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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Karankar, Nilima Seth, Anita Institute of Engineering and Technology DAVV Department of Computer Engg. Indore India Institute of Engineering and Technology DAVV Department of Electronics and Telecommunication Indore India

ISBN: (纸本)9798350396157

The Internet of Things is thriving rapidly in terms of quality and quantity, helping us to provide better services every day. With the wide-spread growth of the Internet of Things, it introduces new technical challenges, such as managing a vast number of devices. In almost every field, like smart homes, smart transactions, automobiles, and healthcare, IoT is being used very effectively. Even though so many architectures and frameworks are proposed to overcome these challenges, security and scalability are major challenges for an IoT system. Additionally, IoT technology has been merged with other advanced technologies, such as cloud computing, big data, and blockchain, to form a strong IoT system. Blockchain is an emerging technology that is based on distributed protocols. It has the potential to ensure the security, scalability, and reliability of an IoT system and can provide secure digital transactions. This paper attempts to identify the current contributions to IoT security using blockchain and highlights various research gaps in this area. The paper concludes with a discussion on open challenges and research issues. © 2024 IEEE.

关键词： Scalability

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Robust Multi-Dialect End-to-End ASR Model Jointly with Beam Search Threshold Pruning and LLM

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SN computer Science 2025年第4期6卷 1-12页

作者： Shunmuga Priya, M.C. Karthika Renuka, D. Ashok Kumar, L. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Information Technology PSG College of Technology Coimbatore India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai India

This paper aims to develop a novel robust multi-dialect end-to-end ASR system with beam search threshold pruning. The efficacy of our proposed model is evaluated using word error rate (WER). Our key contributions are: (1) To develop an end-to-end ASR system using attention-based neural network architecture and analyze the effectiveness of two features such as MFCC and log mel filter bank energies on multiple speech dialect corpora including American, Britain, and Indian accents;(2) To integrate beam search threshold pruning as a decoding mechanism to reduce the decoding time (3) To conduct an experimental analysis to test the model performance and compare the results against baseline system. (4) Post processing analysis are carried out using Llama2-7B based large language model(LLM) for enhancing the performance of proposed ASR system. The proposed model significantly improves performance by 1.91% and 4.29% over clean and noisy speech in librispeech corpus. Similarly, for the Indian accented speech, the model attains an average WER of about 6.6%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic speech recognition Beam search Decoding LLM Log Mel filter bank energies

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Machine Learning Model Comparison for Flood Detection Application from Satellite Images 2

Machine Learning Model Comparison for Flood Detection Applic...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Sonavale, Amruta Chakkaravarthy, Midhun Rao, Surampudi Srinivasa Salleh, Hishamuddin Bin M. Jadhav, Jagannath Lincoln University College Department of Computer Science and Multimedia Malaysia Malla Reddy College of Engineering and Technology Department of Electronics and Communication Engineering Telangana India Kle College of Engineering and Technology Department of Electronics and Communication Engineering Chikodi India

ISBN: (纸本)9798350373110

Backgrounds: Flooding, a rapidly increasing and increasingly frequent natural disaster, requires effective management, even if unavoidable, and the development of an effective classifier for image detection is crucial for effective response. Methods: The study uses machine learning methods like closest K-nearest neighbors, logistic regression, support vector classification, and decision tree as classifiers. The accuracy of these algorithms varies. The dataset, consisting of ground and space-based sources, is used to determine their accuracy rates. The most accurate classifier is used for forecasting flooding in advance, allowing for faster evacuation and care for those in need. Result: In this case, we could anticipate the likelihood of flooding by analyzing historical rainfall data. The support vector machine produced an efficient accuracy of 93.92%. Conclusion: The accuracy of each classifier is shown, and detection is performed mostly with great precision. The outcomes of any image tests conducted are shown as well. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Smart Metering for Electricity Theft Detection and Power Monitoring 1

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Control Instrumentation System Conference, CISCON 2023

作者： Gaikwad, Vijay Thopte, Kaushalya Dave, Tilak Rajendra Deshmukh, Akash Abhay Afasarpasha, Patil Jiyan Tathe, Ayush Ramchandra Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune Pune India

ISBN: (数字)9789819758661

ISBN: (纸本)9789819758654

Electricity theft is a major problem that causes financial losses for utility companies and household users. To address this issue, we propose an IoT-based solution that detects electricity theft by bypassing or hooking methods. The proposed system utilizes a current measuring and comparing approach to monitor energy consumption indirectly from the electric pole to an intermediate distributor box and then to individual houses. The system periodically measures the current in the distributor box and electric meter and posts the data to the server database. During installation, the user's details are stored in the server database through a user-friendly mobile application, including the address, latitude, longitude using mobile GPS. The system uses statistical algorithms to compare the current values from the distributor box and electric meter. If a marginal difference between the currents is detected, the system identifies electricity theft and alerts the authorized mobile application with the user's details and shutdowns the electricity flow for particular that area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electric power measurement

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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IoT-Assisted AI Framework for Preserving Biodiversity: Monitoring Rare Trees in Dynamic Forest Ecosystems

IoT-Assisted AI Framework for Preserving Biodiversity: Monit...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vidyasree, G. Jothi, J. Nirmal Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering 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 Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India

ISBN: (纸本)9798331509828

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoLiA-Net (Forest Lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoLiA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoLiA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts © 2025 IEEE.

关键词： Biodiversity Forest Ecosystems Machine Learning Rare Trees Simulation Sustainability

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Rotation-Invariant LiDAR-based Localization Method Based on Feature Pyramid and Vector Neural Network 4

Rotation-Invariant LiDAR-based Localization Method Based on ...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Tian, Gengxuan Zhao, Junqiao Tongji University School of Electronics and Information Engineering Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350394375

LiDAR-based localization for unmanned vehicle has recently become a highly prominent research area. The LiDAR-based localization task typically involves two crucial tasks: place recognition and pose estimation. However, current research often fails to effectively address issues of place recognition retrieval failures caused by rotations. Some existing methods employ a strategy of coupling the pose estimation task with location recognition, but they often separate these two tasks using two distinct sets of networks to achieve robustness against rotations. Unfortunately, such methods frequently exhibit poor generalization. In this paper, we propose an innovative rotation-invariant LiDAR-based localization method based on feature pyramids and vector neural networks. This approach extracts rotation-invariant global features to accomplish the location recognition task and utilizes rotation-invariant local features for matching corresponding points in point clouds. Employing a coarse-to-fine point matching filtering method to eliminate outliers, and ultimately implementing pose estimation through the RANSAC algorithm, we successfully achieve high-precision and rotation-robust LiDAR-based localization. The experiments indicate that on the Oxford dataset, our method consistently achieves a remarkable 94.7% AR@1% regardless of whether the point cloud undergoes rotation, a performance significantly superior to other methods © 2024 IEEE.

关键词： Rotation

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A Semi-Supervised Deep Learning Approach for Intrusion Detection and Classification for the Internet of Things

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Biomedical Materials and Devices 2025年 1-17页

作者： Uthradevi, G. Thiruvasagam, P. Mythili, S. Manoj, S. Oswalt Department of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Electrical and Electronics Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Biomedical Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul 624622 India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India

Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats and ensuring the security of critical infrastructures. This paper introduces an innovative hybrid model that combines Deep Belief Networks (DBNs), Principal Component Analysis (PCA), and Support Vector Machines (SVM) with adversarial learning to enhance the detection of both known and emerging cyber threats. The proposed model achieves a strong balance of accuracy, efficiency, and scalability, making it highly effective for real-world applications. By incorporating adversarial learning, the system can detect zero-day exploits—previously unseen attacks—and adapt to evolving threats. This is achieved by training the model on adversarial samples, which improves its resilience to variations in attack patterns. SVM further enhances the model’s ability to classify known and unknown threats with high precision. The model was rigorously tested on two benchmark datasets, NSL-KDD and CICIDS2017, achieving outstanding results. It demonstrated a high accuracy rate of 99.73% on NSL-KDD and a low false positive rate of 0.55%, while on CICIDS2017, it achieved a false positive rate of 0.73%. The model also outperformed existing approaches in precision, recall, and F1-score, particularly in detecting complex attack types such as R2L (Remote-to-Local), U2R (User-to-Root), and Botnet attacks. Additionally, PCA reduced the feature space by 40%, significantly lowering the model’s inference time to just 9.0 ms per sample. This makes the model highly suitable for real-time intrusion detection in high-traffic network environments, where speed and efficiency are critical. Overall, this hybrid model strengthens cybersecurity defenses and contributes to building resilient infrastructures. By effectively detecting and mitigating cyber threats, it supports the creation of safer, fairer, and more inclusive digital communities. The integration of adversarial learning and dimensionality reduction ensures rob

关键词： Adversarial learning Cyber security Deep belief networks Dimensionality reduction Feature extraction IDS Networks PCA SVM

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Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

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computer Modeling in engineering & Sciences 2023年第9期136卷 2291-2319页

作者： Abdullah Ahmed Al-Dulaimi Muhammet Tahir Guneser Alaa Ali Hameed Mohammad Shukri Salman Department of Electrical and Electronics Engineering Karabuk UniversityKarabuk78100Turkey Department of Computer Engineering Istinye UniversityIstanbul34303Turkey College of Engineering and Technology American University of the Middle EastEgaila54200Kuwait

Recently,the demand for renewable energy has increased due to its environmental and economic *** panels are the mainstay for dealing with solar energy and converting it into another form of usable *** panels work under suitable climatic conditions that allow the light photons to access the solar cells,as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these ***,the panels are unable to work under these conditions.A layer of snow forms on the solar panels due to snowfall in areas with low ***,it causes an insulating layer on solar panels and the inability to produce electrical *** detection of snow-covered solar panels is crucial,as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper *** paper presents five deep learning models,■-16,■-19,ESNET-18,ESNET-50,and ESNET-101,which are used for the recognition and classification of solar panel *** this paper,two different cases were applied;the first case is performed on the original dataset without trying any kind of preprocessing,and the second case is extreme climate conditions and simulated by generating motion ***,the dataset was replicated using the upsampling technique in order to handle the unbalancing *** conducted dataset is divided into three different categories,namely;all_snow,no_snow,and partial *** fivemodels are trained,validated,and tested on this dataset under the same conditions 60%training,20%validation,and testing 20%for both *** accuracy of the models has been compared and verified to distinguish and classify the processed *** accuracy results in the first case showthat the comparedmodels■-16,■-19,ESNET-18,and ESNET-50 give 0.9592,while ESNET-101 gives *** the second case,the models outperformed their counterparts in the first case by evaluating performan

关键词： Deep learning CNN models image classification solar panels solar panel defect detection

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A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

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computer Systems Science & engineering 2023年第4期45卷 329-342页

作者： Nada M.Elfatih Elmustafa Sayed Ali Maha Abdelhaq Raed Alsaqour Rashid A.Saeed Department of Electrical and Electronics Engineering Red Sea UniversityPort Sudan33311Sudan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics Engineering Sudan University of Science and TechnologyKhartoum11111Sudan

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection *** optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold ***,these methods do not take into account the effect of sample size and its effect on improving CoR *** general,a large sample size results in more reliable detection,but takes longer sensing time and increases ***,the locally sensed sample size is an optimization ***,optimizing the local sample size for each cognitive user helps to improve CoR *** this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)*** the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关键词： Cognitive radio spectrum sensing energy detection double threshold neural network machine learning optimization quality of service

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