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Edge Computing for Distributed and Cooperative Media Algorithms in Connected Networks

Edge Computing for Distributed and Cooperative Media Algorit...

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International Conference on Computing and Networking Technology (ICCNT)

作者： V. Lilly Grace Anchal Gupta Rakesh Kumar Giri Tanuja Satish Dhope Mani Sarma Vittapu Dharmesh Dhabliya Department of computer science and engineering G.pullaiah college of engineering and technology Kurnool Andhra Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Computer Science & Engineering Sunrise University Alwar India Electronics and Communication Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune Maharashtra India Computer Science and Engineering Mallareddy Engineering College and Management Sciences Kistapur Village Medchal Hyderabad Telangana India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Side Computing is a rising technique used for connected networks’ allotted and cooperative media algorithms. Part Computing refers to processing operations being carried out near the source of the facts, generally close to the edge of the network, in place in a centralized cloud. It allows information to be processed quickly, securely, and reliably in remote or low-connectivity locations, allowing dispensed computing operations inside connected networks. This distributed machine enables latency when working with significant facts sets and enhances security and reliability. Cooperating media algorithms, including laptop vision, audio processing, or natural language processing, are frequently optimized using aspect Computing in related networks. The advantages of facet Computing allow for progressed scalability, green helpful resource utilization, and better latency performance, mainly in areas that suffer from restrained or negative networking situations and bandwidth. Moreover, aspect computing enables enhanced privacy, protection, and personal revel when handling sensitive information and data stored within the cloud.

关键词： Performance evaluation Portable computers Scalability Media Natural language processing Reliability Security Resource management Protection Edge computing

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Advanced Hyper-Tunning Strategies for Global Mean Based Feature Selection and Creation for enhanced Accuracy

Advanced Hyper-Tunning Strategies for Global Mean Based Feat...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Maradana Durga Venkata Prasad Srikanth T Durgesh Mishra Department of Computer Science and Engineering Gandhi Institute of Technology and Management (GITAM) GITAM University Visakhapatnam Andhra Pradesh India SCSIT Symbiosis University of Applied Sciences Indore India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Clustering accuracy is crucial for effective data analysis in unsupervised learning tasks, with the usage of the simple and efficient cluster analysis technique named as K-means technique. However, standard K-means suffers from limitations such as sensitivity to random initialization and challenges in handling high-dimensional data with irrelevant features. This paper presents a hyper-tuned approach to improve K-means clustering accuracy by Unifying Global Mean-Based Nearest Feature Object Selection for robust Centroid initialization with a Feature Creation Method Based on Important Feature Selection. The proposed method first enhances Centroid initialization by selecting points close to global feature means, ensuring initial centroids are more representative of the data distribution. Additionally, it focuses on identifying and weighting the most important features while creating composite features that amplify cluster separability. Experimental results demonstrate a 1.36986% improvement in clustering accuracy, an increase in silhouette scores, faster convergence, and improved stability across multiple trials compared to traditional K-means.

关键词： Accuracy Sensitivity Feature extraction Stability analysis Reliability Optimization Unsupervised learning Tuning Standards Convergence

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Assessing Students for Industry Readiness using Classification Methods 9

Assessing Students for Industry Readiness using Classificati...

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9th ACIS International Virtual Conference on Applied Computing and Information Technology, ACIT 2022

作者： Hossain Molla, Jakir Basak, Sandip Kumar Obaidullah, Sk Md Alam, Parveen Ahmed Goto, Takaaki Sen, Soumya Department of Computer Science and Engineering Aliah University India Department of Data Science Friedrich-Alexander-Universität Germany Department of Management & Business Administration Aliah University India Faculty of Information Sciences and Arts Toyo University Japan A.K.Choudhury School of Information Technology University of Calcutta India

ISBN: (纸本)9781450397605

Although Placement of students is an intrinsic requirement for the students worldwide majorly for professional as well as for higher degree courses. However, often the students are not aware about their skill levels that are considered as the industry readiness parameters. The Institutions often take care of the students to upgrade the skill level to meet the requirement of the Industry. In order to analyze the students based on the several parameters of industry readiness intelligent methods are required to assess them. In this research work, different classification methods are applied on the existing placement data to evaluate whether a student will get a job or not. Accuracies of these methods are compared using a real life data set. This analysis will help the Institutes to take the decision how long the training programs will continue. The result of the classification methods have been improved further using Random oversampling. © 2022 ACM.

关键词： Students

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Unveiling drug-induced side effects through network-based analysis: an update

Unveiling drug-induced side effects through network-based an...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alessio Funari Federica Conte Paola Paci Department of Translational and Precision Medicine Sapienza University of Rome Institute for Systems Analysis and Computer Science “Antonio Ruberti” National Research Council Rome Italy Institute for Systems Analysis and Computer Science “Antonio Ruberti” National Research Council Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this article, we propose an updated version of our previously developed model for predicting drug-side effect associations, applied to two case studies: long QT syndrome and asthma. The classifier accepts the name of a specific drug side effect as input and outputs a list of drugs potentially associated with this side effect. By simulating how drug effects propagate within the interactome using the Random Walk with Restart algorithm, the classifier identifies genes potentially associated with the onset of the side effect. Based on the rationale that the more a drug perturbs these genes, the more likely it is to cause the side effect, the model identifies drugs potentially linked to the onset of the side effect. Moreover, the model enables the categorization of drugs into chemical subclasses using the ClassyFire schema, facilitating the analysis of complex side effects, such as asthma, through more specific mechanisms. The results show that the model identifies both drugs known to be associated with certain side effects, as well as drugs not officially reported by the FDA, demonstrating its generalizability and practical relevance. This method is also adaptable for analyzing other side effects.

关键词： Drugs Analytical models Adaptation models Predictive models Classification algorithms Bioinformatics Chemicals Asthma

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Towards Speedy Permission-Based Debloating for Android Apps

Towards Speedy Permission-Based Debloating for Android Apps

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International Conference on Mobile Software engineering and Systems (MOBILESoft)

作者： Ferdian Thung Jiakun Liu Pattarakrit Rattanukul Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

ISBN: (数字)9798400705946

ISBN: (纸本)9798350363975

Android apps typically include many functionalities that not all users require. These result in software bloat that increases possible attack surface and app size. Common functionalities that users may not require are related to permissions that they intend to disallow in the first place. As these permissions are disallowed, their related code would never be executed and therefore can be safely removed. Existing work has proposed a solution to debloat Android apps according to the disallowed permissions. However, for large and complex applications, the debloating process could take hours, typically due the long time that may be needed to construct call graph for analysis. In this work, we propose MiniAppPERM, that speeds up the permission-based debloating by constructing a partial call graph instead of a complete call graph. Our preliminary experiments on a set of apps in Google Play show that MiniAppPerm can reduce the call graph construction time by up to $85.3 \%$. We also checked that the debloated apps can run without crashes. CCS CONCEPTS - Security and privacy $\rightarrow$ Software security engineering.

关键词： Privacy Codes Software computer crashes Internet Security Software engineering

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Hate Speech Detection Using the GPT-2 and Natural Language Processing

Hate Speech Detection Using the GPT-2 and Natural Language P...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Savya Sachi Ajay Kumar Singh Arpit Jain Suman Devi Yogesh Kumar Sharma Senthil Athithan Department of Information Technology Lalit Narayan Mishra College of Business Management Muzaffarpur Bihar India Allenhouse Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur A.P. India Department of Computer Science and Engineering Chaudhary Devi Lal University Sirsa Haryana India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India

Hate speech on social media has increased as people use it to communicate and express their thoughts. Automatic hate speech detection has been studied in several languages. This study investigates the conclusion of the performance evaluation of the machine learning model GPT-2 (Generative Pre-trained Transformation 2) for natural language processing. The proposed model is developed and evaluated using a hate speech dataset. We attained an accuracy of about 97.12 percent, higher than the existing models called biLSTM, CNN, CNN-LSTM, and BERT.

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SVM Classifier for Hygienic Food Handling with Cloud-Connected UV-C Sterilization Solutions

SVM Classifier for Hygienic Food Handling with Cloud-Connect...

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International Conference on Communications and Signal Processing

作者： N. Arunfred Chitra Sabapathy Ranganathan P.S. Kumaresh B. Maruthukannan D. Gobinath Bhuvaneswari Arunagiri Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu India Corporation Chandler Arizona USA Department of Electronics and Communication Engineering K.L.N.College of Engineering Pottapalayam Sivagangai Tamil Nadu India Department of Computer Science and Engineering NPR College of Engineering & Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Technology Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This research integrates a support vector machine (SVM) classifier with cloud-connected ultraviolet-C (UV-C) sterilization systems to improve food handling hygiene. To ensure food safety, this system uses machine learning to analyze ambient conditions, handling processes, and equipment cleanliness. The SVM classifier classifies hygiene levels based on input data for real-time monitoring and decision-making. Cloud-connected UV-C sterilization technologies automate and streamline food industry microorganism control. Remote monitoring and control via the cloud lets stakeholder’s access vital data and take immediate action. This increases food handling cleanliness, system speed, and responsiveness. The research examines the reduction of microbial contamination in various settings by using the SVM classifier and the cloud-connected UV-C sterilization solutions. Results show a considerable increase in hygiene level predictions and a significant decrease in the presence of bacteria in food processing. The SVM classifier and cloud-connected UV-C sterilization systems might improve food safety standards. Machine learning and cloud technologies provide a complete and adaptable solution to sanitary food handling issues, providing customers with safe and high-quality food items.

关键词： Support vector machines Visualization Machine learning Transforms Signal processing Safety Stakeholders

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Network Traffic Prediction using Deep Learning Models: Reducing Latency in High-Demand Scenarios

Network Traffic Prediction using Deep Learning Models: Reduc...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： B.J. Job Karuna Sagar Divya M V Namita Kale Neetu Anand Kumar Gaurav Shrividya Shashidhara Department of CSE Joginpally B.R Engineering College Hyderabad Telangana Department of Computer Science East West Institute of Technology Bangalore India Department of Information Technology MET'S Institute Of Engineering Nashik India Department of Computer Applications Maharaja Surajmal Institute Delhi India Department of CSE BMS Institute of Technology and Management Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The availability of the internet and enhanced applications, more specifically, the utilization of more devices in networks, and improved computational applications has created complexity in the control of network traffic. Network traffic forecasting is an essential component in low-latency networks, as well as in real-time applications that deserve excellent service quality and efficient resource allocation. This study therefore focuses on identifying whether deep learning models can further improve the accuracy in forecasting traffic flows in a network. Therefore, with the help of LSTM networks and Convolutional Neural Networks, the work will build temporal dependencies and spatial correlations of network traffic data. There are major and minor adaptations to some classic methodologies that conform the proposed framework; moreover, it is applied to two real datasets to highlight the capacity of the model in anticipating traffic congestion with a higher level of accuracy than the traditional methodologies. Evaluation of models in dynamic network conditions is done through common parameters such as prediction accuracy, lowered latency, and scalability. Saved models show that deep learning-based solutions reduce latency at high loads much more effectively, which will help network administrators in finding efficient solutions. This study demonstrate how deep learning can aid in the prediction of traffics, and the manner in which it is changing the efficiency and effectiveness of modern-day network systems. It makes the foundation for improved understanding of traffic patterns in order to apply it to optimize future generation of communication networks.

关键词： Deep learning Adaptation models Accuracy Telecommunication traffic Predictive models Throughput Convolutional neural networks Forecasting Long short term memory Load modeling

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SCAPS-1D Based Analysis of Novel Absorber ASrTSe Stacked CZTS Thin Film Solar Cell

SCAPS-1D Based Analysis of Novel Absorber ASrTSe Stacked CZT...

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engineering and Technology (PICET), Parul International Conference on

作者： Ashutosh Srivastava Gajendra Sharma Paramita Sarkar Payel Deb School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of ECE B.M.S. Institute of Technology and Management Bangalore India Mechanical Engineering Department PIET Parul University Gujarat India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Device performance improvement of Thin Film So- lar cells (TFSC) through stacking of various absorber layers has been of great importance these days. In this paper, Novel absorber material Ag2SrSnSe4 (ASrTSe) has been stacked with existing Cu2ZnSnSe4CZTSe as a p-type absorber layer in most studied TFSC and the device performance has have been studied. For simulations, Solar Cell Capacitance Simulator (SCAPS 1-D) has been used. A promising Power Conversion Efficiency (PCE) of 21.16 % has been obtained showing an improvement from 18.06 % PCE in basic ASrTSe based solar cell.

关键词： Performance evaluation Photovoltaic cells Stacking Capacitance Power conversion

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An Examination of The PSO Algorithm’s Sensitivity For Estimating Solar Cell Parameters

An Examination of The PSO Algorithm’s Sensitivity For Estim...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Bhanu Sharma Abhishek Sharma Wei Hong Lim Deepak Kaushal Department of Management Studies Graphic Era (Deemed to be) University Dehradun India Department of Computer Science Engineering Graphic Era (Deemed to be) University Dehradun India Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur Malaysia

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Extracting parameters from photovoltaic cells poses a complex optimization challenge due to its inherent nonlinearity. This study investigates the sensitivity of the Particle Swarm Optimization (PSO) algorithm in estimating solar cell parameters. The evaluation assesses PSO’s exploitation and exploration capabilities, factoring in measurement errors. Performance is compared across six measurement error scenarios using metrics like convergence rate along with root mean square error (RMSE). It is very evident from the results that for case 2, the PSO algorithm achieves the lowest RMSE value of 0.02980, while it produces nearly identical RMSE values of 0.05040 and 0.04915 for cases 1 and 6. Simulation results clearly indicate that the PSO algorithm effectively handles measurement inaccuracies of 1% and 3%.

关键词： Measurement errors Renewable energy sources Accuracy Sensitivity analysis Photovoltaic cells Simulation Solar energy Root mean square Parameter extraction Genetic algorithms

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