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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Srinivasan, Dhamodharan Premkumar, M. Deepa Nivethika, S. Dhilipkumar, P. Parameswari, S. Chowdary, M. Kalpana Centre for Blockchain and Cybersecurity Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore India Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Vit University School of Computer Science and Engineering Chennai India Sri Ranganathar Institute of Engineering and Technology Department of Ece Coimbatore India Sri Sairam Insitute of Technology Department of Ece Chennai India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

In this article, the potential of low-terahertz (THz) technology is discussed to present high data rates in future biomedical systems, and also in the 6G mobile system. However, due to the loss, the design of high-gain antennas is crucial to overcome power limitations and compensate for the additional path loss. This article highlights recent developments in wideband and high-gain sub-mm-wave and low-THz antennas and their fabrication technologies. The advancements in recent technologies have enabled the manufacture of antennas with complicated structures with precise accuracy and less expensive. Overall, the paper emphasizes the importance of high-gain antennas in overcoming the challenges associated with low-THz technology and highlights the potential of AM technology in the development of low-cost, high-performance THz antennas. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Antennas high gain low-cost low-terahertz three-dimensional printing (3DP)

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Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

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computer Systems Science & engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan M.Kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and *** proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of ***,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

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Design and Implementation of Light Weight Cryptography Using Spartan-6 FPGA 2

Design and Implementation of Light Weight Cryptography Using...

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2nd IEEE International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Poojari, Asmita Kiran Kumar, V.G. NMAM Institute of Technology Department of Computer Science and Engineerng Karnataka Nitte574110 India A J Institute of Engineering and Technology Department of Electronics and Communication Engineering Kottara Mangaluru575006 India

ISBN: (纸本)9798350354461

In the current scenario where our lives are totally obsessed with the internet, data security (confidentiality, authenticity, integrity, non-repudiation) plays a cardinal role. Therefore, there's constant demand of stronger encryption methods. Because of these reasons, information systems need to be secured. The foundation of these systems are cryptographic algorithms, which guard data and resources against unauthorized access. In recent years, the Internet of Things and the cyber-physical systems have expanded rapidly and significantly. This expansion has fuelled the development and broad adoption of low-cost devices across widespread IoT *** of the initial lack of security features in resource-constrained devices, many are being used to meet the broad application needs of the IoT and the cyber physical systems, where privacy and security have become key concerns and thus need for lightweight cryptography for these resource constrained devices. This study presents a lightweight cipher that combines the BRIGHT and SIMON structures into a single Feistel framework using ARX (Addition-Modulo, Rotation, and XOR) operations. The cipher passes the Avalanche test, the key sensitivity test, correlation coefficient, entropy, and histogram requirements, encrypting 32-bit plaintext with a 64-bit key. The software implementation is done with the Xilinx toole or Abstract. © 2024 IEEE.

关键词： Ciphertext

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Real-time Animal Identification and Alert System using IoT and Deep Learning 2

Real-time Animal Identification and Alert System using IoT a...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Manujakshi, B.C. Shashidhar, T.M. Ravikiran, H.N. Asha, R. Bilagi, Rajeev Karnataka Bengaluru India Department of Computer Science and Engineering Acharya Institute of Technology Karnataka Bengaluru India Department of Electronics and Communication Engineering Adichunchanagiri University Karnataka Mandya Nagamangala India Department of Electronics and Communication Engineering Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India

ISBN: (纸本)9798350383867

The amount of traffic accidents that result in fatalities and injuries is one of the most urgent problems facing the world today. Many tragic incidents are caused by collisions between humans and animals. Worldwide, there are startlingly many automobile accidents' "one every few seconds. Our primary objective is to decrease the probability of traffic accidents caused by animals by identifying them and informing cars of their presence. We suggest using IoT technology to create a detection and warning system that can significantly lower animal-related incidents. The purpose of this warning system is to alert cars on the roadway as soon as a detection event takes place by means of a signal. Our solution is based on Internet of Things technology, with the Raspberry Pi3 Model B acting as the main component for car warning and animal identification. Real-time photographs and videos of animal movements are captured by the Pi camera, which is equipped with a Raspberry Pi, through the use of sensors to identify impediments. After that, it alerts any vehicles and pedestrians using the woodland road. Additionally, it marks particular distances at night with lights. In the end, the document' s primary goal is to safeguard numerous people from possible harm and to preserve animals from mishaps. In order to do this, the YOLO (You Only Look Once) algorithm was created. It processes approximately 45 frames per second and examines the complete picture in order to determine if objects are present or not. © 2024 IEEE.

关键词： Deep learning

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Efficient Mobility in Next Generation Heterogeneous Wireless Networks Based on Enhanced Vertical Handoff Approach

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 128-139页

作者： Kaliappan, Vishnu Kumar Padmapriya, T. Manikanthan, S.V. Ilavarasi, S. Suresh Kumar, B. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Arasur Tamil Nadu Coimbatore India Melange Publications Puducherry India Melange Academic Research Associates Puducherry India Tamil Nadu Tiruchengode India Department of Electronics and Communication Engineering The Kavery Engineering College Mecheri Tamil Nadu Salem India

The progression of mobile technologies from first generation (1G) to fifth generation (5G) networks has brought about noteworthy advancement. The present network environment is characterized by the coexistence of various mobile networks and technologies, leading to overlapping or non-overlapping coverage areas, creating a next-generation heterogeneous landscape. Handoffs within the same network (i.e., GSM to GSM or vice versa) are known as horizontal or homogeneous handoffs. Conversely, when handoffs entail a transition to a different network type, such as moving from GSM to CDMA or vice versa, they are categorized as vertical or heterogeneous handoffs. Multiple link triggers, such as link down (LD), link going down (LGD), link detected, and link up, are employed to initiate and execute vertical handoff (VHO) when needed. We have determined the thresholds for vehicular speed and link triggers through extensive simulations, explicitly focusing on high-demand traffic types like video. This paper proposed an enhanced VHO approach based on IEEE 802.21, commonly called media independent handover (MIH), to improve Quality of Service (QoS) parameters, specifically mitigating packet loss (PL), and handoff delay and enhancing the user experience. © 2025 by the authors of this article.

关键词： 5G mobile communication systems

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Design and Implementation of a Decentralized Voting System Using Volta Blockchain 5

Design and Implementation of a Decentralized Voting System U...

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5th IEEE International Women in technology Conference, WINTECHCON 2024

作者： Arthi, M. Suganthi Evangeline, C. Bharadwaj, Rohit Avrit, M. Jaswant, Gogineni Siranjeevi, G.P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350374070

The drive for secure, open, and easily accessible voting systems has spurred the advance of decentralized blockchain-powered voting systems. This paper outlines the design and implementation of a decentralized voting system that utilizes the Hardhat Ether for testing and deployment, the Volta blockchain, Solidity smart contracts, and MetaMask for user interaction. Our approach ensures the confidentiality and integrity of votes by harnessing the irreversible and transparent properties of blockchain technology, enabling participation from any location with internet connectivity. Users can safely cast their ballots straight from their web browsers thanks to the inclusion of MetaMask, improving accessibility and usability. Furthermore, the application of Hardhat Ether simplifies the development process, enabling reliable deployment and testing protocols. This paper discusses the architecture, functionality, and security considerations of our decentralized voting system, highlighting its potential to revolutionize democratic processes worldwide. © 2024 IEEE.

关键词： Voting machines

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Android Malware Detection using SVM and Neural Network 14

Android Malware Detection using SVM and Neural Network

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Gundla, Rajeshwari Gengaje, Sachin R. Department of Computer Science & Engineering PAHSUS Solapur India Department of Electronics Engineering Walchand Institute of Technology Solapur India

Over the past few years, Android OS, the most widely used operating system (OS), has experienced tremendous growth in popularity for smartphones. Cybercriminals will use this opportunity to commit piracy and malware crimes. Traditional malware detection methods are insufficient to stop newly developed sophisticated malware. Therefore, intelligent malware detection systems are required to lower the malicious activities risk. Malware classification using machine learning techniques has shown encouraging results in recent years. In this paper, we propose a framework for android malware detection using SVM and Neural Network. Our Deep learning framework outperforms existing machine learning approaches. All experiments are performed on two datasets (Malgenome-215 and Drebin-215) to assess our model. The results of experiments demonstrate that the improved algorithm has better detection rate as compared with other malware detection approaches. Our Neural network model achieved F1 score over 99%. © Grenze Scientific Society, 2023.

关键词： Android malware

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PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20764-20777页

作者： Humayun Kabir, M. Hasan, Md. Ali Islam, Md. Shafiqul Ko, Kyeongjun Shin, Wonjae Islamic University Department of Electrical and Electronic Engineering Kushtia7003 Bangladesh Korea University School of Electrical Engineering Seoul02841 Korea Republic of Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Hongik University School of Electronics and Electrical Engineering Seoul04066 Korea Republic of

Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PC). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and sub-urban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that proposed PC-DeepNet achieves superior accuracy than existing model-based and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches. © 2025 IEEE.

关键词： Global positioning system

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Medi-Connect: An End-to-End Intelligent Framework to Facilitate New Era Online Consultations Toward Sustainable Healthcare 5th

Medi-Connect: An End-to-End Intelligent Framework to Facilit...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Yadav, Jayash Raj Singh Jain, Paras Sharma, Shanu Dubey, Ashwani Kumar Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819716814

The healthcare industry has undergone a significant transformation in recent years, with the integration of information technology and online platforms revolutionizing the way medical services are delivered. One such development is the emergence of online consultations, enabling patients to seek medical advice from doctors without the need for in-person visits. The increasing popularity of online consultations in the healthcare industry has highlighted the need for efficient systems to support remote medical services. In this paper, an intelligent framework is proposed to assist patients and doctors during online consultations. The framework offers end-to-end services starting from patient registration, doctors’ availability, the online appointment of doctors, etc., required during an online consultation. The proposed framework is also capable of assisting the doctors during diagnosis of diseases and drug recommendation through intelligent decision-making capability based on the symptoms provided by the patient. The use of the Naïve Bayes algorithm is explored here to perform classification on patient data and the determination of the probability of specific diseases. In addition to accurate diagnoses, the system recommends appropriate medicinal treatments and provides a database of specialist doctors for further consultation. This integration of technology and health expertise seeks to streamline the healthcare process, saving time and reducing costs for both patients and doctors. By presenting a streamlined framework for online consultations, the main aim of this work is to contribute to the advancement of virtual healthcare services and to bridge the gap between traditional healthcare practices and the evolving digital landscape, ultimately improving healthcare accessibility and outcomes for patients worldwide. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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AppleDiseaseNet-B7: A Robust Solution for Apple Leaf Disease Classification and Model Evaluation 4

AppleDiseaseNet-B7: A Robust Solution for Apple Leaf Disease...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mathias, Ajisha Raj, J. Relin Francis Raja, S.R. Krishnan, R. Santhana Balamuralikrishnan, G. Soundiraraj, N. Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Hindustan Institute of Technology and Science Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India

ISBN: (纸本)9798331540364

Apple leaf diseases, like cedar apple rust, black rot, and apple scab, pose severe obstacles to apple farming and impact crop productivity and quality. The sophisticated classification model AppleDiseaseNet-B7, known for its efficiency and outstanding performance, was created using the EfficientNetB7 architecture and is presented in this paper. The model improves its feature extraction capabilities by utilizing pretrained weights from ImageNet, which makes it a good fit for differentiating between various apple leaf diseases. AppleDiseaseNet-B7 incorporates numerous unique layers intended to increase classification precision. A Global Average Pooling layer streamlines feature maps, complicated patterns are captured by a dense layer with ReLU activation, overfitting is reduced by a dropout layer, and class prediction is accomplished by a final dense layer with softmax activation. The model's resilience is further increased using thorough data preparation, and augmentation approaches from the PlantVillage and Kaggle Apple Leaf Disease datasets. A comprehensive comparison is made between AppleDiseaseNet-B7's performance and other well-known models, including DenseNet121, ResNet50, and AlexNet. The analysis shows that AppleDiseaseNet-B7 regularly performs better than comparable models in terms of accuracy, precision, recall, and F1 score, among other criteria. This comparison demonstrates how well AppleDiseaseNet-B7 performs in classifying apple leaf diseases, providing a reliable and effective method for managing and detecting agricultural diseases. The study emphasizes how crucial sophisticated model selection is to enhancing crop health monitoring and disease control. © 2024 IEEE.

关键词： Fruits

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