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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Ur Rahman, Aniq Chaudhry, Hassan Nazeer Asim, Malik Muhammad Kulsoom, Farzana University of Engineering and Technology Computer Department Taxila Pakistan Barani Institute of Technology Computer Science Department Rawalpindi Pakistan University of Engineering and Technology Telecommunication Engineering Department Taxila Pakistan

ISBN: (纸本)9798350338461

Fake news is a growing problem in the digital age, spreading misinformation and affecting public opinion. Existing fake news detection is based on style analysis or news generator's behavior analysis, the former fails if the content is generated using existing news corpus while the latter requires a very specific data set. In this research, we aim to address the issue of fake news detection by employing deep learning-based time series analysis (TSA). We propose a TSA method to gauge the authenticity of the news based on previously available news content of a similar genre. We employed pre-trained models for encoding including GloVe and BERT and transformer-based sequence-to-sequence (Seq2Seq) for TSA. The results demonstrate 98% accuracy of pre-trained models, such as GloVe and BERT, over traditional encoding approaches having accuracy between 77% and 93%. Our study also compares the effectiveness of various deep learning methods, including Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM with attention, GRU with attention, and Transformers with 8 and 16 multi-heads. The results show that Transformers with 8 and 16 multi-heads achieve 98% and 97% accuracy respectively as compared to LSTM (87%) and GRU (88%). Our work is useful for future research in TSA-based fake news detection and proposes to use GloVe and BERT-based encoding and multi-head transform architecture. © 2023 IEEE.

关键词： Harmonic analysis

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Navigating Bengali Linguistics: Insights from Machine and Deep Learning Perspectives for Categorization of Sentences 26

Navigating Bengali Linguistics: Insights from Machine and De...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md Minhazul Jubaer, Tanbeer Srizon, Azmain Yakin Rakib Hossain, Md. Mahedy Hasan, S.M. Farukuzzaman Faruk, Md. Minhazur Rahman, A.F.M. Rajshahi University of Engineering and Technology Department of Computer Science & Engineering Rajshahi Bangladesh Rajshahi University of Engineering and Technology Department of Electronics and Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

The paper presents an innovative exploration into the classification of Bengali sentences, an essential aspect of advancing natural language processing (NLP) for languages characterized by rich linguistic diversity. In this comprehensive study, various machine learning and deep learning methodologies were meticulously applied to navigate the intricate linguistic landscape of the Bengali language. The research draws upon and synthesizes insights from previous studies that have focused on aspects such as sentence attributes, simplification algorithms, context-free grammar applications, and grammar-rule based transformations. The core of this study is anchored in the deployment of three distinct vectorization approaches - BERT-based, Glove-based, and TF-IDF-based - for the nuanced classification of Bengali sentences into simple, complex, and compound categories. A series of classifiers were applied, with the BERT-based vectorization demonstrating exemplary proficiency, registering an impressive 98.07% overall test accuracy. This finding underscores the model's robustness and versatility, illuminating its potential applicability beyond the designated dataset to encompass bespoke, real-life datasets. The research is set against the backdrop of a recently published large-scale Bengali sentence dataset titled Bangla Transformation of Sentence Dataset (BTSD) consisting of 3793 samples, which has filled a significant void in the existing literature, enabling a more intricate and detailed analysis. The findings of this study not only contribute to the enriched understanding of Bengali sentence structures but also hold implications for the broader field of computational linguistics and NLP. © 2023 IEEE.

关键词： Bengali Text Processing BERT Deep Learning Natural Language Processing Sentence Classification

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Solar Power Generation Technique and its Challenges - A Comprehensive Review 15

Solar Power Generation Technique and its Challenges - A Comp...

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15th International Conference on Advances in Computing, Control, and telecommunication Technologies, ACT 2024

作者： Kitey, Himanshu Chandankhde, Pankaj Jajulwar, Kapil Shaikh, Mohammad Shahnawaz Fatinge, Pragati M. M. Tech Communication Engineering Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur Nagpur India Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur India Department of Computer Science and Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (纸本)9798331300579

In the contemporary world, energy stands as an essential driving force behind socioeconomic development. However, with the alarming levels of environmental pollution, there has been a growing focus on renewable energy sources. These sustainable alternatives are gaining increasing attention, particularly as the depletion of fossil fuels becomes more ap-parent. Natural components such as sunlight, wind, and rainfall are used to generate renewable energy. Among these non-traditional renewable sources, solar energy emerges as a prominent contender for power generation. Solar power generation has gained recognition as a promising and environmentally sustain-able renewable energy source to meet growing global energy demands while minimizing cli-mate change consequences. This paper extensively examines solar power generation techniques, encompassing Photovoltaic (PV) Systems and Solar Thermal Technologies. The paper explores the present state of solar power generation technology, outlines its advantages, and researches the various challenges obstructing its widespread adoption. Furthermore, the study offers insights into potential solutions and outlines future directions for advancing solar power generation techniques. © Grenze Scientific Society, 2024.

关键词： Photovoltaic (PV) System Renewable Energy Socio-economic Solar Power Solar Thermal Technologies

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Wav2Vn: Scalable Unsupervised Pre-training and Adaptation for Multi-Accent Vietnamese ASR 9

Wav2Vn: Scalable Unsupervised Pre-training and Adaptation fo...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Kim, Viet Nguyen Ngoc, Huyen Nguyen Thi Minh, Hieu Dam Khoi, Nguyen Do Hoang Do, Tien-Dung Dung, Cao Truong Posts and Telecommunication Institute of Technology Department of Information Technology Ha Noi Viet Nam Swinburne University Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Computer Science Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Faculty of Information Ha Noi Viet Nam Posts and Telecommunication Institute of Technology Department of Electronic Engineering Ha Noi Viet Nam

ISBN: (纸本)9798331505042

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper. © 2024 IEEE.

关键词： Deep learning

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Blockchain based E-Voting System on Smartphones 7

Blockchain based E-Voting System on Smartphones

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Pillai, Sandeep Lahamage, Darshan Gurav, Shreya Ambawade, Dayanand Sardar Patel Institute of Technology Department of Electronics & Telecommunication Mumbai India Sardar Patel Institute of Technology Department of Computer Science & Engineering Mumbai India

ISBN: (纸本)9798331527822

India, the world's largest democracy, faces significant challenges in conducting free and fair elections due to the prevalence of vote tampering, booth capturing, and rigging. To address and solve these challenges, a blockchain-based e-voting system will be developed that is secure, transparent, and accessible to all eligible voters in India. The decentralized nature of blockchain, combined with the transparency and security of smart contracts written in Solidity, will ensure that the voting process is tamper-proof and transparent. The system will be developed and tested on Ethereum testnet, and the complexity of existing systems will be simplified into a simple smartphone application that can be accessed by all eligible voters in India. The system will remove dependency on crypto wallets like Meta-mask, the backend api's are developed using ***, *** and ***. SHA-3 will be used for hashing and encryption purposes. The frontend will be developed using React Native, ensuring a user-friendly and accessible interface. The successful implementation of this blockchain-based e-voting system could revolutionize the democratic process in India, making it more accessible, secure, and transparent, and setting an example for other countries to follow. © 2024 IEEE.

关键词： Ethereum

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Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Model on EEG Data 3

Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Mod...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hasan, Nowshad Sharif, Md. Maskat Rahman, Miskatur Islam, Md Saiful Khan, Md Tohedur Rahaman Hoque, MD Jiabul Bahar, Anamul Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Computer Science Enginnering Chittagong Bangladesh International Islamic University Chittagong Department of Computer and Communication Engineering Chittagong Bangladesh University of London Department of Computer Science Enginnering London United Kingdom

ISBN: (纸本)9798331534400

Epileptic seizure detection from EEG signals is essential for the diagnosis and treatment of neurological conditions. However, this task is difficult because of the complex and fluctuating characteristics of EEG data. To overcome these difficulties, this research employs a specialized CNN-GAN hybrid model designed to improve seizure prediction by generating realistic synthetic data. This approach addresses class imbalance and enhances feature learning. The process begins with preprocessing EEG datasets to remove noise and artifacts. Data augmentation follows, expanding the dataset and reducing the risk of overfitting. Segmentation then isolates important features to boost prediction accuracy. Feature extraction is achieved using a custom CNN model, and classification is performed with a GAN model. The model demonstrates high accuracy, with results of 99.95% on the BONN Dataset, 98.93% on the CHB-MIT Dataset and 98.80% on the SWEC-ETHZ Dataset. Overall, the custom CNN-GAN hybrid model significantly improves the accuracy of predicting epileptic seizures from EEG signals across various datasets. © 2024 IEEE.

关键词： Prediction models

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Solarsense: Enhancing Energy Efficiency Through Iot-Enabled Solar Tracking 2

Solarsense: Enhancing Energy Efficiency Through Iot-Enabled ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Thopate, Kaushalya Gawade, Mayuri Kakade, Surabhi Bahir, Suvarna Kulkarni, Mukund Jaiswal, Krishna Nilesh Computer Engineering Vishwakarma Institute of Technology Pune India Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Computer Engineering Trinity Academy of Engineering Pune India Electronics and Telecommunication Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350360523

The surging global demand for energy is primarily fueled by a burgeoning world population and elevated living standards. However, with traditional fossil fuels like oil dwindling in availability, there is an urgent need to shift towards renewable energy sources. Given the rapid progress in renewable energy technologies, a key objective of the project is to develop a solar energy tracking panel that can rotate for enhanced power generation. This innovative panel, capable of adjusting its position to optimize energy output by tracking the sun, proves invaluable in monitoring power consumption. The design of this rotatable solar energy tracking panel, incorporating four essential components (solar energy tracking panels, light-dependent resistors (LDRs), an Arduino, and a motor), is intricately explained in this study. This configuration not only serves as an advanced educational tool but also provides engineering technology students with a comprehensive understanding of alternative energy sources like solar power. © 2024 IEEE.

关键词： Fossil fuels

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UAV Communication and Localization Network Deployment for Content Distribution in Emergency Scenarios 6

UAV Communication and Localization Network Deployment for Co...

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6th International Conference on Electronics and Communication, Network and computer Technology, ECNCT 2024

作者： Wang, Kailiang Yu, Ran Zhang, Donghui Zhang, Yuhao Lu, Jie Yu, Xinyu Men, Baoxia Zhang, Yi Ji, Yutong State Grid Jibei Information & Telecommunication Company Beijing China School of Computer Science National Pilot Software Engineering School Bupt Beijing China

ISBN: (纸本)9798350366143

Unmanned Aerial Vehicles (UAVs) are increasingly recognized for their potential to revolutionize emergency response communications and localization, especially when traditional infrastructure is damaged or non-existent. This study presents an iterative deployment strategy for UAVs equipped with directional antennas to optimize communication and localization in disaster-stricken areas. By integrating a line-of-sight coverage model with real-world environmental data, and utilizing a greedy algorithm alongside an improved Particle Swarm Optimization algorithm, the strategy refines UAV placement and antenna orientations iteratively. The results of simulations show that our method significantly enhances communication rates and localization accuracy, proving effective in complex emergency scenarios. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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Smart Agriculture: Leveraging CNN-RF Fusion for Enhanced Tomato Disease Classification 3

Smart Agriculture: Leveraging CNN-RF Fusion for Enhanced Tom...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Mahajan, Manish Upadhyay, Deepak Aeri, Manisha Kukreja, Vinay Sharma, Rishabh Military College of Telecommunication Engineering Faculty of Communication Engineering Madhya Pradesh Mhow India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttrakhand Dehradun India Chitkara Univeristy Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350381931

This paper presents a unique method to the categorization of tomato illnesses, using a combination of models involving Random Forest (RF) and Convolutional Neural Networks (CNN). The hybrid model incorporates both types of neural networks. The trial results show that the predictive algorithm has an extraordinary capacity to reduce the number of false positives, with accuracy values ranging from 78.7 to 95.1%. The model's strong recall scores, ranging between 86.1% to 95.2%, reflect its ability to recognize occurrences of each unique disease class and gather real positives. The model can detect true positives, confirmed by these studies. Furthermore, the balanced equilibrium of the F1 Ratings, which may range from 84.4% to 95.1%, demonstrates that the framework is capable of executing sickness classification tasks on a consistent and dependable basis. This new set of findings adds to the body of evidence. Statistics on sickness incidence are also accessible at various support levels, with some choices providing more precise data than others. The CNN-RF hybrid model is an effective and adaptable tool for tomato disease classification. This is due to the fact that it is able to reach accuracy values that are consistently between 0.96 and 0.98. © 2024 IEEE.

关键词： Convolutional neural networks

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