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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Anshul Kumar Sandeep Mathur Adarsh Malviya Department of Computer Science in School of Science Noida International University Noida Uttar Pradesh India Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science Dronacharya Group of Institutions Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Efficient load balancing is vital in cloud computing to optimize resource use, enhance performance, and maintain application availability. Traditional methods often underutilize the scalability of IaaS models. Hence, a dynamic load balancing system is crucial for real-time traffic distribution among IaaS servers based on demand and resource availability. High traffic challenges in web hosting can degrade load times. Dynamic server load balancing is pivotal here. Our approach integrates ASA genetic algorithms with process scheduling. It queues and assigns requests randomly to virtual servers, verifying connections. A genetic algorithm optimizes schedules via selection, crossover, and mutation, iterating for the best schedule. This strategy markedly reduces load times and maximizes system performance.

关键词： Cloud computing Schedules Processor scheduling System performance Scalability Heuristic algorithms Load management Servers Genetic algorithms Load modeling

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An Intelligent Learning Approach to Case Analysis for Predicting Legal Outcomes Using Random Forest and K-Nearest Neighbors

An Intelligent Learning Approach to Case Analysis for Predic...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Puneet Bafna Hardeo Kumar Thakur Jagendra Singh Jannam Sadana M. Muhsen Hassan M. Sree Vani Amity Law School Amity University Jaipur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Information Technology CVR College of Engineering Hyderabad India The Islamic University Najaf Iraq Department of CSE BVRIT Hyderabad College of Engineering for Women Hyderabad India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Machine learning is an increasingly popular area of research that holds great potential for enhancing the effectiveness and precision of legal analysis and decision-making. More specifically, machine learning can be used to predict the most probable outcomes of a legal case, and the purpose of this project is to utilize machine learning with case law data from the Chennai District Court to predict the outcomes of cases that involve criminal offences and the charge of murder. In other words, the goal of this project is to predict whether a case will result in the conviction of a victim from a list of case factors. For this research, several approaches to machine learning, including K-Nearest Neighbors, Support Vector Machines, Naive Bayes, Random Forests, and Decision Trees, are implemented to identify the best solutions for predicting the case outcomes. The case factors used in the research provided in the court judgments and applied various preprocessing tools to eliminate random factors that could lead to unreliable predictions. Finally, the various performance scores of the model are assessed, which includes the accuracy of each trained model in terms of precision, recall, Fl-score, and the Area Under the ROC Curve. As a result, about 95% of models displayed high accuracy rates, while KNN performed the best - 96.76% precision and 0.985 AUC-ROC, with SVM at 94.5% and 0.975 extremely close to perfection. In addition, NB, RF, and DT also showed good levels of accuracy at 92.1%, 88.9%, and 85.6%, respectively. This research confirms that machine learning can be highly effective in predicting legal outcomes and increasing legal practice accuracy.

关键词： Support vector machines Radio frequency Measurement Analytical models Accuracy Law Decision making Nearest neighbor methods Predictive models Random forests

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Routing Protocols in Wireless Sensor Networks: A Comprehensive Review and Future Perspectives

Routing Protocols in Wireless Sensor Networks: A Comprehensi...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Puneet Kaur Kiranbir Kaur Deepak Kumar Verma Kuldeep Singh Keshav Kaushik Vineeta Singh Department of Computer Engineering & Technology Guru Nanak Dev University Amritsar Punjab India Department of Computer Science and Engineering School of Engineering and Technology (Formerly known as UIET Kanpur) Chhatrapati Shahu Ji Maharaj University Kalyanpur Uttar Pradesh India Department of Electronics Technology Guru Nanak Dev University Amritsar Punjab India Amity School of Engineering and Technology Amity University Mohali Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Wireless Sensor Networks (WSNs) have significantly transformed diverse domains such as industrial automation, healthcare, disaster management, and environmental monitoring. The complex interaction of routing protocols, essential for enabling smooth data transport despite the specific difficulties in WSN environments, is the core of their success. The objective of this study is to get a accurate understanding of routing algorithms specifically projected for wireless sensor networks. Expanding upon the essential features and significant disputes faced in WSN systems, admitting limited energy resources, bandwidth constraints, and node processing capabilities, the discussion accentuates the significance of effective routing protocols. Furthermore, it thoroughly analyzes many forms of routing protocols, including basic hierarchical and flat schemes, as well as advanced location-based and data-centric protocols. The protocol category is thoroughly examined, uncovering its unique benefits, limitations, and suitability for various WSN applications. Furthermore, the review reveals the latest developments and upcoming trends in WSN routing protocols, brooding areas such as energy efficiency, dependability, scalability, and adaptive resilience to changing network considerations. In conclusion, the evaluation furnishes a forward-looking view by outlining potential research areas and possible pathways for meliorating the designs of WSN routing protocols. The main goal is to heighten the efficacy and practicality of WSNs. In this work, we examined a number of routing algorithms according to important criteria including mobility, scalability, energy efficiency, and Quality of Service (QoS). We looked at how each method handles node mobility, performs as the network expands, guarantees dependable data transmission, and optimizes energy use. We also looked at ways for choosing the cluster head (CH), energy-saving data aggregation strategies, redundancy-reduction mechanisms, and the

关键词： Wireless sensor networks Reviews Scalability Clustering algorithms Quality of service Medical services Routing Routing protocols Energy efficiency Resilience

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Addressing the Unique Security and Privacy Challenges in Cellular Network Environments

Addressing the Unique Security and Privacy Challenges in Cel...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： N Rajashekar K Aravinda N Sirisha Ajay Rana Taqi Mohammed Khattab Al-Rubaye Atul Singla Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Amity School of Engineering and Technology Amity University Greater Noida India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq Lovely Professional University Phagwara India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

This study offers a full solution to cellular network privacy and security challenges. The recommended remedy tackles numerous major issues with enhanced login protocols, sophisticated encryption, machine learning algorithms to discover threats, and privacy-protecting technology. The study's 98% success rate emphasizes the importance of solid identification for preventing unauthorized entry. Communication is safe and data is secure with modern encryption technologies like 256-bit AES. Machine learning helps the system identify and address security threats. This yields 95% danger detection. ‘Very high’ privacy is offered by privacy-protecting technologies and secure multi-party computing components that prevent data intrusions. Controlling network latency allows cellular networks to connect seamlessly and in real time, improving the user experience. The recommended solution balances computing speed and security without delaying the system. This research provides a thorough and practical method for making cellular network settings safer and more private. The diverse strategy of the offered solution makes it suitable for many purposes and provides more security and robustness in the ever-changing world of cellular networks.

关键词： Cellular networks Protocols Machine learning algorithms Machine learning User experience Threat assessment Real-time systems

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Behavioral Biometrics Authentication Systems: Leveraging Machine Learning for Enhanced Cybersecurity

Behavioral Biometrics Authentication Systems: Leveraging Mac...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Jhansi Bharathi Madavarapu Mudit Mittal Shailaja Salagrama Myasar Mundher Adnan Aryan Rana Kanchan Yadav Department of Information Technology University of Cumberlands Williamsburg KY USA Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Amity School of Engineering & Technology Amity University India Department of Electrical Engineering GLA University Mathura

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

This study focuses on behavioral biometrics and machine learning procedure that is employed vigorous in the verification frameworks to improve cybersecurity. Elaborating interesting designs in client behaviour, such as keystroke flow and mouse developments along with advanced machine learning algorithms we propose to generate verification components capable of accurately identifying a person's identity while adapting themselves all the fast live changes associated either. The experiments carried out on real-life datasets demonstrate LSTM models pervasive implementation in contrast to traditional machine learning algorithms (SVM, KNN and Random Forest). Thus, LSTM achieved an accuracy of 95.6%, outperforming SVM (88.7%), KNN (84.5%) and Random Forest (923). Additionally, LSTM proved great flexibility and competitive performance despite longer training times. The abovementioned results have demonstrated that LSTM is effective in learning the complicated real world conditions in sequential data and could be used for improvement of behavioral biometric recognition systems.

关键词： Support vector machines Training Surveys Adaptation models Machine learning algorithms Biometrics (access control) Nearest neighbor methods

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Optimized Classification of Firewall Log Data using Heterogeneous Ensemble Techniques

Optimized Classification of Firewall Log Data using Heteroge...

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2021 International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2021

作者： Sharma, Deepanshu Wason, Vansh Johri, Prashant GGS Indraprastlia University Amity School of Engineering Technology Delhi India Galgotias University School of Computer Science Engineering Uttar Pradesh India

ISBN: (纸本)9781728177410

In the ever-increasing time of network data exchange, the need for a smarter and automatic filter for web data packets is a priority. Hence, in this research study, an optimized solution for classification of firewall data packets is proposed using machine learning. The packets are filtered using four firewall action labels of accept, drop, reject, TCP reset. The study is done on 65532 instances of log files by creating advanced ensemble models for classification of log data. Heterogenous voting ensemble and stacking ensemble models were generated using five well known machine learning classification algorithms namely, K-nearest neighbor, logistic regression, support vector machine, decision tree classifier, and stochastic gradient descent classifier. The stacking ensemble involved random forest algorithm as its meta classifier. The models were compared using their performance metrics to obtain a best solution for data packets classification. It was found that the heterogeneous stacking ensemble model thus created, had a Precision value of 91% and the accuracy score of 99.8% which outperformed other considered models for optimized classification of firewall data. © 2021 IEEE.

关键词： Classification (of information)

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Advancing Residential Energy Management: Integrating Renewable Resources and Vehicle-to-Grid Systems for Optimized Electrical Grid Performance

Advancing Residential Energy Management: Integrating Renewab...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Rakesh Chandrashekar D Shobha Rani Ginni Nijhawan A Balaram Prateek Chaturvedi B Sankara Babu Department of Mechanical Engineering New Horizon College of Engineering Bangalore Karnataka India Department of Electrical and Electronics Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Lovely Professional University Phagwara Punjab India Department of Computer Science & Engineering MLR Institute of Technology Hyderabad Telangana India Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering GRIET Bachupally Hyderabad India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The term “smart grid” describes the future generation of electrical power networks, which are very complicated systems. It must take into account all aspects of the electrical system, increasing its intelligence and adaptability. In response to shifts in the electrical market, this idea is put out with the intention of controlling the rising demand while guaranteeing higher service quality and enhanced safety. This research presents an urban scenario that incorporates dispersed energy sources into a smart grid. The research incorporates renewable energy sources like solar panels and tiny windmills into the system, as well as battery storage and grid-connected vehicles that can power the electrical grid by drawing electricity from the storage device. So, to maximize the energy production and absorption systems as well as the recharging and discharging time for a home user’s electric car, a mixed integer linear programming is proposed. Additionally, a number of case studies are provided by changing important variables using the Taguchi technique of design of experiments in order to identify the best solution and demonstrate how each influences the system’s complexity.

关键词： Vehicle-to-grid Renewable energy sources Electricity Production Safety Mixed integer linear programming Solar panels Informatics Energy management Optimization

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Evaluating the Ecological Footprint: Analyzing the Environmental Impact of Battery Energy Storage Systems within Electric Grids

Evaluating the Ecological Footprint: Analyzing the Environme...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： S Vikram Singh Shaik Saddam Hussain Atul Singla A Balaram Y Krishna Bargavi R Gowtham Raj Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science & Engg VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Lovely Professional University Phagwara India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of CSE GRIET Bachupally Hyderabad Telangana India Department of Mechanical Engineering New Horizon College of Engineering Bengaluru

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This research paper shall cover a detailed assessment of the overall ecological impact of BESS within electric grids, which becomes a critical component if grid reliability is to be improved, renewable energy integration advanced, and the pledging of attainment of sustainable energy served. In this interdisciplinary study, life cycle analysis and environmental impact assessment in regard to conventional energy storage solutions will be integrated with comparative methods. The BESS focus is on quantifying emissions, resource depletion, and ecological disturbance, respectively, at various stages of manufacturing, operation, and end-of-life. Further, design mitigation strategies for reducing negative environmental impacts through the discussion on the role of recycling and second-life uses for used batteries. Results show that while BESS offers huge benefits in mitigating GHG by allowing greater integration of renewable energy sources, they have associated environmental-related problems with resource extraction and end-of-life wastes. Broad environmental legislation in new technologies is called for to be put in place for the improvement of the sustainability profile of BESS. The present work looks, with a balanced view, at the environmental benefits and global sustainability challenges connected with the deployment of BESS in electric grids.

关键词： Renewable energy sources Prevention and mitigation Production Raw materials Software Batteries Recycling Manufacturing Reliability Sustainable development

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APE-GAN++: An Improved APE-GAN to Eliminate Adversarial Perturbations

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第3期48卷 1-18页

作者： Yang, Rui Chen, Xiu-Qing Cao, Tian-Jie Master's student of the School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu China Lecturer of the School of Medicine Information and Engineering Xuzhou Medical University XuzhouJiangsu China Professor of the School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu China

Deep neural networks (DNNs) have been deployed successfully in various scenarios, but numerous studies have shown that deep neural networks are vulnerable to the attack of adversarial examples. In order to protect deep neural networks against adversarial examples, a lot of countermeasures have been developed. The APE-GAN is one of these proposed countermeasures, which employ a generative adversarial network (GAN) to eliminate adversarial perturbations. Although it performs more excellently than other countermeasures, it still has some shortcomings. First, its training process is precarious and has a vanishing gradient problem. Second, its performance can be improved further. In this paper, we propose the APE-GAN++, which is an enhanced APE-GAN, to overcome its disadvantages. First, the proposed APE-GAN++ utilizes the WGAN-GP loss to make the training process stable. Then, it uses a newly added third-party classification loss to enhance the capacity of the generator to eliminate adversarial perturbations. Experiments are conducted on the MNIST and CIFAR-10 datasets to verify the proposed APE-GAN++'s performance. Experimental results show that the proposed APE-GAN++ has a stable training process and solves the vanishing gradient problem. Besides, it can also achieve a more excellent performance than other countermeasures when defending against adversarial examples. Experimental code is available at https://***/Afreadyang/APE-GAN-Plus-Plus. © 2021. All Rights Reserved.

关键词： Generative adversarial networks

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Advanced Deep Learning Approaches for Real-Time Anomaly Detection in IoT Environments

Advanced Deep Learning Approaches for Real-Time Anomaly Dete...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Himanshu Rai Goyal Saif O. Husain Krishna Kant Dixit Nandini Shirish Boob B. Ravali Reddy Jaysheel Kumar Smita Sharma Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Electrical Engineering GLA University Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana New Horizon College of Engineering Bangalore Amity School of Engineering & Technology Amity University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The proliferation of Internet of Things (IoT) systems has led to the generation of vast amounts of data, increasing the need for effective anomaly detection mechanisms to ensure system reliability and security. Traditional methods often fall short due to the high dimensionality and dynamic nature of IoT data. This paper presents a deep learning-based approach for real-time anomaly detection in IoT systems, leveraging the power of neural networks to identify patterns and anomalies within complex datasets. By employing deep learning algorithms such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, this framework can efficiently detect abnormal behaviors in real-time, offering a significant improvement over conventional methods. The proposed system is trained on labeled datasets collected from IoT devices, allowing it to distinguish between normal and anomalous activity. Furthermore, the model adapts to new data, enhancing its detection capabilities over time. Experimental results demonstrate that this approach achieves high accuracy and low false-positive rates, making it suitable for deployment in real-world IoT environments. The framework’s scalability and adaptability highlight its potential for enhancing the security and reliability of IoT networks, particularly in critical applications such as smart cities, healthcare, and industrial IoT.

关键词： Deep learning Adaptation models Computational modeling Real-time systems Internet of Things Security Reliability Convolutional neural networks Anomaly detection Long short term memory

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