of fatalities among cancer diseases, it comes in first place, and therapy is made more challenging by late-stage diagnosis. Early identification and detection are crucial for treating this lethal condition, though. Benign and malignant tumors are the two forms that manifest in the early stages of this illness. These are visible with a computed tomography (CT) scan. Thanks to machine learning, these pictures can be used to determine the stages of cancer. In this study, a machine learning framework is presented using the proposed convolutional neural network techniques in order to develop a reliable and precise classification model for the diagnosis of lung cancer and to address the problem of class imbalance datasets, a general problem in medical data that results in difficulties and mistakes in prediction. The data source for the investigation was the IQ-OTHNCCD dataset. Scale Invariant Feature Transform (SIFT) and Watershed were the best feature extraction methods employed in this work, which was provided as a segmentation method. A dataset imbalance is later resolved by data augmentation, and CNN is used to achieve classification. In the malignant lung image, we finally identify the nodule. An accuracy rate of 0.97% is achieved with the proposed CNN-based classification of CT scan pictures.
Deep Emotions Recognition from Facial Expressions using Deep Learning
Deep emotion recognition has a wide range of applications, including human-robot communication, business, movies, services hotels, and even politics. Despite the use of various supervised and unsupervised methods in many different fields, there is still a lack of accurate analysis. Therefore, we have taken on this challenge as our research problem. We have proposed a mechanism for efficient and fine-grained classification of human deep emotions that can be applied to many other problems in daily life. This study aims to explore the best-suited algorithm along with optimal parameters to provide a solution for an efficient emotion detection machine learning system. In this study, we aimed to recognize emotions from facial expressions using deep learning techniques and the JAFFE dataset. The performance of three different models, a CNN (Convolutional Neural Network), an ANN (Artificial Neural Network), and an SVM (Support Vector Machine) were evaluated using precision, recall, F1-score, and accuracy as the evaluation metrics. The results of the experiments show that all three models performed well in recognizing emotions from facial expressions. The CNN model achieved a precision of 0.653, recall of 0.561, F1-score of 0.567, and accuracy of 0.62. The ANN model achieved a precision of 0.623, recall of 0.542, F1-score of 0.542, and accuracy of 0.59. The SVM model achieved a precision of 0.643, recall of 0.559, F1-score of 0.545, and accuracy of 0.6. Overall, the results of the study indicate that deep learning techniques can be effectively used for recognizing emotions from facial expressions using the JAFFE dataset.
LeafNet: Using Convolutional Neural Network for Plant Leaf Detection
Pakistan is home to thousands of plant species. As a result of pollution, natural disasters, and climate change, many of these species are at risk of extinction. Plant categorization and detection systems are designed to assist non-experts in automatically identifying plants based on their leaves to ensure their safety. The current study proposes a plant leaf detection system utilizing a Convolutional Neural Network architecture. Making use of the PlantVillage dataset, the proposed system can identify seven species of plants namely apple, cherry, tomato, potato, soybean, strawberry, and corn. Data augmentation strategies have been used to provide more training examples to get around the problem of bias and imbalanced data. The accuracy achieved on the training set was 98.87% which improved to 99.5% when using data augmentation. Apart from the monitoring of endangered species, the adoption of the proposed model can also aid the evaluation of weed management efforts and analysis of species distribution under climate change.
Using Educational Data Mining to Predict Student Academic Performance
An educational institution's primary objective is to create a learning environment that enhances student academic success by mitigating academic failure and promoting higher performance. In order to accomplish this, the institute needs an effective mechanism for quickly identifying students’ performance, in particular students at the risk of falling behind or failing a course. Using the classification approach of educational data mining, this study utilizes student descriptive, behavioral, and attitudinal data to predict academic performance at an early stage during a semester. Specifically, this study makes use of ruled-based, decision tree, function-based, lazy, multilayer perceptron, and probabilistic classification techniques for early student performance prediction. The models generated by several classifiers exhibited good performance with the model generated by the Random Forest classifier exhibiting an accuracy of 93.40% and a Kappa score of 0.9160. The experimental results of the study indicate the effectiveness of using a set of descriptive, behavioral, and attitudinal attributes to predict student performance at an earlier stage during the conduct of a semester.
RLTD: A Reliable, Link Quality, Temperature and Delay Aware Routing Protocol for Wireless Body Sensor Networks
The miniaturization of biomedical sensor nodes has paved the way for remote patient monitoring using wearable wireless sensor nodes. The network of such invasive and non-invasive sensor nodes is most commonly known as Wireless Body Sensor Network (WBSN). The healthcare applications of WBSN are delay sensitive and of critical nature, therefore, it require reliable and timely dissemination of patients’ critical data to the remote location to ensure Quality of Service (QoS). However, poor link quality (LQ) may affect QoS and result in higher transmission delay, loss of critical data packets data corruption, packet retransmissions, thereby, degrading network performance and compromising the patient’s privacy and confidentiality. Furthermore, the in-vivo wireless sensor node produces electromagnetic radiations which are absorbed by the human body thereby, causing temperature rise around the implanted sensor node. The prolonged communication of such in-vivo nodes creates a hotspot node that results in sensitive tissue damage. Therefore, ensuring hotspot-free communication is a necessity in WBSN. Considering such issues, we propose a Reliable, Link Quality and Temperature aware routing protocol (RLTD) for delay-sensitive application of WBSNs that route data through reliable nodes and good quality links to the destination node. The efficacy of the proposed (RLTD) routing protocol is confirmed by comparing results with the well-known thermal-aware protocols for WBSN.
A Novel Face Spoofing Detection Using hand crafted MobileNet
There are several uses for face spoofing detection, including human-robot communication, business, film, hotel services, and even politics. Despite the adoption of numerous supervised and unsupervised techniques in a wide range of domains, proper analysis is still lacking. As a result, we chose this difficulty as our study problem. We have put out a method for the effective and precise classification of face spoofing that may be used for a variety of everyday issues. This work attempts to investigate the ideal method and parameters to offer a solution for a powerful deep learning spoofing detection system. In this study, we used the LCC FASD dataset and deep learning algorithms to recognize faces from photos. Precision and accuracy are used as the evaluation measures to assess the performance of the CNN (Convolutional Neural Network) model. The results of the studies demonstrate that the model was effective at spoofing face picture detection. The accuracy of the CNN model was 0.98. Overall, the study's findings show that spoofing detection from photos using the LCC FASD dataset can be successfully performed utilizing deep learning algorithms. Yet, the findings of this study offer a strong framework for further investigation in this area.
Sentiment Analysis on Disputed Territory Discrepancies Using Machine Learning-based Text Mining Approach
Conducting a comparative study of real-time sentiment analysis poses a significant challenge due to the continuous variation in individuals' accents and the difficulty in quantifying them. In computer science, specifically supervised machine learning classifiers, researchers face the obstacle of lacking direct observation. This study explores the performance of well-known supervised machine learning classifiers such as SVM, K-SVM, and Multinomial Naïve Bayes. We utilize a comprehensive corpus of real-time Twitter tweets and news blogs related to disputed territories. These classifiers are trained on the Kaggle dataset for real-time sentiment analysis to achieve the highest accuracy and subsequently tested on real-time data. Notably, the kernel support vector machine performs better in real-time data, as evidenced by the substantial proportion of positive sentiments detected. Furthermore, our study offers a pathway for young scholars to assess the real-time sentiment correlation between corresponding countries, enabling predictions of potential conflicts that may lead to significant casualties
Enhancing SCTP Performance through the Selection of Appropriate Retransmission Policies
The Stream Control Transmission Protocol (SCTP) is a reliable transport protocol that provides message oriented communication services between applications. One of the critical functions of SCTP is to ensure reliable delivery of data by detecting the lost or missing packets due to transmission errors. Once the errors are detected the SCTP uses retransmission policies for immediate retransmission of data along the same or alternate path. However, the performance of SCTP retransmission policies can significantly impact its efficiency and reliability in different network conditions. In this paper, we analyzed three retransmission policies of SCTP that are (1) CWND, (2) SSTHRESHOLD and (3) LOSSRATE, and evaluated their performance in terms of network bandwidth, propagation delay and packet loss. We conducted simulations using the NS-2 network simulator and evaluated the performance of each policy under different network conditions and in each simulation the impact on throughput is analyzed. From the simulation results, the retransmission policy that uses loss rate parameter (LOSSRATE) for the transmission of data outperforms the retransmission policy that uses parameters such as congestion window (CWND) and the slow start threshold (SSTHRESHOLD). The analysis on the obtained results provides valuable insights into the tradeoffs between different SCTP retransmission policies and can help network administrators and application developers optimize SCTP performance in different network environments.
Financial Prices Prediction of Stock Market using Supervised Machine Learning Models
The process of predicting stock market movements may initially appear to be non-statistical due to the multitude of factors involved. However, machine learning techniques can be utilized to establish connections between past and present data, enabling the training of machines to make accurate assumptions based on the information. By effectively linking historical data to current data using machine learning, it becomes possible to make precise predictions regarding stock performance. These predictions can lead to substantial profits for individuals and their brokers. Traditionally, stock market predictions have exhibited chaotic patterns rather than randomness, which is why a thorough analysis of a market's historical data allows for predictions to be made. Machine learning offers an efficient means of modeling such processes. By closely aligning market predictions with actual values, the analysis's accuracy can be raised greatly. The field of stock prediction has seen a growing interest in machine learning among researchers due to its effectiveness and precision. Regression-based models are commonly employed when the objective is to forecast continuous values based on independent variables. To predict stock market closing prices for the upcoming ten to fifteen days, we used SVR, RF, KNN, LSTM, GRU, and LSTM with GRU in our study. In regression modeling, the R-squared (R2) value represents the percentage of difference explained by the independent variable(s). A higher (R2) value near to 1 indicates better performance. Our experiments yielded R2 values of 0.832, 0.832, 0.574, 0.838, 0.825, and 0.815 for SVR, RF, KNN, LSTM, GRU, and LSTM with GRU, respectively. Consequently, the most effective model for correctly predicting stock market closing prices is the LSTM learning model, which had the greatest R2 value of 0.838.
Classification of Skin Disease using Machine Learning
Erythemato-squamous disease (ESD) is one of the dermatology field's complex diseases. Due to its common morphological features, it is challenging to diagnose and generally produces inconsistent results. In addition, the physician's expertise was used to make the diagnosis based on the observed symptoms. The accurate classification of erythemato-squamous disorders is one of the dermatology field's problems that need attention, and to help with this issue, by using clinical and histopathological data, this tool will differentiate the six classes of ESD. In this research, we have applied 3 different machine learning algorithms as base models i.e. Random Forest, Decision Tree, and Naïve Bayes to classify the ESD and 5 Ensemble Meta techniques such as Voting classifier, average classifier, Stacking, boosting, and bagging classifiers to measure the accuracy. In comparison to other classifier methods, the ensemble technique employed on dermatology dataset, original dataset and clinical feature extraction to identify which model performs better on both cases. The ensemble method provides a more precise and accurate prediction of skin diseases.
Roman Urdu Sentiment Analysis of Songs‘ Reviews
To process Natural Language reviews using Machine Learning techniques is known as Sentiment Analysis. It is a way to categorize people's opinions, sentiments, and attitudes towards a specific entity. Due to easy access to the internet and smart devices, people are becoming habitual in posting reviews about any specific entity/product, they use. These reviews are very helpful for all types of users in decision-making. In the past, most of the work in Sentiment Analysis was carried out on resource-rich language but very little literature is witnessed on resource-poor languages. Very few efforts have been made to build language resources to process the Roman Urdu language. This research targets to perform Sentiment Analysis on Urdu (i.e. source-poor language) in Roman script. For this purpose, the dataset is generated from the comments on songs. Three songs from the Sub-continent music industry opt from YouTube. After pre-processing the reviews, Roman Urdu reviews are analysed using Naïve Bayes, KNN, Decision Tree (ID3) and ANN. Naïve Bayes outperforms the other classifiers and achieved 82.41% results in terms of accuracy.
Humanoid Robots: Cybersecurity Concerns And Firewall Implementation
Technology has grown more important in our lives, and scientists are developing new products to make people’s life easier and more pleasant. One of these innovations is the humanoid robot. The use of humanoid robots in our daily lives is expanding at an unprecedented rate as robots are being used in different aspects of life. The market is becoming more automated and optimized, Robotics serves as one of the primary instruments used for these reasons. Yet, security continues to pose a concern for robotics. As humanoid robots begin to function "in the open," we must assess the threats they will confront. Through the literature review, researchers found that security assessments were not performed on the robots which cause the robots to be weak against cybersecurity attacks. In this research, we perform different security assessments to identify the vulnerabilities in humanoid robots. Furthermore, different metrics were used to check and perform security assessments on the robot as well as the results of security assessments has been shown. It was shown that humanoid robots are vulnerable as anyone will be able to hack the login credentials of robot’s website as well as there are some open ports in the robot’s network which can be used by the hackers to exploit robot’s working. Based on the results of assessment methods and our findings, we gave the firewall framework which will be helpful to protect the humanoid robot against those security vulnerabilities and attacks.This firewall framework will be able to protect the humanoid robots in aspects of both network and website/webpage exploitation.
Agent-based Modeling of COVID-19: A case study of Hyderabad city
This research aims to design and develop an agent-based model (ABM) that can simulate the COVID-19 outbreak, presenting the interaction dynamics and impact of various control strategies imposed in the city of Hyderabad, Pakistan. The observations include how ABM controls the disease outbreak depending on different scenarios. The individuals are visually represented in the form of agents that can interact with each other and can make decisions depending on the situation. The observations are made by studying the dynamics of the behavior of individual agents, and their movements from one compartment to the other. The ABM model is developed in NetLogo and simulates various hypothetical scenarios that reflect the real behavior of the virus’s interaction with humans and their environment while adjusting medical, social, and demographic parameters. The study results provide details on how restricting the social interactions between individuals and their movement decreases the rate of the spread of the virus.
Sentiment Analysis of Omicron Tweets by using Machine Learning Models
The COVID-19 epidemic has been affecting a lot of individuals worldwide since 2019. It is emerging as an infectious disease that set off a disaster with far-reaching effects on things like education, economics, and health. During the coronavirus outbreak, new COVID-19 mutations such the Beta, Delta, and Omicron variants emerged, terrifying and alarmed the population. Around 6 million people reportedly died as a result of COVID-19 variations, according to World Meter. The SARS-CoV-2 omicron strain was initially identified in South Africa on November 24, 2021, and it has since spread to more than 57 nations. In this essay, we examine how people feel and act toward the omicron variation. On Omicron, we proposed an approach for determining sentiment analysis for tweets from Twitter. The analysis of Twitter data's sentiment has a lot of potential. In the intended methodology, we extract the best characteristics from the Omicron tweets using NLP techniques in Python, resulting in a dataset that can be used to train the Models. The produced dataset was employed by four ML Classifiers, including “Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)”, to accurately categorise users' emotional behavior into three categories: neutral, negative, and positive. The Class Neutral receives the best score and the Class Negative receives the lowest score based on the accuracy of the forecast level.
An Improved Framework for Sindh School Monitoring System Android App
Sindh government has presented a system for observing schools called the Sindh School Monitoring System (SSMS) Framework. One of the part of the system is SSMS app, which is based on Android. SSMS app is widely used in the Sindh province in order to monitor the school with major focus on attendance. The SSMS app has increased the system performance in terms of attendance, however several flaw are present in its current framework. This paper identifies the key issues in the current framework such as identification and verification of Monitoring assistant (MA), school search options and SMC, teacher performance evaluation, reporting, curriculum, student performance evaluation and census, school building details, SNE, school amenities, GR register, NADRA verification, rights of MAs and online reporting options in the app. The changes are proposed in the existing framework, for the said key issues, which could improve the overall system performance. In order to validate the key findings and proposed changes in the existing framework a questionnaire has been prepared and evaluated from the SSMS app users. All the app users validated the proposed changes in the framework.
Secure Architecture for Electronic Commerce Applications Running over the Cloud
E-commerce and mobile commerce are two new business methodologies that utilize the cloud. A new technology called cloud computing uses the Internet to process and store data from a network of distant computers that are dispersed around the globe. Any online transaction must have security as a necessary component. Therefore, one of the main issues with the cloud is security. If electronic commerce's security is breached, customers can lose trust in it. An unauthorized individual should not have access to or be able to intercept a customer's personal information while it is being transmitted. Data integrity is a major problem since personal information shouldn't be changed before, during, or even after it is at rest on the network. The suggested solution ensures the protection of personal data and the avoidance of security problems. We have developed a solution in this study to address issues with privacy, confidentiality, and the integrity of data stored in the cloud, among other security-related issues. The suggested method employs over-encryption that is double encryption, to avoid the various security issues. It can be inferred from the results that ECC (secp256r1) utilised less time for encryption operation as compared to others asymmetric algorithms with small dispersion from means and recorded results behaviour of data is consistent because data point tends to be very close, Decryption operation ElGamal during of time was smaller than ECC and RSA with small consistent behaviour.
Empirical Investigation on Blockchain Interoperability
With the emerging employment of blockchains in different fields a need for blockchain intercommunication has arisen but there is no set standard yet for blockchain development, adoption and implementation due to which its interoperability has become a challenge. Interoperability refers to the mechanism of exchange and utilization of information between two software or computer systems etc. In blockchain, interoperability is the process of data creation, transfer and storage between two blockchains or blockchain applications. Blockchain interoperability is complex as every blockchain may have a different implementation platform and protocol for consensus mechanism. Bringing together two different blockchains and enabling communication between them without modifying their underlying implementation structure is a challenge today. There has been ongoing research in this domain to achieve interoperability in blockchains effectively. Its importance is evident from the fact that blockchain interoperability is vital for promoting scalability which is another research challenge presently. Apart from this, blockchain interoperability also promotes data privacy, application flexibility and portability and provides new opportunities in business. In this paper we have discussed in detail the three approaches and the solutions they provide for implementing blockchain interoperability. An empirical based analysis has been used to strengthen our methodology, which takes into consideration the selection of known & established blockchain network with state-of-the-art tools and technology. In order to have seamless communication across different chains, light clients (representing the respective chain) have been enabled to store each other’s information such as protocol version etc. In this way, the handshake between both the chain has resulted in a successful IBC (Inter Blockchain Communication) inside the Cosmos environment. It is concluded that although blockchain interoperability is being implemented today, this implementation is highly restricted to specific organizations or software tools. Moreover interoperability between two different blockchains is still an ongoing challenge. This study will assist the future work in the domain of blockchain interoperability as it makes the understanding and implementation of blockchain interoperability easier.
Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey
Mental health is recognized as a non-communicable disease that impairs human lives, sometimes beyond recovery. While everyone is at risk of developing a mental illness, adolescents are more prone to it due to various factors like hormonal changes, study pressure, social pressure, etc. If mental health goes ignored at this stage, it can cause serious, even fatal problems later on in life, which not only impacts a family but also the young workforce of a country. Hence, constant efforts are being made for the early detection of mental disorders so they can be treated better. Early prediction of mental health issues is a classic machine learning problem relying on patient history and data. In this survey, we discuss a total of 22 previous research papers based on machine learning algorithms and other statistical analysis tools employed for the said task and compare their efficacy. The research papers are categorized into different mental health disorders such as 1) Methods for predicting Depression and Anxiety 2) Methods for Suidial Prevalence 3) Methods for Predicting Autism Spectrum Disorder (ASD) 4) Methods for Predicting Substance Abuse among adolescents. On the basis of accuracy, the performance of machine learning prediction models was compared. CNN models, Random Forest, and XGBoost generally performed better than other models. There is centralized research in Pakistan on mental health based on machine learning so SPSS and other tools are mostly used for data analysis. The findings suggest that Machine learning algorithms can be effective for classifying and early predicting high-risk factors among adolescents
Prioritization of Exigency Services in Multi-Agent Transportation Systems
Multi-agent system helps in achieving a single global goal by working on different tasks in a distributed environment. This research presents a new framework for handling the prioritization of exigency services in an urban transportation system. Prioritization in exigency services needs to be handled for vehicles like Ambulance, Police Mobile, Fire Brigade, Bomb Disposal Squads, Search and Rescue vehicles, Turntable Ladder, and other similar vehicles. In the proposed framework a single ARTIS agent with four In-agents is deployed at each signal node. In-Agents at different nodes share information about the exigency with the ARTIS Agent that analyses the input data and determines the types of conflict that might cause delays in emergency services provision. We operate the signal based on their priorities for exigency vehicles with different priorities. In case they have the same priorities then we use the lane congestion and vehicle wait time to operate a signal. We demonstrate the application of our proposed approach using different cases of a traffic case study.
A Survey on Security Issues and Attacks of Fog Computing
There is a link between the cloud and the Internet of Things (IoT). The layer that makes up the dispersed network environment is exactly what it is. Cloud computing is brought out to the edge of the network through the type of networking topology referred as fog computing. Users can benefit greatly from fog computing. Fog's primary role, similar to cloud computing, is to allow people mobility. Fog computing is becoming more and more popular, whereas at the same time, security dangers are growing every day. Users' identification & verification are crucial. The fact of fog computing cannot effectively utilize the security and privacy solutions provided by cloud computing must be emphasized. The risks, issues, and solutions linked to security in fog computing are outlined throughout this study. The poll then includes information on ongoing research projects as well as open security and safety concerns for fog computing.
Fake News Detection in Urdu using Deep Learning
An increase in the volume of false information circulating as a direct consequence of the rise in the growth of social media has an effect of misguiding the general population. Therefore, a mechanism for identifying fake news is required to prevent such repercussions. Almost all of these already existing algorithms for detecting fake news work with resource-rich languages such as Spanish and English; however, few techniques can work for resource-constrained languages such as Urdu. The study aims to identify instances of fake news written in Urdu by applying deep learning and machine learning methodologies. We use the MuRIL and T5 models for the implementation process because these models were developed specifically for Urdu and Hindi language recognition. The newspaper articles included in the valid subset derived from credible news sources, and the accuracy of these items have been checked by hand. Inside the misinformation subsection, the problem of how challenging it was to discover fake news was overcome by employing experienced reporters who were native Urdu speakers and instructing them to compose deceptive news items purposefully. It has allowed the researchers to overcome the problem. The dataset covers various subjects, including business, sports, health, showbiz, and technologies. We have carried out baseline classification to use our Urdu database as a standard for other datasets. The experiments with the various systems have indicated that the MuRIL model significantly improves over the other models, such as the T5 Model, and attains an average F1 score of 0.96 and a validation accuracy of 0.83. These results have been determined based on the findings of the experiments.
Hybrid technique to predict the position of Vehicle
Utilizing wireless networking to estimate the position of vehicles on the road is an important aspect of vehicular safety. It can be used to predetermine on-coming collisions and warn the drivers with ample time to either prevent the crash or minimize damage. The proposed methodology takes advantage of multipath effect by using the hypothesis test to detect a disturbance in the vehicle position estimate. The benefit of this technique is that it reduces position estimate error by integrating all of the vehicle’s data with Time Difference of Arrival (TDOA) and then passing that information through a Kalman filter to smooth the range data thereby decreasing Non-Line of Sight (NLOS) errors as well. The proposed technique accounts for error in the GPS data and reduces random error influences to significantly improve precision in position estimation and accuracy. The simulations showed that the Kalman filter improved accuracy by 5.45% and precision by 3.56% on an average as compared to TDOA position estimation without Kalman Filter.
Automated Verification of Star-Vote in the Applied Pi Calculus
Security protocols form the backbone of our modern digital society. Voting is one of the most important activities in modern democratic world. Recently proposed Star-vote voting system claims to satisfy some crucial electronic voting protocol properties. In this work, we analyze properties of Star-vote electronic voting system. We are interested in privacy, fairness and eligibility properties. Intuitively, an electronic voting protocol ensures privacy if the link between voter and his vote remains secret. Fairness property holds if no partial election results can be obtained which can influence the remaining voters. Eligibility property holds if only registered voters can vote. We used proverif to prove these properties hold. To our knowledge we are conducting first automated analysis of privacy, fairness and eligibility properties of Star-vote voting system in presence of authorities behave as intruder. Ultimately, this work will help to avoid the current situation whereby many electronic voting protocols are purposed and deployed, and does not satisfy basic security property and found to be insecure.
The Design, Use and Impact of Cloud Computing During the Covid-19 Crises
The incidence and severity of cybercrime have increased recently, and worries about personal data protection are widespread. Cybercriminals are taking advantage of the COVID-19 pandemic's crucial reliance on cloud computing emerging virtual environments by organizations and individuals. One of the biggest issues is the security of data that is stored and transmitted over the cloud. It is crucial to protect the privacy of data stored by Cloud Service Providers since in the Cloud, private and confidential information is kept on external servers and hidden from Cloud Service Users. Concerns regarding the safety of personal data are pervasive, and cybercrime has lately grown in frequency and severity. The various researchers presented their work of vulnerabilities of the data security over the cloud environment and provides encryption data at rest with symmetric key cryptography where the same secret key is used for encryption and decryption. This research takes a different strategy than that which has previously been suggested by other researches. An application-level security is proposed instead of data at rest encryption to safely store data at rest. In the public cloud computing environment, our developed system guarantees the privacy and confidentiality of data while it is at rest. In order to guarantee data security against hostile infiltration, the system also ensured that data must be safeguarded during, and after a transaction while it is being stored on the cloud storage. It can be inferred from the result that our system, when compared to other symmetric algorithms with less consistent behavior and scatter of value from mean, took less time to perform cryptographic operations.
Fuzzy smart framework for diagnosis of liver disorder in Rheumatoid arthritis
Despite most of the centralization techniques used in medical diagnosis, a right finding is yet considered a priority. Most of this condition is due to the fact that clinical issue requires both expertise and knowledge in managing with complexity. In this paper, we intend a fuzzy expert framework for studying and examining the risk of anemia and liver dysfunction due to the usage of drugstherapy (DMARDS) among patients in Rhematoid arthritis. The data illustration of this system is given from a significant level, taking into account the recorded data about signs and symptoms in rheumatic patients as well as the clinical appraisal. This framework imitates the professional doctor's behavior. The system predicts the risk of anemia and liver dysfunction in patients treated with DMARDS for rheumatoid arthritis.The framework is designed in such a manner that the patient can access it individually and has correlation with other normal indicative frameworks is quicker, less expensive, and furthermore more liable and exact.
Efficient Application Protection against Untrusted Operating Systems
Commodity monolithic systems are enormous, having lots of unprotection that leads to attacks. In this era of increasing technology, relying on mobile devices and other electronic devices increases the threat from adversaries as they have the potential to obtain physical access to many computers through breaching and security theft. It is still challenging to secure data as our operating systems are less trustworthy than we expected. When the operating system (OS) is destabilized, the application data and implementation are at the attacker's mercy. Other operating systems are less trustworthy than we imagined. Therefore when the OS is unstable, data and application implementation are vulnerable to attackers. Our protective application strategy includes a secure network platform, equipment modification, and efficiency expenditure. Existing protecting application scheme has different drawbacks, such as a vast trusted computing base, hardware refinement, and high-performance overhead. To overcome these drawbacks, we present the scheme of AppShield, which has a bare-metal hypervisor-based technique in which we safely protect the data of critical applications and their execution by application localization. Due to this, we protect only critical applications, and due to this, our operating system can run without performance loss.
Comparison of Clean Energy Trading Method in Microgrid by using Blockchain Technique
The old conventional energy distriution system cannot fulfill the demand of the prosumers in providing secure energy trading and with minimum losses in energy transmission. Because it is centralized system and user has no freedom to trade energy without involving the third party. In conventional grid there are lot of issues such as controlling the losses and forecasting the demand and reliability. One major issue is theft of electricity and losses. The new blockchain system has potential to provide secure energy trading in network without involvement of third party. The combination of blockchain and Microgrid has all features to resolve the issues related to the current grid such as controlling power, Forecasting the demand and reliability. In this paper the combination of Microgrid and blockchain for trading energy will be analyzed and energy trading method in P2P Network based will be investigated to find best method for trading enegy in network. For the simulation the matlab tool will be used.
Energy Trading in P2P Network by using Blockchain in Smart Grid
In recent years, the dramatic increase in demand of electricity utilization has introduced various environmental and sustainability concerns for Conventional Energy Grid based on fossil fuels, which driving the global energy manufacturers to adapt new efficient and resilient energy systems. The conventaional Grid is based on centralized system suffer from power losss, theft cases and cannot adopt new power systems properly. Presently the focus of the scientist and researchers is on the Clean Energy resources and Smart Grid, which has potential to address the contemporary problems found in conventional energy system. On the other side , Smart is a new technology, which allows the integration of clean energy resources such as wind, solar, geothermal, hydro, biomass, and tidal energy systems, ensuring energy availability at all times. The quintessential purpose of renewable technologies is to contrive a sustained community. Nevertheless, it has become a challenging concern for Smart Grid to integrate and coordinate enormous consignment of expanding users and connections. Consequently, blockchain has emerged as a resilient technology in various industries offering data integrity, security, building trust between parties, data immutability, and secure communication. These feature of blockchain making it an auspicious technology for decentralized Smart Grid and Renewable Energy criterion. In this research work, a blockchain based energy trading modal using Peer-to-Peer transactions management is presented. The main goal is to investigate an energy-trading model based on crypto wallets as a digital currency using smart grid and renewable energies. Moreover, contemporary issues of industrial and commercial communities adopting renewable Energy system will be address.
Disease Identification using Deep Learning in Agriculture: A Case Study of Cotton Plant
Among all the agrician products, cotton is known as “Ready Cash Crop” and it plays the significant role in the stability of the economy of a country. Therefore, it is extremely important to monitor the cotton crop from the numerous diseases. Unfortunately, sometimes human eyes not be able to analyze these diseases at earlier stage and that will affect not only the quality and also the quantity of the cotton crops. To address this early monitoring issue we proposed an interactive framework based on target feature extraction and deep learning model for cotton leaf screening to deal with these well-known dangerous diseases; Grey Mildew, Cercospora, Bacterial Blight and Alternaria. In this study we chosen our own collected dataset that contains 522 images of cotton leaves that were collected from the field (Cotton agricultural areas near the Multan city). The performance evaluation matric indicates the algorithm secure; 85.42% overall accuracy, 0.8542 precision, 0.8542 recall, 0.854 F1 score and 0.817 kappa coefficient indicates the generalization and acceptability of the model. The proposed framework not only assists the agronomist but also the farmer because of early identification of diseases from cotton crop and to avoid from the massive loss. It make better decisions for cotton crop management and contributes in the sustainability of the economy.
A Systematic Literature Review on Software- refactoring Techniques, Challenges, and Practices
Software-refactoring improves the quality and reduces the complexity during the whole life cycle of the software system. The objective of this work is to elicit the existing challenges and refactoring techniques and practices to counter those challenges. This study presents a systematic literature review based upon the scientific literature published since January 2000. After rigorously applying the SLR protocol defined by Barbara Kitchenham, we identify 36 articles discussing software- refactoring challenges, techniques, and practices. We point out complexity, understandability, and maintainability as three challenges of traditional software engineering that are resolved by software-refactoring. This SLR identifies assertions, program slicing, graph transformation, software metrics, and metamodeling, as five commonly used refactoring techniques. Moreover, eight main challenges of software-refactoring, namely determining refactoring and code location, selecting refactoring type and technique, managing refactoring dependency, consistency maintenance, refactoring ordering and increase in testing cost, are also highlighted. The findings of this work suggest that software-refactoring techniques can resolve challenges of traditional software engineering.
Application of Advance Energy Storage System in IoT and Blockchain
In many under developing countries the demand of the electricity has been increased rapidly and the cost of the electricity is become very high. The researchers are focusing on cheap energy resources for sustainable future. Only few energy options are available, such as solar power and wind power systems. These systems depend on battery storage system because, if wind speed below desire limits no electricity will generated and same conditions are for solar system, if there will be no sun light or below the desire intensity of light, no electricity can be produced. The commercially available batteries are inefficient and average battery cost 500-1000 US dollars and warranty is around only two years for lead acid battery and three years for dry battery. Previously very limited research work has been done related to topic. The only solution is availability of efficient batteries with low cost and longer life span to address the present issue. Today the demand and utilization of these batteries has been increase so rapidly in many filed such as in energy sector and IoT application. One-way is to improve battery life and efficiency by using Nano coating, Nano wire and Nano batteries. In this paper, the technique presented to improve batteries and storage system for Smartgrid. Also the blockchain technique will be analyzed to control and energy transfer and charging IoT devices efficiently. For analyzing the proposed technique, the matlab simulation tool will be use.
An Agent-based Rule Driven Knowledge Model for Epidemic Simulation: A Case Study of Lahore City.
In order to mitigate the effects of disease spread, it is necessary to understand the Spatio-temporal dynamics of epidemic spread and response evaluation. For that purpose, an agent-based heterogeneous knowledge model is synthesized using a probabilistic approach that depicts population interactions. The approach can generate a baseline population synthesis for cities such as Lahore, Pakistan where the population is quite dense and agent-based modeling becomes quite complex. An agent-based Spatio-temporal model of airborne disease is analyzed (i.e., influenza) in a metropolitan area. Such diseases result in mass casualties’ events in the populous regions, every year. Using GIS shapefiles, prior knowledge of people activities and IPUMS data, the agent base model is synthesized and an epidemic spread scenario is simulated. The proposed model can be used by healthcare officials for disease spread simulation. The model is helpful when heterogeneity semantics in population bring complexity in computations. It brings different attributes like age, sex and social status under consideration while calculating the probability of being infected as the way of interaction between people is highly depend upon them. This proposed model takes spatial factor into account as the way an infection spreads depending upon spatial distribution of infected and susceptible ones.