VAWKUM Transactions on Computer Sciences
The (Virtual AWK and Unified Modeling) VAWKUM Transactions on Computer Sciences is a yearly peer-reviewed scientific journal published by the VFAST-Research Platform. It was established in 2013 and covers the areas of Computer Sciences and its applications. The goal of this journal is to provide an international platform for computer scientists and academicians all over the world to promote, share, and discuss various new issues and development in the field of Computer Science and its application. ISSN: 2308-8168 (Online), 2411-6335 (Print) Editor in Chief: Prof. Sher Afzal Khan, Abdul Wali Khan University Mardan <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 EDITORIAL BOARD WORD TEMPLATE LATEX TEMPLATE The Journal is indexed by: HEC Category Y Worldcat ZMATH Pastic.gov.pk JournalTOCS Academic Journals Database Index Copernicus Sciences Po Sudoc Publons Journal Guide ISSN + ROAD Ulrich’s Periodicals Directory/ProQuest Oalib Open Access Library <a href="http://sfx.princeton.edu:9003/sfx_pul/az?param_textSearchType_save=startsWith&param_perform_save=searchTitle&param_letter_group_script_save=&param_chinese_checkbox_save=0&param_starts_with_browse_save=0&param_sid_save=b2d32284a5f202b8c792b440b6535c2f&param_lang_save=eng&param_type_save=textSearch&param_chinese_checkbox_type_save=Pinyin&param_current_view_save=table&param_letter_group_save=&param_langcode_save=en&param_jumpToPage_save=&param_services2filter_save=getFullTxt&param_pattern_save=vawkum&param_type_value=textSearch&param_jumpToPage_value=&param_pattern_value=VAWKUM+Transaction+on+Computer+Sciences&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 USA Library World of Periodicals BASE: Bielefeld Academic Search Engine COnnecting REpositories (CORE) Dimensions Research Bible
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Multi-labeled call transcription classification is essential for public and private sector organizations, as they spend a lot of time and workforce manually classifying phone call queries. Implementing a machine learning-based auto-classifier can effectively assist in this task, especially by reducing the time and resources required. The paper proposes an efficient call transcription classifier that not only reduces manpower but also saves time significantly. The first step in transcript cleaning involves several essential processes, such as converting the transcript to lowercase, applying word embedding techniques, and removing numbers, punctuation, and stopwords. The second step involves designing the model to incorporate four separate classifiers, each trained independently. Each classifier is comprised of an embedding layer, a bi-directional LSTM layer, and three subsequent dense layers. These dense layers utilize the rectified linear unit (ReLU) activation function, culminating in a final layer employing the softmax function. The experimental results demonstrate that all four classifiers have achieved precision, recall, and F1-score greater than 80\%. In conclusion, we conduct a comparative analysis of the results against existing studies, demonstrating that our model has exhibited superior performance.
Encryption Techniques and Algorithms to Combat Cybersecurity Attacks: A Review
The danger of cyber-attacks is constant in the current digital environment. The necessity for effective security actions are more crucial than ever before due to the increasingly complex nature of cybersecurity attacks. Using encryption approaches and algorithms is one of the best ways to secure more sensitive data from cyber-attacks. In order to effectively defend against cybersecurity assaults, this research study attempts to give an analysis of the function that encryption methods and approaches serve. We investigate various encryption algorithms and techniques, their advantages and disadvantages, and their applications. In addition, we investigate the difficulties of encryption and the potential solutions to these difficulties.
A Bidirectional Long Short-Term Memory Network and Bidirectional Encoder Representations from Transformers for Cyber-Bullying Detection
Social media has brought people from all around the world to one place to share their feelings, thoughts, and emotions with their close ones. However, using social media Cyberbullying involves harming social masses emotionally and sharing threatening content. Further, social media has become the spotlight of cyberbullying to down someone emotionally or physically with the use of aggressive words, abusive language, or even by the user of power. Machine and Deep learning models outperform to detect of cyberbullying, We Utilized Multiple Algorithms such as Naïve Bayes, Bi-directional long short-term memory (Bi-LSTM), and BERT (Bi-Directional Encoder Representation from Transformers) models, to detect bullying tweets using the pre-trained model of word2vec embeddings. This work efficiently reveals the techniques for the detection of cyberbullying over social platforms by classifying tweets for cyberbullying into five different classes: 1-Racism, 2-Age, 3-Gender, 3-Ehtnicity, 4-Not Bullying, 5-Other Bullying. The work in this paper will identify and provide cyberbullying in text, videos, pictures, and audio as an automatic tool to detect bullying and prevent the public from bullying using social media. We evaluate the model through a baseline model using the naive Bayes on the Twitter dataset. The baseline model compared with the proposed Bi-Directional LSTM using the same dataset which surpasses the baseline and achieves higher accuracy.
Evaluation of Safe Landing Site Detection Methods for Unmanned Aerial Vehicles
Nowadays, aerial vehicles (drones) are becoming more popular. Over the past few years, Unmanned Aerial Vehicles (UAVs) have been used in various remote sensing applications. Every aerial vehicle is now either partially or completely automated. The tiniest type of aerial vehicle is the UAV. The widespread use of aerial drones requires numerous safe landing site detection techniques. The paper aims to review literature on techniques for automatic safe landing of aerial drone vehicles by detecting suitable landing sites, considering factors such as ground surfaces and using image processing methods. A drone must determine whether the landing zones are safe for automatic landing. Onboard visual sensors provide potential information on outdoor and indoor ground surfaces through signals or images. The optimal landing locations are then determined from the input data using various image processing and safe landing area detection (SLAD) methods. UAVs are acquisition systems that are quick, efficient, and adaptable. We discuss existing safe landing detection approaches and their achievements. Furthermore, we focus on possible areas for improvement, strength, and future approaches for safe landing site detection. The research addresses the increasing need for safe landing site detection techniques in the widespread use of aerial drones, allowing for automated and secure landing operations.
Test Driven Development and Its Impact on Program Design and Software Quality: A Systematic Literature Review
Test-Driven Development (TDD) is a methodology in software development that necessitates tests to be written before to the production code. This approach can be used in any software development paradigm that involves writing code, including Agile, Scrum, XP, and Lean. This research paper surveys the impact of TDD on software development with a specific focus on its effects on code coverage, productivity, internal and external software quality, and the affective reactions associated with TDD. The paper also identifies potential challenges and drawbacks of implementing TDD, such as increased overhead and time consumption, a learning curve for developers, and difficulty in testing certain types of code. The studies’ results suggest that TDD can improve code coverage, and code quality, reduce defects, increase productivity and developer satisfaction, improve internal and external software quality, and ultimately lead to higher customer satisfaction.
Robot Assist Sign Language Recognition for Hearing Impaired Persons Using Deep Learning
The adoption of Sign Language Communication (SLC) systems has become more significant in closing the interaction between the deaf society and the world of hearing people. In this study, researchers aim to contribute to this field by developing a system that helps sign language users communicate through BERT approaches based on deep learning frameworks as well as NLP. Accurate recognition of sign language is critical for SLC systems to work well. Deep learning models are effective in identifying sign language with high accuracy. This study aims to determine the most suitable DL model for identifying sign language and assess the impact of incorporating Natural Language Processing (NLP) techniques in generating frequent and accurate responses in SLC systems. The NLP model will be developed as an optimum return mechanism to generate frequent responses. This research includes testing three different deep learning models: MLP, CNN, and RestNet50v2 to recognize sign language gestures. Restnet50v2 outscored the other two approaches with a 0.97% perfection. As said earlier, the system also generates automated responses using the NLP BERT model, with an overall accuracy of 0.8% and a BLEU score of 0.83%. This method has a way to enhance interaction among the deaf community via the use of technology, opening new avenues for developing intelligent chatbots that can better understand nonverbal communication. Further research can be done to expand its functionality to recognize a broader range of sign language gestures and improve the user interface. Overall, this study demonstrates how technology can enhance the ways of people with deafness or hearing loss by addressing communication barriers.
A Study Towards Exploring Access Control Mechanisms and its Limitations in Cloud Computing
Cloud computing technologies are growing fast day by day. Cloud technologies are attracting enterprises to themselves by providing great and enhanced services. There is no doubt that cloud technologies reduced the burden of the digital world by giving manageable computing services, huge room for unlimited data storage, on-demand software services, great platforms, and access control management systems. To use cloud-based manageable services users and organizations must have access to the cloud. Before using any access control mechanism, the organizations should know about the limitations of the access control mechanism. At present, many access control mechanisms are available in cloud computing. In this paper, our main goal is to identify the access control mechanisms in cloud computing and their limitations in cloud computing.
W-rank: A keyphrase extraction method for webpage based on linguistics and DOM-base features
This paper addresses the problem of an automatic keyphrase extraction for a webpage text. Our method is unsupervised, and we call it W-rank. In our method, first we extract the text of a webpage and tokenize into three different candidate words list: unigram ,bigrams and noun phrases. Then we assign score to all words based on their individual appearance in linguistic and DOM-based feature sets. In the final step, we rank these candidate words using score and select top 5 keyphrase from each list and combine them as a final keyphrases for a given webpage. We focus more on the relevancy of keyphrases to its content using linguistic features. We compare our method with other methods using precision, recall and f-score. The experimental result shows, W-rank improves the performance of our previous method D-rank and outperforms other state of art methods.
Statistical Methods in Computer Science: A Comparative Review of Inductive and Deductive Approaches
Combination issues lie at the core of computer science and modern discrete mathematics. Artificial intelligence lays the foundation for induction and deduction techniques. Induction in computer science involves establishing statements based on specific to general observations, while deduction operates in the opposite direction. In this paper, we analyze induction and deduction using three criteria: the difference in space and time complexity, inconsistencies within induction and deduction, and their application areas within the computer science domain. Induction enhances the power of observation and analysis, whereas deduction plays a crucial role in the logical realm of computer science. Deduction is primarily employed for quantitative analysis, including statistical analysis.
The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining
Finding the most dominant and pertinent user opinions on a certain topic is crucial to the sentiment analysis success factor. During the pandemic lockdowns around the world, the suspension of academic institutions leads to an exceptional increase in distance education. Academic institutions closed their campuses immediately to mitigate the effects of COVID-19 and prevent its pervasive spread, and educational activities were shifted to online platforms. The effectiveness of online education is a significant topic of interest for both students and their parents, especially in terms of how students and teachers perceive it and how technologically viable it is in a range of social circumstances. Before such a wide adoption of e-learning is possible, these issues must be analyzed from multiple perspectives. The present research aims to evaluate the efficacy of e-learning by examining individuals' perceptions of it. Opinions can be found on websites such as Instagram, Facebook, Twitter, etc. As social media has recently emerged as a significant means of communication. This study addresses factors connected to a significant change in the educational system. 200,000 tweets were gathered from Twitter to evaluate the opinions of Twitter users who were taking part in online learning. This study adopts VADER to analyze the subjectivity and polarity score of tweets, a topic model was also created using the LDA algorithm to determine the themes that were talked about on Twitter the most. The models have been constructed and evaluated using Word2Vec to capture the semantic relationships between words and LSTM and RNN sequential model for sentiment analysis. This study measured the efficiency of a sentiment analysis model using the accuracy metric, the conducted experiments reveal that the proposed hybrid model achieves an overall accuracy of 96.3%. The results also indicate a significant negative impact of the Covid-19 pandemic on individuals' emotions, with 64.4% of the analyzed tweets displaying negative sentiments. These findings provide valuable insights into the relationship between global events and individual emotions on social media platforms.
A Blockchain-Enabled Machine Learning Mask Detection method for Prevention of Pandemic Diseases
During the COVID-19 pandemic, finding effective methods to prevent the spread of infectious diseases has become critical. One important measure for reducing the transmission of airborne viruses is wearing face masks but enforcing mask-wearing regulations can be difficult in many settings. Real-time and accurate monitoring of mask usage is needed to address this challenge. To do so, we propose a method for mask detection using a convolutional neural network (CNN) and blockchain technology. Our system involves training a CNN model on a dataset of images of people with and without masks and then deploying it on IoT-enabled devices for real-time monitoring. The use of blockchain technology ensures the security and privacy of the data and enables the efficient sharing of resources among network participants. Our proposed system achieved 99% accuracy through CNN training and was transformed into a blockchain-enabled network mechanism with QR validation of every node for authentication. This approach has the potential to be an effective tool for promoting compliance with mask-wearing regulations and reducing the risk of infection. We present a framework for implementing this technique and discuss its potential benefits and challenges
Comparative Analysis of Privacy Preserving Location Based Services Mechanisms
Recent trends in computing have enabled the provision of location-based services, offering practicality and convenience to users. Moreover, this has also given rise to new challenges and vulnerabilities that can potentially compromise user privacy. As these services are predominantly used on handheld devices, the risk of security breaches is higher. This research collates existing studies that have conducted quantitative and qualitative comparisons and analyses on how to address related challenges, with a particular focus on protecting user privacy in location-based services.
A Survey on Blockchain-based Intrusion Detection Systems for IoT
The Internet of Things (IoT) is a contemporary concept that unifies the Internet and physical objects across various domains, such as home automation, manufacturing, healthcare, and environmental monitoring. This integration enables users to leverage Internet-connected devices in their daily routines. Despite its numerous advantages, IoT also presents several security challenges. As the popularity of IoT continues to grow, ensuring the security of IoT networks has become a critical concern. While encryption and authentication can enhance the security of IoT networks, protecting IoT devices against cyber-attacks remains a complex task. A successful cyber-attack on an IoT system may not only result in information loss but also potentially cripple the entire system. Intrusion detection systems (IDS) are instrumental in identifying malicious activities that could compromise or disrupt network performance. Consequently, there is a pressing need for effective IDS solutions to safeguard IoT systems. Blockchain, an emerging technology, bolsters security systems to counter modern threats. In this paper, we provide an extensive review of state-of-the-art blockchain-based intrusion detection systems for IoT applications. Additionally, we present recent advancements in addressing security concerns in a tabular format. Lastly, we identify open challenges and current limitations that warrant further exploration.
Identification of an Optimized Google PageSpeed Audit-Rule-Sequence to Optimize Page Speed
World Wide Web is a collection of online resources and websites including e-commerce, social sites, educational content, etc. To find relevant online resources, people search these by using search engines by providing their desired keywords. After filtering those keywords, search engine list the most relevant websites which are more optimized and efficient in terms of loading speed. Search engine optimization is a set of techniques used to make a website optimized and relevant to those keywords, and set the rank of a website. An online resource or website will be on the top of the search result set if it has a higher rank in search engines. Page speed is one of the most important on-page search engine optimization techniques that is used to make web site efficient in load time, so the user will get the content of the websites in a minimum time. Google has set page speed as the main factor in a higher ranking in search engines. Getting higher page speed is not an easy task, as several performance matrices must be optimized to get efficient loading time. There are many audit rules which are irrelevant or have less impact on the performance score. So selection of audit rules to be optimized is one of the main decisions before starting page speed optimization work. It will waste of time to investigate audit rules for their impact on performance scores. In this paper, we have analyzed all of the audit rules and identified the most important and relevant audit rules in optimizing page speed. A tool is used to generate the best sequence of relevant audit rules based on weighted performance benefit scores in the execution of each audit rule. The same audit rule sequence is applied on five different websites and is found more than 80% improvement in performance scores by applying the first three audit rules only and above 90% performance score obtained by using the first five to seven audit rules in our proposed audit rules sequence.
Enhancing Card Swipe Machines using Mathematical Model with JFLAP Formal Methods and Automation: A Mathematical Model with JFLAP
Automation is a novel approach that can enhance production capacity, work quality, and working environment, while minimizing labor disputes by automating all handling parameters. Formal methods are scientiﬁc techniques used to design complex mathematical systems. They involve specifying requirements and verifying software systems. The card swipe machine is a widely used point-ofsale terminal in supermarkets, medical centers, and shopping malls. Customers can easily make payments through these machines, which also provide detailed receipts of all transactions, including reversed transactions. This resolves cash management issues, improves customer service, and supports marketing. While multiple conventional machines are available in the market, they lack visual representations, making it diﬃcult to understand their working mechanism without graphical representations. Deterministic ﬁnite automata (DFA) is a mathematical model that has limited states and moves from one state to another based on input and transition functions. This study proposes the use of the JFLAP software to create a mathematical model of card swipe machine transactions. The proposed model allows for viewing each processing step in a card swipe machine, offering a new approach to understanding their working mechanism.
Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System
This study aims to develop a computer-based clinical decision support system that will help clinicians and healthcare personnel to make an early and correct decision to prevent the patient from nontransmissible respiratory diseases. The main contribution of this study is to analyze, investigate, and extraction of the useful feature of pathological respiration and Classification of Crackle and Wheeze from recorded lungs sound by using machine learning techniques. In the particular spectrogram, Time-frequency and Mel-Frequency cepstral coefficient (MFCC)technique is applied for feature analysis and data conversion into a format that can be useful for feature extraction and training models. PCA dimensional reduction technique is used to reduce the dimensionality of the extracted feature. In order to apply various machine learning techniques a widely used dataset freely available dataset ICBHI-2017 is used. The respiratory lungs sound is comprised of 126 patients with 920 Chest sound annotations that include adventitious sounds such as “Crackle” and “Wheeze”. Machine learning algorithms such as MusicANN, VGGish, and OpenL3 were applied for testing the better accuracy of the classification model. The accuracy of the utilized classifier with the extracted feature set is determined as 72%, 81%, and 69% respectively.
Identifying Cancer Patients at risk for Heart Failure using deep learning models
The cardiotoxicity that may occur as a side effect of cancer treatments has emerged as a significant problem. Cancer patient’s quality of life may be improved if those at risk of cardiotoxicity are identified early and given prophylactic treatments before receiving cardio toxic drugs. The advancement of deep learning will help to support medical practitioners in their ability to make accurate. This study will focus on predicting the enhancement of heart failure in cancer patients. The purpose of this research is to determine whether historical data from electronic health records can accurately predict the occurrence of heart failure in cancer patients. We investigated deep learning algorithms by applying them to 300 cancer patient’s dataset drawn from the Seer database. We determined that there were a total of 300 eligible cases and matched them with controls according to gender age and the primary cancer type etc. Results from the tests suggest that techniques based on deep learning may effectively capture clinical characteristics linked with heart failure in cancer patients.
A Review of Cellphone Healthcare Applications: Content Privacy and Safety Issues, Challenges and Recommendations
Nowadays, cellphone healthcare applications are receiving a considerable amount of attentiveness from both researchers and developers. Advances in communication technologies have aided the evolution in the usage of cellphones and digital devices and present challenges in terms of the accuracy and reliability of cellphone healthcare applications. Nevertheless, these applications may compromise crucial threats related to the seclusion and safety of users’ health content. Awareness of these challenges can help cellphone and cellphone application producers to manufacture efficient tools to allow patients and users to access the services of these technologies very effectively. The main objective of this research paper is to recognize the potential limitations and strengths regarding content seclusion and safety for the development and widespread utilization of effective cellphone healthcare applications. We have employed a literature survey and a relative comparison of the top ten top-rated cellphone healthcare applications to recognize security threats and characteristics that can support developers in building healthcare applications with necessary seclusion and safety standards and allow the users to select the appropriate application for their personal use.
DeepImmuno-PSSM: Identification of Immunoglobulin based on Deep learning and PSSM-Profiles
Immunoglobulin has a close connection to a number of disorders and is important in both biological and medicinal contexts. Therefore, it is crucial for illness research to employ efficient techniques to increase the categorization accuracy of immunoglobulins. Computational models have been used in a small number of research to address this important issue, but the accuracy of the predictions is not good enough. As a result, we use a cutting-edge deep learning technique with convolutional neural networks to enhance the performance results. In this study, the immunoglobulin features were extracted using the dipeptide acid composition (DPC) with the position-specific scoring matrix (DPC-PSSM) and position-specific scoring matrix-transition probability composition (PSSM-TPC) methods. we apply extracted features information from the DPC-PSSM profiles and PSSM-TPC profile by using a 1D-convolutional neural network (CNN) over an input shape. The outcomes demonstrated that the DeepImmuno-PSSM method based on sequential minimal optimization was able to properly predict DPC-PSSM accuracy score 93.44% obtained and of the immunoglobulins using the greatest feature subcategory produced by the PSSM-TPC feature mining approach accuracy score 89.92% obtained. Our findings indicate that we are able to provide a useful model for enhancing immunoglobulin proteins' capacity for prediction. Additionally, it implies that employing sequence data in deep learning and PSSM-based features may open up new path for biochemical modelling.
Evaluation of Deep Learning Approaches for Sentiment Analysis
Due to the increasing popularity of posting evaluations, sentiment analysis has grown to be a crucial area of study. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit). Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. To discover the optimal deep learning methodology for the given data, authors here proposed many deep learning methodologies for text data on sentiment analysis. A publicly available dataset including both positive and negative reviews on LSTM, CNN, RNN, and GRU was used in the experiments, and the findings showed that CNN had the highest accuracy compared to the other models. Based on the experimental results of CNN, it was found that prediction from the proposed work exhibited a significant improvement over existing work.
Research in Collaborative Tagging Applications: Choosing the Right Dataset
Collaborative tagging is an interesting approach that provides the flexibility to add description(s) to a resource according to the user’s own perception about that resource. These applications are the hottest in the areas of Social Bookmarking, Media Content Sharing and E-Commerce. Being favorite among users, these applications accumulate users’ interactions in the form of embedded datasets very quickly. These datasets are very important for further improving these applications and subsequently facilitating the user in better performing his/her activities. We feel there is a need to study these datasets to help researchers test their proposed algorithms on the right dataset and make valuable assessment and informed decisions. In this paper, we have identified measures for evaluating collaborative tagging applications’ datasets suitability for research experiments. The appropriateness of the identified measures is tested through experiments. Based on the results, recommendations are made on the suitability of the available datasets and how future dataset should look like. Researchers working not only in tagging but also in other disciplines can utilize these datasets to test their proposed algorithms without developing their own. This article provides measures which we dig out by reviewing existing available datasets. These measures are significant in selection of suitable and appropriate dataset(s), as selection of inappropriate dataset leads to errors in the results researchers are expecting. This work will prove extremely relevant and beneficial to all researchers who wish to use datasets of collaborative tagging applications for their research experiments.
Performance Prediction for Undergraduate Degree Programs Using Machine Learning Techniques - A Preliminary Review
Academic Performance prediction for undergraduate students is considered as one of the hot research areas since last couple of decades. An accurate and timely prediction of the student’s performance can directly influence the three participants; learner, instructor and the institution. This study presents a brief, preliminary review to explore existing literature from 2010 to 2022 in the context of performance prediction for Undergraduate Degree Programs (UDP). This review is organized according to Online and Traditional Education Systems (TES), and granularity level of performance output i.e., Degree program (Final CGPA), Next-semester, and the Course level grades. Aggregate analysis of the extracted data reveals that course level prediction is highly worked area deploying classification and regression techniques using data from academic domain. Existing empirical studies are mostly evaluated using accuracy, precision, recall and F1-measure and are validated with 10-fold cross validation. Contribution of this study is the novel categorical distribution of studies with respect to education system and granularity levels. Another important finding was the Success ratio of different Machine learning (ML) techniques used for these prediction studies. It is concluded that further research is required for TES to discover interdependent group of courses and Course Clusters for a certain degree program and then to develop prediction models for those course clusters.
XGboost-Ampy: Identification of AMPylation Protein Function Prediction Using Machine Learning
A developing post-translational modification known as AMPylation involves the formation of a phosphodiester bond on the hydroxyl group of threonine, serine, or tyrosine. Adenosine monophosphate is covalently attached to the side chain of an amino acid in a peptide during this process, which is catalyzed by AMPylation. We used AMPylation peptide sequence data from bacteria, eukaryotes, and archaea to train the models. Then, we compared the results of several feature extraction methods and their combinations in addition to classification algorithms to obtain more accurate prediction models. To prevent additional loss of sequence information, the PseAAC feature is employed to construct a fixed-size descriptor value in vector space. The basic feature set is received from 2nd features extraction method. All of this was accomplished by deriving the protein characteristics from the evolutionary data and sequence of the BLOUSM62 amino acid residue. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. In this research, we proposed framework for AMPylation identification that makes use of the XGBoost algorithm (AMPylation) and sequence-derived functions. XGBoost -Ampy has an accuracy of 86.7%, a sensitivity of 76.1%, a specificity of 97.5%, and a Matthews’s correlation coefficient (MCC) of 0.753 for predicting AMylation sites. XGBoost -Amp, the first machine learning model developed, has shown promise and may be able to help with this problem.
The Signiﬁcance of IoT: A Healthcare Systems Perspective
In the current era of modern technologies, the health of the patient demands real time monitoring system. This dynamic system can be developed by using efficient sensors, network and internet cloud either wire or wireless. For example, for heart patient blood pressure and pulse must be measure constantly, in case if the patient is in moving and changing his position. For this purpose, an efficient system is required. In future there will be many other problems such as viruses attach detection, dingy fever detection, and sugar problems. For all these problems there will be multiple parameters of patient must me monitor and control. In this paper a method will be device to monitor all these parameters in real time. Moreover, we are concentrating on using mobile agents to provide patient assistance and healthcare services in order to help with the diagnosis of patient’s illnesses Furthermore, platform-agnostic solutions for healthcare data collection and dissemination over NoSQL are being studied. The Apache Jena Fuseki NoSQL database with the JAVA Example Application Framework -JADE client platform was used in testing environment. The consequences show that No Structure Query Language version beats the rel-database implementation.
Business Growth Prediction Using Social Media Data Analytics
Marketing has become an important aspect of the growth of any business. Without good strategies for sharing content across social media platforms, no business can ﬂourish and will lose its customers. Many social media platforms provide various analytics tools for free. These tools provide analytics of content engagement. Many proprietary tools provide such features with enhanced capabilities. These tools require shared content of users for analysis. These tools do not exactly state the content being shared. Businesses need marketing teams for creating attractive content. However, a lot of money is needed for hiring skilled personnel. This research proposed an eﬃcient platform that provides analysis of content shared by successful businesses on social media platforms. Firstly, it’s free and public shared data, no surveys to be done because the analysis is done on businesses who are experts in marketing, and secondly, there is no headache for searching trends because the analysis is done on real-time data.
Questgator: A Platform for Content Aggregation and Text Classification
The Web has witnessed a surge in content over recent years. Content is revolutionizing the way people conduct business, communicate, and make informed decisions. However, the vast amount of data used for communication today is often unstructured and challenging to comprehend. Content aggregators provide a solution to this problem by collecting data from various sources and organizing it into a structured format in one place. This research proposed the content aggregator "Questgator" that extracts content for example news, scholarships, jobs, books, video content, and research papers. In this paper Naive Bayes theorem is used for text classiﬁcation. Moreover, paper also provides comparison with other platforms to show the eﬃciency of proposed content aggregator.
AOPs-XGBoost: Machine learning Model for the prediction of Antioxidant Proteins properties of peptides
Abstract Antioxidant proteins are essential for protecting cells from free radicals. The accurate identiﬁcation of antioxidant proteins via biological tests is difﬁcult because of the high time and ﬁnancial investment required. The potential of peptides produced from natural proteins is demonstrated by the fact that they are generally regarded as secure and may have additional advantageous bioactivities. Antioxidative peptides are typically discovered by analyzing numerous peptides created when a variety of proteases hydrolysis proteins. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. We suggested a machine-learning model that we named AOPs-XGBoost, built on sequence features and Extreme Gradient Boosting (XGBoost). We used 10-fold cross-validation testing was performed on a testing dataset using the propose. AOPs-XGBoost classiﬁer, and the results showed a sensitivity of 67.56%, speciﬁcity of 93.87%, average accuracy of 80.72%, mean cross-validation (MCC) of 66.29%), and area under the receiver operating characteristic curve (AUC) of 88.01%. The outcomes demonstrated that the XGBoost model outperformed the other models with accuracy of 80.72% and area under the receiver operating characteristic curve of 88.01% which were better than the other models. Experimental results demonstrate that AOPs-XGBoost is a useful classiﬁer that advances the study of antioxidant proteins.
Formal Specification and Verification of Distributed Denial of Service (DDoS)
DDoS (Distributed Denial of Service) attack is the main cause for interrupting the requests of users. DDoS uses more than one IP addresses. It makes botnets (machines that are affected with malware), through which interruption of a service begins and the requests are either denied or delayed to the legitimate users. A protocol is introduced in [DDoS Attack Detection Method Based on Network Abnormal Behavior in big data environment, CoRR, vol. abs/1903.11844, 2019] for the detection of DDoS in which a threshold is defined for detection of illegitimate users. We exhibited simulating of the protocol using a model checker UPPAAL and formally verified the functional requirements of the protocol to determine the system’s accuracy.
Formal Verification of Twin Clutch Gear Control
Twin clutch model enables the power-shifts as conventional planetary automatic transmission and eradicates the disadvantages of single clutch transmission. The automatic control of the dual clutches is a synchronization problem. Particularly, to control the clutching component that engages torque when running in one direction of revolution and disengages when running in the other direction, which exchange the torque smoothly during torque phase of the gear-shifts on planetary-type automatic transmissions, seemed for quite a while hard to compensate through clutch control. Another problem is to skip gears during multiple gearshifts. However, the twin clutch gear control described in ["M Goetz, M C Levesley and D A Crolla. Dynamics and control of gearshifts on twin clutch transmissions, Proceedings of the Institution of Mechanical Engineers, Journal of Automobile Engineering 2005"], a significant improvement in twin clutch gear control system is discussed. We formally specify the algorithm for the twin clutch gear control system and verify it using the model-checking. Formal methods have a high potential to measure correctness of communicating protocols. We use UPPAAL for formal specification and verification. Our results show that the twin clutch gear control model partially fulfills its functional requirements
Pneumonia Classification from Chest X-ray Images Using Pre-Trained Network Architectures
Pneumonia is a serious disease caused by a lung infection that affects young and old people and approximately cause of 4 million deaths each year. Patients that are facing disorders such as weak immune systems, asthma, and babies all are at risk specifically if pneumonia is not detected at an early stage. An early diagnosis of pneumonia is required to plan a potential treatment strategy to control and treat the condition. The objective of this study is to analyze chest radiograph images to identify lung abnormalities using pretrained architecture. After extracting features from the images using convolutional neural network models that have been pre-trained on a large dataset called ImageNet, they are typically passed through a classifier for further processing and diagnosis. Pre-trained networks variants including VGG16, VGG19, DenseNet121, ResNet50, and InceptionV3 architecture were utilized in this study & results show that VGG-16 architecture performance is effective with a test accuracy of 90% and validation accuracy of 93.98% than other pretrained architectures.
Application of Blockchain Technology in Energy Sector for Sustainable Future
In old energy control system, there was no freedom for users to sale or purchase energy with their own choice and rate because system was centralized and also there were many issues such as electricity theft cases and losses and burden of these losses directly inculdded in public bills. Now after the introduction of blockchain these issues has been resolved such as the blockchain technology give freedom to user to sale or purchase energy according to the own rate with secure transaction system. The conventional energy system with centralizedcontrol technique fails to resolve many issues such as freedom to user’s to sale or purchase energy in network in secure way and combination of blockchain and Microgrid has all feature to resolve all present isssues. In this paper, the blockchain technology features and application of blockchain with clean energy system will be investigate to provide cheap electricity to users by using Microgrid because Blockchain provide complete solutions for energy distribution and trading. In addition, somemajor present issues will be focus such as low eﬃciency, losses and ant algorithm model to distribute the energy in the network eﬃciently.
Design and Analysis of an Anthropomorphic Finger with Three Points Pneumatic Actuation
The human being’s hand is a set of fingers and can achieve any shape to hold or grip or grasp the object of different shapes. In the industrial sectors, various robot manipulators with different types of end-effectors (robot grippers) are used to perform tasks like gripping the object, picking & dropping the object, holding the tool, etc. The different types of end-effectors are required for grasping according to the geometry of the object. In this paper, an anthropomorphic finger is designed to mimic the human being’s finger to achieve any shape as the human’s finger and this finger can be a part of the robotic hand easily. This single type of robotic hand can perform all tasks for grasping any geometry of the object. The proposed finger consists of three pin joints with three pneumatic muscles, which help to achieve the desired shape with maximum holding/grasping power. The CAD model is developed to visualize and analyze finger motion. The comparison of the proposed finger and a real human finger is presented for validation of results. The results show that the proposed system can mimic the human finger and shape modulation.
AI and Machine Learning-based practices in various domains: A Survey
In several projects in computational biology (CB), bioinformatics, health informatics(HI), precision medicine(PM) and precision agriculture(PA) machine learning(ML) has become a primary resource. In this paper we studied the use of machine learning in the development of computational methods for top ﬁve research aeras. The last few years have seen an increased interest in Artiﬁcial Intelligence (AI), comprehensive ML and DL techniques for computational method development. Over the years, an enormous amount of research has been biomedical scientists still don’t have more knowledge to handle a biomedical projects eﬃciently and may, therefore, adopt wrong methods, which can lead to frequent errors or inﬂated tests. Healthcare has become a fruitful ground for artiﬁcial intelligence (AI) and machine learning due to the increase in the volume, diversity, and complexity of data (ML). Healthcare providers and life sciences businesses already use a variety of AI technologies. The review summarizes a traditional machine learning cycle, several machine learning algorithms, various techniques to data analysis, and effective use in ﬁve research areas. In this comprehensive review analysis, we proposed 10 ten rapid and accurate practices to use ML techniques in health informatics, bioinformatics, computational and systems biology, precision medicine and precision agriculture, avoid some common mistakes that we have observed several hundred times in several computational method works.
Hate Speech Detection Model on Web 3.0 Based Platform using Blockchain and NLP
Abstract With the increased usage of social media applications like Facebook, Twitter, or Instagram, hate speech is also rising. Hate speech can be deﬁned as ill talk toward any race, caste, religion, or ethnicity. Now with the new development of web 3.0, which is decentralized, it is challenging to control elements like hate speech because there is no central body that can control it. This research paper presents a novel approach for detecting hate speech on web 3.0-based platforms using blockchain technology and natural language processing (NLP) techniques. The proposed model utilizes blockchain to ensure the immutability and transparency of the data, while NLP algorithms are used to analyze and classify the text. The experimental results show that the proposed model achieves high accuracy in detecting hate speech, and the use of blockchain technology enhances the trustworthiness and security of the system. The proposed system can effectively detect and mitigate hate speech on web 3.0-based platforms and may serve as a valuable tool for promoting online safety and inclusivity.
K-Nearest Neighbor Classifier for Classifying user Reviews on Social Media Networks
Gigantic content generated on social media networking sites have made the online users enabled to communicate their opinions and sentiments about products and other entities like political events etc. Opinion mining applications aim to provide facilities to user and companies for know about products in which they are interested. In this work, opinion mining system for comparative reviews is developed using supervised machine learning approach. For this purpose, K-nearest neighbor classifier is trained on a publicly available dataset. Effectiveness of the system is validated by comparing its performance with other classifiers.
Pathway-disease Association Prediction Based on Graph Regularized Logistic Matrix Factorization (PDA-GRLMF)
Complex alterations to the cellular machinery occur as a result of diseases. There are distinctive patterns associated with a disease in the gene expression proﬁle of the affected cells. As a result, these proﬁles can be used to extract additional biological information about an illness, which helps us better identify and evaluate disease risks. Human pathway-disease interaction research is a recurrent area of interest for the biomedical community. Finding the processes or connections between diseases and pathways can be aided by this association. This paper provides an overview of human pathway and human disease, with the accuracy of disease identiﬁcation has been less than satisfactory. In predicting disease-pathway interactions, this study suggests a computer model. In this research study we proposed the Graph Regularized Logistic Matrix Factorization (GRLMF) method for pathway-disease association prediction. A cutting-edge computational model called the PDA-GRLMF disease-pathway association model can predict probable pathway-disease associations. The model can also assist pathologists in comprehending the relationships between diseasepathway linkages, therapies, and outcomes. In order to increase the association between disease variation and new molecular correlations between genetic mutations, we carried out a pathway-based investigation. On the basis of shared gene interactions among pathways-disease, we created a biological network, and then we used network analysis to try and understand how a disease constructed the pathway-pathway network and then disease-disease network. To merge the gathered biological data, which was based on the pair similarity of sequence expression weights, we employed the heterogeneous network of pathway-disease relationships. The ROC (AUC) score achieved for the best prediction results was 0.8018%, and the precision-recall curve had two classes. These ﬁndings suggest that our strategy outperforms previously suggested methods in terms of scientiﬁc performance. By contrasting them with established connections and conducting a literature search, we projected relationships between pathogen, DD, and disease-pathway.
Identifying disease genes based on machine learning approaches for classiﬁcation
In recent years, researchers have become increasingly interested in disease-gene association prediction. In the postgenomic era, this is one of the toughest jobs around. It is also challenging to determine biological research since complex disorders sometimes have very varied genotypes. Machine learning methods are used widely in the identiﬁcation of crawl marks, but their images depend heavily on their quantity and quality. In crawling studies, we ﬁnd that the recognition of genes reconciling diseases can be improved by an machines classiﬁer qualiﬁed in practical gene seamlessness from gene ontology (GO). In order to predict the genes of the disease, we’ve developed a supervised machine learning system. In the proposed pipeline, the use of autism spectrum disorder (ASD) is assessed. Similarity tests from various semantics have been used to quantitatively measure similarity in gene function. In this paper we suggest various techniques for classifying data from one-hot encoding method. This experiment is complicated by the fact that the into training and test sets. This is generally called an algorithm evaluation divided-train-test split method. ASD is a disease associated with high health care costs and early intervention will signiﬁcantly minimize these costs. ASD is a neurodevelopment disorder. Unfortunately, wait times are lengthy for an ASD diagnosis and treatments are not cheap. The economic effects of autism and an increase in ASD cases worldwide show an urgent need to establish methods of screening that are quickly enforced and eﬃcient. A timely and affordable ASD screening is therefore imminent to help health practitioners and to let individuals know whether they will be formally diagnosed clinically. Classiﬁers qualiﬁed and validated for ASD and non-ASD genes work better than ASD classiﬁers previously reported. For instance, in order to predict new ASD genes, the complementary forest classiﬁcation (CF) classiﬁcation reached AUC 0.80 above the reported classiﬁcation (0.73). Continuing, 73 novel ASD candidate bases can be predicted by the classiﬁer function. Such genes enrich the central ASD syndrome, such as autism and compulsion.
Analyzing updates in Amino Acid Composition and Translation Algorithm towards Predicting Membrane Proteins using Machine Learning Approaches
Membrane proteins are of different types that take on different functions. Classification of protein sequences in a data set is very important for understanding cell functions, disease prevention, and drug discovery. Initially, traditional methods were used for transmembrane protein classification. However, due to advanced technology and new research, it increases the transmembrane protein datasets by thousands which are almost impossible to obtain accurate results based on traditional methods. Computational methods are very useful for membrane protein classification. Several methods such as Pseudo Amino Acid Composition (PseAAC) can extract many silent features of a protein sequence. In this work, we intended to modify an existing algorithm of amino acid composition and translation to extract membrane protein features with better accuracy. To validate our algorithm, we will use the Support Vector Machine SVM and KNN.
Specification of Operating Rules for Water Reservoir to Manage Flood Using Z-Notation
Flood is natural event; it brings a lot of destruction. Due to flood not only loss of human life and misery of millions of people occur, but tremendous damages occur in public and private property. It cannot be stopped, but can be managed to save the lives and property of the people. To manage a flood, there are two solutions, physical and logical. In physical solutions, different physical constructions are made to manage flood like levee, channel improvement, flood ways, and widening of barrages, but these control works have many deficiencies. In logical solutions, models and techniques are developed to manage the flood, however, these systems also have some deficiencies. To overcome the deficiencies in the existing models, we focus on to develop a flood management model consisting of an off-river reservoir and diverted canals having regulators. Reservoirs are most effective water storage that smooth down extreme inflow. The optimal operations of reservoirs determine the release and accumulation of water over time. For effective operative decision, operating rules will be defined in Z-Specification. For these operating rules, we will develop an algorithm, will verify the rules in Z-Notation and will implement them in the Java programming language.
Discrimination of SARS-COV2 virus protein strain of three major affected countries: USA, China, and Germany
In this paper, we discuss the discrimination of SARS-COV2 viruses associated with three major affected countries the USA, China, and Germany. The discrimination can reveal the mutation as the result of viral transmission and its spread due to mutation associated with its protein structure which makes small changes in the Spike protein. To investigate the mutation in SARS-COV2, we downloaded the protein strains associated with the USA, China, and Germany from the UniProtKB by advance search through SARS-COV2, country name, and protein name: Accessory protein 7b, 6, ORF3a, 10, 8 protein, Envelope small membrane protein, Nucleoprotein, Membrane protein, Spike glycoprotein, 3C-like proteinase, and 2'-O-methyltransferase. After retrieving the protein sequences, we transform the biological form of sequences to their equivalent numerical form by using statistical moments. Further classification algorithms like Random Forest, SVM are used for their training and classification. Finally, performance evaluation is carried out using K-fold cross-validation, independent testing, self-consistency, and jackknife testing. The result received through all testing is more than 97%, which shows the visible discrimination among the protein strains of mentioned countries, which shows the strong mutation in SARS-Cov2 sequences.
New Approach to Distributed Flood Prediction Model using Agents based Communication
Flood is the overflow of water from its normal ways to submerges land which is usually dry. It is clear that floods cannot be stopped, but the timely prediction and management is the only way to combat this hazard. Much work is documented on physical and logical solution to predict on the basis of risk to save people and reduce the destruction of their belongings. In this work we developed a logical model based on client and server agent communication to predict the hazard. In the process, initially a conceptual model is developed, further, an algorithm is constructed. Moreover, for it correctness and verification the algorithm is transformed to formal language Z. Finally the specified formal model is implemented using JAVA programming language.
A Survey of Feature Extraction and Feature Selection Techniques used in Machine Learning-Based Botnet Detection Schemes
Machine learning techniques have been widely used for the classification of botnets as they have been argued to have improved strengths compared to signature-based approaches. The level of performances of some of these detection schemes have been traced to the relevance of the features used for the classification models. Therefore, extraction and selection of the most discriminative features in the classification of botnets is an important research area. It has equally been found that when a Machine-learning based approach is being used to identify botnets, the dataset chosen has to be real and representative. Feature extraction and Feature Selection are necessary steps prior to using a Machine Learning-based classification algorithm for identifying botnets. The reason for the pre-processing and feature selection steps in Machine Learning-based model is to be able to remove irrelevant and redundant data in the experimental dataset, minimize computational complexity, and increase both model simplicity as well as accuracy. This paper provided a survey of various feature extraction and feature selection methods that have been used by researchers that proposed Machine-Learning based botnet detection models. The main purpose of this approach is to provide a better understanding and insights on how improved botnet detection mechanisms can be achieved through enhanced feature extraction and selection methods.
Prediction of Saudi Arabia SARS-COV 2 Diversifications in Protein Strain Against China Strain
The recent outbreak of a COVID-19 pandemic infecting millions of people has sparked a global health emergency. It was named SARS-COV2 because of its generic similarity to the existing SARS-COV of the 2003 coronavirus pandemic. Scientists work to find the effective vaccine for its prevention and, on the other hand, work on its origin, mutation, and evolution to determine its severity from area to area is also under great concern. In this work, focused on the second part, proposes the identification of protein strains associated with the locality in Saudi Arabia as opposed to strains associated with China. For this purpose, the data of both countries were collected from the UniProt database. The identification was carried out by using machine learning, in which the biological data was transformed to numerical form by a hybrid approach using Amino Acid Composition (AAC) and Statistical Moment, and further classifications are carried out using Random Forest and SVM. For the model evaluation, 10-Fold validation and the Jackknife test were applied and achieved high accuracy. These results of high predictions reveal the noticeable variation in both the set of sequences, which shows diversification between coronavirus strains of both the countries Saudi Arabia and China which are the clear indication of sequence mutation.
Attacks Analysis of TCP And UDP Of UNCW-NB15 Dataset
TCP (Transmission Control Protocol) and UDP (User Datagram Protocol) are the most important protocols in complete protocol architecture. There are many types of attacks that can block the communication or reduce the performance of a protocol. This study provides a detail analysis of TCP and UDP attacks and their application layer protocols. The authors will also suggest that where the security administrator should focus for providing best security. The old datasets such as KDD99 and NSLKDD has many limitations. This study uses UNSW-NB15 dataset which has recently been generated.
Promising Compounds for Treatment of Covid-19
The study spans over-identification of phytochemicals suited for the treatment of COVID-19. The study focuses on the chemical that has a tendency to bind with Human ACE2 protein and two of the main Sars-Cov-2 proteins which are the Spike protein and the RNA-directed RNA polymerase (RdRp) protein. After screening a large list of phytochemicals two of the compound i.e. Kansuinine B and Digitoxin were found to have promising traits for the treatment of COVID-19. Both the compounds have been in use for centuries. Digitoxin was extracted from Foxglove seeds in the 18th century for heart-related illnesses. Kansuinine B originates from a Chinese herb Euphorbia Kansui (E. Kansui) E. Kansui has been widely used in herbal medicine for a multitude of illnesses including lungs related diseases. Studies also show that it has the ability to suppress cytokine response through the expression of the SOCS3 gene. In-silico simulations show that both these compounds have a better affinity and binding properties with these three proteins as compared to many other drugs under trial for COVID-19 like Remdesivir, Ritonavir, Famotidine, Camostat Mesylate, and Hesperidin. A treatment based on the combination of both compounds can be very effective. Any self-medication of both the compounds is highly discouraged as misuse of both the compounds can be very harmful.
Anomaly Based Intrusion Detection System Which Analyze the Dataset and Detect Intrusion
As the research increased in computer science highlight the scientists mind for the growing research world towards security. Researchers have done a lot of research work in network Security. Cybersecurity has progressively become a zone of alarm for officials, Government agencies and industries, including big commercialized infrastructure, are under attack daily. First signature-based intrusion detection systems were developed, and it detects only novel attacks. To detect strange attacks statistical IDS came into being recognized as anomaly-based IDS. It is not as much efficient as it detects all. In this, study the author focus on the efficiency of IDS using NSL-KDD99 dataset and support vector machine (SVM) technique to identify attacks. NSL-KDD dataset is used for the evaluation of these type of systems.
A NEW RELATION IN Q-POCHHAMMER SYMBOL WITH APPLICATIONS
In this paper, we propose and investigate a new relation in q-Pochhammer symbol. Further, we establish q-bracket, q-factorial and q-binomial coefficient in term of q-Pochhammer symbol using our proposed relation. As an application, we express q-Pochhammer symbols in term of ordinary equations to define new surface graphs.
Theoretical Framework of Agent Assisted Treatment for ASD (Autism Spectrum Disorder)
kids with Autism Spectrum problems (ASD) have conversation deficits and problems with social interaction. A lack of social conduct can bog down healing interventions and may lessen the potential to examine social talents. Robots had been shown to initiate proactive social conduct in youngsters with ASD. improvement of robot systems able to appearing as catalysts for social conduct in the context of ASD remedy is the need of contemporary era. A methodology and theoretical framework is being supplied here in this paper for a robotic system that can not only compare the effects and stage of ASD, however, can assist the victim consequently.
Algorithms for Data Cleaning in Knowledge Bases
Data cleaning is an action which includes a process of correcting and identifying the inconsistencies and errors in data warehouse. Different terms are uses in these papers like data cleaning also called data scrubbing. Using data scrubbing to get high quality data and this is one the data ETL (extraction transformation and loading tools). Now a day there is a need of authentic information for better decision-making. So we conduct a review paper in which six papers are reviewed related to data cleaning. Relating papers discussed different algorithms, methods, problems, their solutions, and approaches etc. Each paper has their own methods to solve a problem in an efficient way, but all the paper have a common problem of data cleaning and inconsistencies. In these papers data inconsistencies, identification of the errors, conflicting, duplicate records etc problems are discussed in detail and also provided the solutions. These algorithms increase the quality of data. At ETL process stage, there are almost thirty-five different sources and causes of poor quality constraints.
GPGPU Virtualization Techniques a Comparative Survey
The Graphic Processing Units (GPU) are being adopted in many High Processing Computing (HPC) facilities because of their massively parallel and extraordinary computing power, which makes it possible to accelerate many general purpose implementations from different domains. A general-purpose GPU (GPGPU) is a GPU that performs computations that were traditionally handled by central processing unit (CPU) to accelerate applications along with handling traditional computations for graphics rendering. However, GPUs have some limitations, such as increased acquisition costs as well as larger space requirements, more powerful energy supplies, and their utilization is usually low for most workloads. That results in the need of GPU virtualization to maximize the use of an acquired GPU to share between the virtual machines for optimal use, to reduce power utilization and minimize the costs. This study comparatively reviews the recent GPU virtualization techniques including API remoting, para, full and hardware based virtualization, targeted for general-purpose accelerations.