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1st International Conference on Mathematical Modeling and Computational science, ICMMCS 2020

作者： Hema Shankari, K. Mathivilasini, S. Arasu, Dilli Suseendran, G. Department of Computer Application Women’s Christian College Chennai India Department of Computer Science Ethiraj College for Women Chennai India Department of Computer Application DRBCCC Hindu College Chennai India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (纸本)9789813343887

The paper addresses research in the area of software testing, which has a critical part of performing in software quality assurance. It becomes very inefficient for a tester to re-execute massive number of test cases again and again for small variations. The proposed artificial neural network with the genetic algorithm-based case suite prioritization (ANNGCSP) achieves better performance compared. New proposed algorithm combined the rational functional tester (RFT) tool, and new proposed algorithm combined open source tool in terms of APFD metric. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Enhancing Academic Performance Predictions Using Deep Belief Networks: A Novel Approach for Educational Data Analysis

Enhancing Academic Performance Predictions Using Deep Belief...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： Irwa Zahoor M S Radha Manga Mani Saravanan B Ajay Kumar Chaitali Bhattacharya Rohit Kar Department of Computer Science and Engineering Islamic University of Science and Technology Awantipora Jammu and Kashmir Department of Information Technology SRKR Engineering College Bhimavaram Andhra Pradesh India Department of CSE Sri Eshwar College of Engineering Coimbatore Tamilnadu India Tecnia Institute of Advanced Studies Rohini New Delhi India Tecnia Institute of Advanced Studies CDL New Delhi India Department of Management St. Claret College Autonomous Bengaluru Karnataka India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The capacity to predict students' performance is vital in matters related to deep learning and its relationship to educational data and, more generally, to higher education. Precise performance prediction facilitates both the selection of appropriate courses and the creation of efficient long-term study plans. It helps with predicting how well students will do in class, and it lets administration and educators keep tabs on students to support the need for training program integration. Preprocessing, feature selection, and model training are all part of the suggested method. Transforming raw data into a usable format is a crucial step in machine learning. It helps fix problems in real-world datasets and produce better outcomes. Principle Component Analysis (PCA) is frequently employed in feature selection. All through the training, DGNN was always in use. This novel approach outperforms GNN and RNN with an average accuracy of 94.27%.

关键词： Training Deep learning computers Renewable energy sources Accuracy Data analysis Computational modeling Feature extraction Solids Principal component analysis

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Multi-Objective High-Fidelity Optimization Using NSGA-III and MO-RPSOLC

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computer Modeling in Engineering & sciences 2021年第11期129卷 443-464页

作者： N.Ganesh Uvaraja Ragavendran Kanak Kalita Paras Jain Xiao-Zhi Gao Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Electronics and Telecommunication Engineering MPSTME SVKM’S Narsee Monjee Institute of Management StudiesShirpur425405India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India School of Computing Science and Engineering VIT Bhopal UniversitySehore466114India School of Computing University of Eastern FinlandKuopio70211Finland

Optimizing the performance of composite structures is a real-world application with significant *** this paper,a high-fidelity finite element method(FEM)is combined with the iterative improvement capability of metaheuristic optimization algorithms to obtain optimized composite *** FEM module comprises of ninenode isoparametric plate bending element in conjunction with the first-order shear deformation theory(FSDT).A recently proposed memetic version of particle swarm optimization called RPSOLC is modified in the current research to carry out multi-objective Pareto *** performance of the MO-RPSOLC is found to be comparable with the *** work successfully highlights the use of FEM-MO-RPSOLC in obtaining highfidelity Pareto solutions considering simultaneous maximization of the fundamental frequency and frequency separation in laminated composites by optimizing the stacking sequence.

关键词： Composites finite element optimization Pareto swarm intelligence

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Low-cost Modular Smart Home Controlling System for Minimising Energy Consumption: A Conceptual Study 1

Low-cost Modular Smart Home Controlling System for Minimisin...

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1st International Visualization, Informatics and Technology Conference, IVIT 2022

作者： Putra Ahmad Baidowi, Zaid Mujaiyid Danial Hakim Nazrin Izuan Gerard, Muhammad Ramly, Ruwaida Mohd Isa, Mohd Rizal Yusof Darus, Mohamad Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Centre of Foundation Studies Selangor Dengkil43800 Malaysia Malaysian Institute of Information Technology Universiti Kuala Lumpur Malaysia National Defence University of Malaysia Computer Science Department Kuala Lumpur Malaysia College of Computing Informatics and Media Universiti Teknologi MARA School of Computing Sciences Malaysia

ISBN: (纸本)9781665486484

Internet of Things (IoT) has become one of the popular topics in Information and Communication Technology (ICT) recently. IoT has been widely used in many fields, and one of the most topics that had gained significant interest of many researchers is minimising energy consumption. IoT which is implemented in a wireless, connects multiple devices via the Internet. It allows data to be gathered and transferred without human intervention. In this paper, a new solution is to propose an automatic control of several electrical appliances such as air-conditioners, lights, and fans. These three electrical appliances contribute to the highest energy consumption. This paper aims to contribute to the solution of minimising energy consumption by deploying a low-cost modular system to be installed into readily houses. Thus, it can adapt any old infrastructure to an efficient home. Minimising power consumption has been studied in a smart home but less work deployed Arduino Nano 33 IoT as compared to the existing available Arduino system. Finally, this work is hoped lead to related studies in minimising energy consumption. © 2022 IEEE.

关键词： Energy utilization

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Secure Multi-Party Computation for Collaborative Data Analysis in Network Security

Secure Multi-Party Computation for Collaborative Data Analys...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), International Conference

作者： Tamilselvi P V. Lathika S. Jayachitra S Arunkumar M. Balasubramani V Kalaichelvi Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of CSE Sona College of Technology (Autonomous) Salem India Department of ECE PSNA College of Engineering and Technology Dindigul India Department of ECE Coimbatore Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering VSB Engineering College Karur India Department of CSE Srinivasa Ramanujan Centre SASTRA Deemed to be University Kumbakonam India

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. advanced threat analysis may also benefit.

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Trajectory Design for 3D UAV Localization in UAV Based Networks

Trajectory Design for 3D UAV Localization in UAV Based Netwo...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.

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Machine Learning and IoT based Performance Improvement of Energy Efficiency in Smart Buildings 2

Machine Learning and IoT based Performance Improvement of En...

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2nd International Conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Jagadeesan, Sowmya Ravi, C.N. Sujatha, M. Southry, S. Sree Sundararajan, J. Reddy, Ch. Venkata Krishna Vels Institute of Science Technology and Advanced Studies Departnent of Bca and It INurture Education Solutions Tamil Nadu Chennai India Cmr Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Easwari Engineering College Ramapuram Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Npr College of Enggineering and Technology Department of Electronics and Communication Tamil Nadu Dindugul India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India

ISBN: (纸本)9781665491990

The energy plays a prominent role in the development of a country's economic conditions. This helps to increase the overall performance index of a country. Thus a continuous energy system is needed to develop a country's performance index. Thus to have an advancement in the energy management system, an improved system called smart grid is implemented. This helps in efficient usage of energy management through meeting the demand side managing in the smart buildings. This assistances to decrease the additional procedure of energy consumption through optimization techniques such as genetic algorithm used in smart buildings. This is done with the machine learning through optimization algorithm. This helps to proper scheduling of appliances. This is done through the priority scheduling. The overall performance is done through the internet of things with sensors. The sensors helps in the process of sensing the external physical parameters such as temperature, humidity, moisture, heat and light. This helps to enhance the energy efficiency in the smart buildings through the machine learning and IoT. © 2023 IEEE.

关键词： Internet of things

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A Detailed Investigation into the Brain Cancer Detection Capabilities of CNN

A Detailed Investigation into the Brain Cancer Detection Cap...

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International Health Informatics Conference, IHIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819771899

Brain tumour detection is an accepted testing task in the beginning phases of life. Yet, presently it has progressed with different AI calculations. Along these lines, to identify the brain tumour of a patient we consider the data of patients like MRI pictures of a patient’s brain. Here our concern is to spot if the tumour is available in persisting’s brain. It’s crucial to identify the tumour at the beginning level for a sound lifetime of a patient. During this task, we gauge the seriousness utilizing Convolutional Neural Network calculationwhich gives us precise outcomes. Tumour division is a pivotal and burdensome assignment inside the territory of clinical picture preparation as a human-helped manual characterization may bring about erroneous expectations and findings. We suggested using convolutional neural organization to distinguish tumours from 2D appealing reverberation brain images (MRI). The exploratory analysis was performed on a dataset that included tumours of various sizes, regions, forms, and picture forces. We used convolutional neural networks, which are implemented using Keras and Tensorflow because they respect a much better display than standard ones. CNN achieved an accuracy of 83.54% in our research. The recently developed CNN design may be used as a productive decisionaid device for radiologists in clinical diagnostics because of its high speculation capacity and quickness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neuroimaging

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Enabling Support for TLS and TLS Fingerprinting in Network Stack Tester

Enabling Support for TLS and TLS Fingerprinting in Network S...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Nitin Dabi Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Transport Layer Security (TLS) is the standard protocol widely used for securing client-server communications over the Internet. It ensures the confidentiality and integrity of data transmitted between systems. With the rising deployment of TLS 1.3, which is the latest version of the protocol, stronger encryption and improved security features are introduced. The adoption of TLS 1.3 brings about the need for techniques such as TLS fingerprinting. TLS fingerprinting involves analyzing the unique characteristics of TLS handshakes to identify and classify different implementations or configurations of TLS. However, researchers currently lack a dedicated platform to test TLS and TLS fingerprinting techniques in a controlled environment. To address this gap, we aim to enhance NeST (Network Stack Tester) by adding support for TLS and TLS fingerprinting. NeST is a Python tool for network emulation that internally uses Linux network namespaces and Linux utilities to create virtual testbeds, perform experiments using various network protocols and collect statistics. By integrating TLS and TLS fingerprinting into NeST, we provide an easy-to-use tool that enables researchers to experiment with TLS, analyze TLS traffic, and evaluate the effectiveness of TLS fingerprinting techniques.

关键词： Protocols Linux Emulation Fingerprint recognition Communications technology Internet Encryption

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Relativistic Digital Twin: Bringing the IoT to the Future

arXiv

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arXiv 2023年

作者： Sciullo, Luca De Marchi, Alberto Trotta, Angelo Montori, Federico Bononi, Luciano Felice, Marco Di Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems University of Bologna Italy Institute of Applied Mathematics and Scientific Computing Department of Aerospace Engineering University of the Bundeswehr Munich Germany

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario. © 2023, CC BY.

关键词： Internet of things

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