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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Dhote, Vijay Sadim, Mohd Tanna, Paresh Tiwari, Annapurna Nand Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Computer Engineering Rk University India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

The cryptographic techniques that underpin current network security standards run the risk of becoming outdated due to the advancement of quantum computing. Researchers and industry professionals are working to create novel strategies for protecting the privacy of confidential data in the post-quantum age. In this study, we explore the possibility that a mix of quantum-resistant network security protocols and machine learning techniques might serve as a potent countermeasure against the growing threat posed by quantum computers. This is the latest addition to the way we've been talking about it. By employing machine learning, we can enhance the effectiveness and adaptability of encryption systems, strengthening network security, therefore. Furthermore, we discuss the potential contributions of several machine learning methods, including neural network-based approaches, pattern recognition, and anomaly detection, to the field of quantum-resistant security protocols. Considering the emergence of quantum computing, combining machine learning with quantum-resistant encryption has become a crucial tactic for protecting sensitive data. This cooperative endeavor, which will be advantageous to both sides, clarifies a crucial tactic. Our results demonstrate that machine learning offers the potential to be a powerful partner in the never-ending fight to protect digital assets. The understanding that our results provide is crucial for building safe networks in the era of quantum computing. © 2023 IEEE.

关键词： Intrusion detection

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Review Based Analysis with Applications of Blockchain in Food Supply Chain management 10

Review Based Analysis with Applications of Blockchain in Foo...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sharma, Deepti Singh, Gurpreet Kaur, Amanpreet Giani Zail Singh Campus College of Engineering and Tecnology Mrsptu Department of Computer Science and Engineering Punjab Bathinda India Punjab Institute of Technology Rajpura Mrsptu Department of Computer Science and Engineering Punjab Bathinda India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9789380544472

The main aim of food supply chains is to ensure the availability of safe and good quality food the consumers across the world. With the advancement in technology the food supply chains are experiencing significant changes. Block chain technology is attracting interest as its provides benefits of transparency, traceability, smart contracts, decentralization and better sustainability. The ubiquitous technology of block chain has the potential to improve traditional food supply system. It can guide the current food processing towards better food sustainability and efficiency. This paper presents a literature review on the use of block chain technology in food supply systems. We discuss the benefits of blockchain technology. We also discuss the challenges faced by the users of block chain-based food supply systems. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Smart contract

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Advanced Malware Detection Methods for Polymorphic Virus Identification 5

Advanced Malware Detection Methods for Polymorphic Virus Ide...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Anil, D. Shreeshayana, R. Kiran, B. Preethi Uvce Bangalore University Department of Computer Science and Engineering Bengaluru India Atme College of Engineering Department of Electrical and Electronics Engineering Mysuru India Pes University Department of Computer Science and Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

Polymorphic viruses pose a significant challenge to traditional malware detection methods due to their ability to modify their code structure with each infection, effectively evading signature-based detection. This paper explores advanced malware detection methods tailored for identifying polymorphic viruses, focusing on the integration of machine learning, deep learning and hybrid approaches. Through a comprehensive analysis of static and dynamic features extracted from executables and URLs, this study employs classifiers such as Random Forests and Support Vector Machines (SVMs) to enhance detection accuracy. The SVM classifier demonstrated superior performance, achieving a detection accuracy of 96.23% compared to the 93.67% accuracy of the Random Forest classifier. This research highlights the potential of combining static and dynamic analysis with machine learning techniques to address the limitations of traditional methods, offering a robust framework for detecting and mitigating polymorphic and zero-day threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

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Complex and Intelligent Systems 2024年第5期10卷 7311-7331页

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wer-Jer Yahya, Abid Badruddin, Irfan Anjum Kamangar, Sarfaraz Hussien, Mohamed Artificial Intelligence Research Institute China University of Mining and Technology Department of Automation University of Science and Technology of China Department of Marine Engineering of National Taiwan Ocean University (NTOU) Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and Technology Department of Mechanical Engineering College of Engineering King Khalid University Department of Chemistry Faculty of Science King Khalid University

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems. © The Author(s) 2024.

关键词： Adaptive observer control Feedback control design Indirect method Lyapunov–Krasovskii candidate T–S fuzzy models

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Sensor Enabled Vehicle Parking System using Blynk IoT Application 5

Sensor Enabled Vehicle Parking System using Blynk IoT Applic...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Sekar, G. Benson Mansingh, P.M. Prasad, J. Britto Manoj, A. Vsb College of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore India Vignan's Foundation for Science Technology and Research Department of Advanced Computer science and Engineering Guntur India Kpr Institute of Engineering and technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331504403

In the current scenario, finding the parking in full slot area is very difficult and also there is no online platform to check those parking slot availability details. The proposed system provides a parking slot availability information over phone and reducing a waiting time to check the availability. Using the 'Smart parking system' the parking slot availability can be accessed easily over the internet. This system can completely automate the entry and exit of car and also payment process. The Node MCU ESP8266 is connected with IR sensors. The IR sensors will show the availability of the parking slot. The entry and exit gates are connected with servo motors. RFID scanner used to show the information of the user while entering. Online service software was used (Blynk app) it shows the slot through your mobile world. © 2024 IEEE.

关键词： Sensor nodes

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Overview of Parallel Computing for Meta-Heuristic Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 656-681页

作者： Sun, Ying Chu, Shu-Chuan Hu, Pei Watada, Junzo Si, Mingchao Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology No.579 Qianwan’gang Road Shandong Qingdao266590 China Graduate School of Information Production and Systems Waseda University Kitakyushu808-0135 Japan College of Computer Science and Engineering Shandong University of Science and Technology China Chaoyang University of Technology Taiwan Taiwan Fujian University of Technology China

The meta-heuristic algorithm is used in the research of various complex problems. Due to the limitations of the original meta-heuristic algorithm, many improved meta-heuristic algorithms have been proposed, such as compact, adaptive, multi-objective and parallel schemes. Among them, the parallel strategy may get a significant improvement for related applications. This paper mainly studies the application of parallel computing in meta-heuristic algorithms. There are two main types of parallelism: one is absolute parallelism, using multiple processors which can solve optimization problems with high computational costs and improve execution efficiency. The other is virtual parallelism (multi-grouping), which decomposes the population into multiple sub-populations, and each sub-population communicates between species to generate better solutions. In addition, the combination of parallel computing and meta-heuristic algorithms can solve a wide variety of application problems: path planning, engineering design, large-scale optimization, image segmentation, neural networks and prediction problems, etc. This paper presents a comprehensive study and systematic survey of parallel meta-heuristics. © 2022, Journal of Network Intelligence. All rights reserved.

关键词： Motion planning

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An ideal deeply contextual prediction model for an effective prediction and mitigation of cyberbullying in social networks

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International Journal of Information technology (Singapore) 2025年第3期17卷 1561-1579页

作者： Prathaban, Banu Priya Vaishnavi, T. Ramesh, Addanki Purna Sujaritha, M. Rama, R. Senthil Gracewell, J. Jeffin Department of Networking and Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai 603203 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Tamil Nadu Chennai 600062 India Department of Electronics and Communication Engineering DNR College of Engineering and Technology West Godavari Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India Department of Electrical and Electronics Engineering DMI College of Engineering Tamil Nadu 600123 Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai 602105 India

Social media have evoked a host of problems, especially Twitter and Instagram, considering misconceived notions of free speech. Among the concerns that have been seen as crucial is the impact of cyberbullying;it is a global issue affecting an individual and society in general. Though various methods have been suggested for deterring cyberbullying, most of these depend on victim interaction and are hence ineffective in the task of independently identifying and preventing cyberbullying. Within this context, the research introduces a new, innovative framework that is ideal and deeply contextual in terms of prediction, thus creating an approach to detecting cyberbullying on social networks without directly engaging with any victim. Consequently, the key contribution of this work is developing the Embedded Residual Recurrent Convolution Network, a hybrid deep learning architecture for the detection of cyberbullying data with more precision and accuracy. Further, this is optimized by a Tenacious Projectile Optimizer for minimizing computational time and improving classifier performance. In this regard, the novelty of the proposed I-DCP framework is established in its superior performances on different social media datasets, with an average accuracy of over 99% and an execution time as low as 13 s, hence highly efficient for real-time detection of cyberbullying. These represent important advances compared to previous methodologies and constitute a strong and scalable response in the fight against cyberbullying. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And classification Artificial intelligence (AI) Cyberbullying detection Deep learning Instagram Optimization Social network Twitter

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Dual-Buffer-Based Optical Datacenter Switch Design

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Journal of Optical Communications 2023年第2期44卷 155-162页

作者： Bhattacharya, Pronaya Tiwari, Amod Kumar Singh, Arunendra Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh Lucknow226031 India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad382481 India Department of Computer Science and Engineering Rajkiya Engineering College Uttar Pradesh Sonbhadra231216 India Department of Information Technology Pranveer Singh Institute of Technology Uttar Pradesh Kanpur209305 India

The unbounded growth of Cloud-based services and Internet-of-Things (IoT) using Software Defined Networking (SDN) has resulted in bandwidth requirements that have grown enormously over the years. To meet this exponential growth that is currently outpacing Moore's Law, we need Optical Interconnection Networks (OIN) that can satisfy the user's requirements. In a similar direction Arrayed Waveguide Gratings (AWG)-based optical interconnects have been heavily investigated in past and many switch designs have emerged over the period of time. This paper proposes a dual-buffer-based optical switch design where the primary buffer stores the contending packets, while the secondary input buffer stores the contending packets which are blocked by AWG for one slot duration only. Moreover, if a blocked packet is generated on the same node then All-Optical Negative Acknowledgement (AO-NACK) will be used for blocked packets. This paper investigates the various issues related to storage of blocked packets, the analysis is done both at physical and network layers in terms of bit error rate (BER) and packet loss probability, respectively. It is found that with proposed switch design BER is 10 times lower than the earlier design, and the number of re-transmitted packets at the load of 0.8, reduces to 26 from 32 as in earlier switch design. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Bit error rate

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Artificial Intelligence and Deep Learning-based Model for Indoor Environment: with Virtual Reference Tag Allocation 5

Artificial Intelligence and Deep Learning-based Model for In...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Mathavan, N. Sivaranjani, S. Department of Electronics &Communication Engineering Nadar Saraswathi College of Engineering and Technology Vadapudupatti Tamil Nadu Theni India Department of Computer Science and Engineering Sethu Institute of Technology Kariyapatti Tamil Nadu Virudhunagar India

By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy;(iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made;and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques. © 2024 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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NeuroCluster: Neural Networks for Intelligent Energy-Aware Clustering in IIoT 2

NeuroCluster: Neural Networks for Intelligent Energy-Aware C...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Rajkumar, S. Gopalakrishnan, R. Shreemitha, V. Parkavi, R. Sankaranarayanan, S. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India K. S. Rangasamy College of Technology Electrical and Electronics Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350328202

The advent of Industrial Internet of Things (IIoT) has ushered in a new era of connectivity, providing unprece-dented insights and control over industrial processes. However, the energy efficiency of IIoT networks remains a critical concern, particularly in clustered environments. This paper introduces 'NeuroCluster,' a pioneering approach leveraging neural networks for intelligent and energy-aware clustering in IIoT networks. NeuroCluster harnesses the power of neural networks to dynamically adapt clustering strategies based on realtime energy consumption patterns and environmental fac-tors. By incorporating learning mechanisms, the proposed system intelligently allocates devices into clusters, considering their current energy states and predictive models of future energy demands. This adaptive clustering not only optimizes energy usage but also extends the overall network lifetime. The neural network architecture employed by NeuroCluster is designed to capture intricate relationships between device characteristics, contextual variables, and energy consumption profiles. Training the neural network involves learning from historical data and continuous updates, enabling the system to evolve and adapt to changing industrial environments. To validate the effectiveness of NeuroCluster, extensive simulations were conducted using representative IIoT scenarios. Results demonstrate significant improvements in energy efficiency, showcasing the ability of the system to outperform traditional clustering methods. Further-more, NeuroCluster exhibits robustness in diverse operational conditions, making it a scalable and adaptable solution for a wide range of IIoT applications. © 2024 IEEE.

关键词： Energy efficiency

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