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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Chawla, Pronika Parihar, Harshita Mathur, Ankit Manav Rachna International Institute of Research and Studies Faculty of Engineering & Technology Department of Computer Science & Engineering Faridabad India

ISBN: (纸本)9798350397284

The on-demand availableness of computer systems resources, especially storage of data (cloud storage) and rectifying power, with no direct ongoing administration by the user is what they call cloud computing. Large cloud functions are usually spread among several locations, each of which constitutes a data center. Customers may save money on capital expenses by using cloud computing, which frequently takes a 'pay-as-you-go' approach. Coherence in cloud computing is achieved by sharing resources. In today's world of the Internet, demand for cloud services is increasing drastically leading to the production of new services day by day. As the services increase, the data gets primarily targeted by spiteful users who attempt to steal the data for their own atrocious and unethical activities. Users and trustworthy applications are considering more security and privacy and services get more in demand. Moreover, this study has reviewed several algorithms such as CPE-ABE (Ciphertext policy attribute-based encryption), ABE, KP-ABE (Key policy attribute-based encryption) CSP (Constraint satisfaction problem), PKG, AES (Advanced Encryption Standards), SHA-1 (Secure Hash Algorithm), Photo encryption, Photo decryption, PRE, IDEA (International Data Encryption algorithm) and LSBG (Least Significant Bit Grouping) for image data security. As already discussed, the increasing threats and frauds around the world, safe and secure applications and services should be created to resolve this problem so that people can store data on a platform that can be relied on. This research study has discussed about the concept of what are the paradigms required for securing and protecting the data and securing the image data at an encrypted level. This study has reviewed several existing research works, studied different algorithms which have been used in different research articles, and compared their strengths and drawbacks accordingly. Different research works have been summarized in a form of ta

关键词： Constraint satisfaction problems

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Integrated Generator Design for Double Stator Hybrid Excitation Flux Switching Machine

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Progress in Electromagnetics Research C 2025年 155卷 11-18页

作者： Mostaman, Nur A. Sulaiman, Erwan Jenal, Mahyuzie Soomro, Irfan A. Faculty of Electrical Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Department of Electrical Engineering Quaid-e-Awam University of Science and Technology Nawabshah Pakistan

Double Stator Hybrid Excitation Flux Switching Generator (DS-HEFSG) has attracted significant interest in power-generating research owing to its potential for improved performance and sustainability. This research examines the utilization of hybrid excitation to enhance the efficiency of a three-phase DS-HEFSG specifically engineered for low-speed power applications such as renewable energy systems like wind and tidal turbines. The innovative method presents a double stator hybrid configuration for a traditional double stator generator, with performance evaluation performed via 2D finite element simulations. The findings indicate that the DS-HEFSG surpasses traditional topologies, rendering it an optimal selection for this generator configuration. The proposed design significantly increases the 18.76% cogging torque value while the electromotive force increases by approximately 21.62%. The proposed design also decreases permanent magnet eddy-current losses by 24.33%. Enhanced performance is noted in electromagnetic torque, torque ripple, output power, and overall efficiency. These advancements contribute to energy savings and lower maintenance costs, reducing reliance on fossil fuels and supporting the transition to greener energy solutions. The proposed DS-HEFSG with hybrid excitation is a viable alternative for efficient low-speed power production. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Energy efficiency

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An Approach for Early Detection of Schizophrenia 15

An Approach for Early Detection of Schizophrenia

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Paranagama, Sachin Uditha, Isiwara Daundasekara, Pamodya Hettiarachchi, Shehani Weerasinghe, Lokesha Kasthurirathna, Dharshana Sri Lanka Institue Of Information Technology Faculty Of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350370249

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Ensembled Machine Learning Models for Antenna Optimization in Wireless Communication/ Biomedical Applications 3

Ensembled Machine Learning Models for Antenna Optimization i...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Upadhyay, Nidhi Kaur, Amanpreet Singh, Ashima Pattanayak, Arnab Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798331528201

In this work, the antenna optimisation process is done by using ML algorithms such as KNN, random forest, Gradient Boost, XG Boost and MLP. Then the predicted results are ensembled using Voting Regressor. A Cylindrical Dielectric Resonator Antenna (CDRA) was designed and simulated in CST MWS'23 for this purpose. Dataset is created by varying the radius of the DR using parametric sweep function of the EM simulator. A close prediction between actual values and the predicted ones is obtained using these ML models. It is observed that by ensembling these varied approaches, a model that can handle different types of relationships in the data, can be created. This algorithm can be further utilized for optimisation of other antenna parameters and thus helping in reducing time in optimising complex antenna designs. The accuracy achieved by the ensemble model is 98.6% while maintaining a small MSE of 0.001. The optimized design shows stable characteristics within the ISM band and hence can be used for biomedical as well as wireless communication applications. © 2024 IEEE.

关键词： Random forests

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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Emotion Recognition from Electroencephalogram (EEG) Signals Using a Multiple Column Convolutional Neural Network Model

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SN computer science 2024年第2期5卷 213页

作者： Jha, Sonu Kumar Suvvari, Somaraju Kumar, Mukesh Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

Emotions are vital in human cognition and are essential for human survival. Emotion is often associated with smart decisions, interpersonal behavior, and, to some extent, intellectual cognition. From the recent literature on emotion recognition, we understand that the researchers are showing interest in creating meaningful "emotional" associations between humans and machines;there is a demand for accurate and scalable systems to detect human emotional states, as emotion recognition is needed to understand the mental status of such persons who cannot communicate their emotions, such as disabled people, mentally challenged persons, etc. Therefore, EEG signals provide a non-invasive method to identify the emotions of these disabled humans. The research community has recently been very interested in employing electroencephalography (EEG) for emotion classification since end-users have wearable EEG systems that may offer a portable, cheap, and straightforward technique for identifying emotions. Deep learning models have recently been extensively used to extract characteristics and recognize emotions from EEG recordings. Apart from that, various papers were reviewed in this research. This paper presented a multiple-column CNN network with a leaky ReLU activation function on the EEG brain wave and DEAP datasets. Multiple scalp electrode locations are used to collect EEG signals, and each electrode offers spatially unique data. The network can detect spatial correlations and extract characteristics that depict the spatial distribution of brain activity by employing a multiple-column CNN, which simultaneously processes signals from many channels. This makes it possible for the model to recognize emotions using the spatial information in EEG data. The result analysis was evaluated on different CNN models, and it was observed that an accuracy of 98.10% was achieved on the EEG brainwave dataset and 81% on the DEAP dataset. © 2024, The Author(s), under exclusive licence to Sprin

关键词： Attention Convolution neural network (CNN) Deep learning EEG Emotion recognition GCNN Multicolumn CNN

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Development of a Comprehensive Deep Learning Framework for Enhanced Detection and Accurate Classification of Renal Cancer 8

Development of a Comprehensive Deep Learning Framework for E...

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Bino, J. Preethi, C. Renukadevi, M. Rathish Babu, T.K.S. Kanageswari, S. Nelson, Leema St. Joseph's Institute of Technology Department of Electronics and Communication Engineering India Sethu institute of technology Department of Computer Science Engineering India SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai89 India Loyola College Department of computer science and Application Chennai India Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9798331544607

Accurate and early diagnosis of renal cancer is important for the improvement of outcomes in patients;hence, timely intervention has much to do with the effectiveness of treatments and survival rates. While there has been an increase in medical imaging modalities, early detection of renal cancer has become quite plausible, though the complexity introduced in the characteristics of tumors calls for advanced methods for their reliable classification. In this work, a new LSTM+CNN-based model is developed for renal cancer disease detection by integrating sequential learning capability from LSTM networks together with the powerful feature extraction abilities of CNN. The system is designed to improve both the accuracy and efficiency in renal cancer diagnosis based on the medical imaging data using spatial and temporal features. Among these, the proposed LSTM+CNN-based model has turned in better accuracy with quicker processing time and better overall classification performance compared to the state-of-the-art models. The proposed model also allows for the non-invasive, high-precision differentiation of renal tumors into low- and high-grade ones, with a view to early diagnosis and prediction. These results provide a proof of the enormous potentials of deep learning models, especially the LSTM+CNN architecture, toward making renal cancer detection an efficient and practical clinical solution. © 2025 IEEE.

关键词： Mammography

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Study on Implementation of Shor’s Factorization Algorithm on Quantum computer

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SN computer science 2024年第4期5卷 413页

作者： Kumar, Mandeep Mondal, Bhaskar Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

The fundamental security aspect of the classical crypto-system depends on integer factorization and discrete logarithm problems. The quantum factorization problem is a crucial problem in quantum computing as it has significant implications for cryptography and security. Shos’s paper on “Polynomial-time algorithms for discrete logarithms and factoring on a quantum compute” has become a threat to the classical crypto-system, influencing many researchers to work on factorization problems using quantum computing. Quantum computers (QC) can be essential in running different factorization algorithms in polynomial time. However, practical implementation on larger numbers is still a major challenge due to the requirement for error correction and massive quantum devices. Although the quantum factorization issue puts traditional cryptographic systems at risk, it also opens up new possibilities for quantum communication and encryption. The challenge of factorization opens up an entirely new field for research into quantum communication protocol security, including quantum key distribution and the development of quantum-resistant cryptographic systems. This paper surveys the various quantum algorithms for factoring a number into prime integers. We present a simulation study of the Quantum factorization algorithm to determine the period of a process. Turning the factoring problem into the challenge of determining a function’s period is the essential strategy of practical implementation using the quantum circuit. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Factorization Quantum Fourier Transformation Quantum period finding Quantum phase estimation Shor’s algorithm

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An energy efficient Dantzig Wolfe Decomposition based parallel optimization for software-defined industrial IoT

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Journal of Intelligent and Fuzzy Systems 2023年第2期44卷 2441-2454页

作者： Sathish, S. Kavitha, K. Poongodi, J. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electronics and Communication Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Tamil Nadu Coimbatore India

The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. © 2023 - IOS Press. All

关键词： Internet of things

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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