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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Anukeerthana, B.S. Lavanya, Dharshini Suresh Gurucharran, V. Madhumathi, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

The installation of water distribution networks (WDNs) on farmland is a key facet of agricultural practices in India, particularly for ensuring adequate irrigation of crops, especially in the context of facility agriculture. Ant Colony Optimization (ACO) presents a disruptive technological approach to tackling the complexities of agricultural resource management. This paper explores the application of ACO techniques to address the challenges faced in enhancing crop yield and water efficiency in Indian agriculture. By mimicking the collective behaviors of ants, these techniques aim to optimize resource allocation, thereby enabling precise and adaptable strategies for crop growth. Thus, ACO emerges as a promising avenue for fostering sustainable farming practices in the Indian agricultural landscape. In a country like India, where water scarcity is a pressing issue, the efficient allocation of resources inspired by ant colonies can significantly contribute to mitigating water-related challenges and ensuring agricultural sustainability. © 2024 IEEE.

关键词： Ant colony optimization

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Self-annotated Labelling and Training Data for Traffic Video Object Detection Using Machine Learning Techniques 7th

Self-annotated Labelling and Training Data for Traffic Video...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Rahul Chiranjeevi, V. Swamy, Maanesh M. Krishna Prasath, M.K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India

ISBN: (纸本)9783031699818

Classical machine learning algorithms are susceptible to objective elements like video quality and the weather, which results in inferior detection results in an erroneous identification of Unmanned Aerial Vehicle (UAV) action photos. Deep learning is suggested for identifying cars in UAV video. Thanks to the technology offered by road management systems, real-time visual information is now accessible in thousands of locations on road networks. The first step in identifying or preventing accidents is locating the vehicles on the path of travel. Convolutional neural networks have made substantial advancements in the field of object detection. Enhancing conventional computer vision techniques. However, there are drawbacks because the pre-trained models that are currently available only provide a low detection rate, especially for small objects. The major drawback is that, in order to retrain the automobile maker, they must manually identify the vehicles that appear in the photographs captured by each IP camera on the road infrastructure. This task will be impossible to complete even if we install hundreds of cameras throughout the extensive road network. The technique utilized in this study uses CCTV footage with a comparable range of images with the aim of identifying cars in an image. The spatial brightness of the original sample is first translated using the HSV (Hue, Saturation, Value) method to maximize sample diversity and adaptability to different lighting conditions. The performance of the basic SSD is enhanced by incorporating focus loss because of feature extraction. Following a trained neural network model’s examination of the UAV footage, the effectiveness of drone identification is assessed. The results show that the proposed method have a car detection rate of 96.49%. © IFIP International Federation for Information Processing 2024.

关键词： Deep learning

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Mind controlled Movements: Directing Action with left and right thoughts cooperating with EEG 2

Mind controlled Movements: Directing Action with left and ri...

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

作者： Sajitha, A.K. Srithilak, R. Karthic, Vikas K. Rathi, G. Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9798350383867

This study demonstrates the potential of Brain-computer Interface (BCI) technology to enable individuals to control devices simply through thought, specifically focusing on actions such as moving left and right. Leveraging machine learning techniques, particularly linear regression analysis, on real-time datasets, the research endeavors to develop an application tailored for individuals with paralysis. The process involves the systematic collection of data through the placement of electrodes to capture brain signals associated with the intention to perform left and right movements. These collected datasets are then processed and prepared to train a machine learning model. Through iterative training, the model learns to accurately interpret the brain signals associated with the intended actions of moving left or right. The results of the study underscore the efficacy and precision of the real-time datasets collected, as demonstrated by their successful implementation within the machine learning model. By successfully splitting and discerning between the signals corresponding to left and right movements, the model showcases promising potential for practical application in assisting individuals with paralysis. Overall, this research represents a significant step towards harnessing the capabilities of BCI technology combined with machine learning to enhance the quality of life for individuals facing motor impairments, offering new avenues for intuitive and efficient control of assistive devices through the power of thought. © 2024 IEEE.

关键词： Fast Fourier transforms

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Deep Learning Based Corona Discharge Severity Classification for High Voltage Equipment 50

Deep Learning Based Corona Discharge Severity Classification...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Messaoudi, Maher Kameli, Sayed Mohammad Refaat, Shady S. Abu-Rub, Haitham Trabelsi, Mohamed Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait

ISBN: (纸本)9781665464543

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage. © 2024 IEEE.

关键词： Long short-term memory

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Flexible Low-Power Digital Circuits with Unipolar Amorphous Silicon Thin-Film Transistors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第12期3卷 533-543页

作者： Ranjan, Shubham Kapar, Sparsh Lee, Czang-Ho Wong, William S. Sachdev, Manoj University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Thin-film transistor (TFT) technology has demonstrated its effectiveness in large-area cost-efficient applications such as displays, flexible electronics, and medical devices. However, TFTs are typically unipolar in nature, and therefore, the realization of CMOS-like digital circuits is challenging. Traditional methods for implementing logic gates and complex circuits with unipolar TFT devices lead to high static power consumption and limited output swing. While various mitigation techniques have been developed, they fail to eliminate the direct path current problem in these circuits, which hinders static power reduction. The objective of this study is to address these issues and study its effect on flexible substrate. In this article, we propose logic gates that address these issues using a half-latch circuit. To demonstrate the concept, a 3-to-8 decoder was built using only n-type amorphous silicon (a-Si:H) TFTs on both glass and flexible substrates. We analyzed the impact of bending and substrate materials on the design. It was observed that the TFTs show an increase in current up to 8% under tensile stress, while a decrease in current up to 4% under compressive stress on flexible substrate. Measurements indicate that the proposed design reduces the average total power consumption of the 3-to-8 decoder by 46.5% compared with state-of-the-art techniques under various conditions. © 2022 IEEE.

关键词： Thin film transistors

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Plant Disease Prediction with Fertilizer Recommendation Engine

Plant Disease Prediction with Fertilizer Recommendation Engi...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Kumar, Vipul Raghav, Sonia Rajpal, Manmohan Vaidya, Himadri Rawat, Ruchira Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382693

Agricultural productivity has a critical role in maintaining economies, especially in nations where a significant proportion of the population is engaged in farming. Plant diseases are a serious risk to agricultural productivity because they can result in losses that are both quantitative and qualitative. To identify plant illnesses, this study suggests a complete system that makes use of deep learning and image processing methods, particularly Convolutional Neural Networks (CNNs).mAn artifi-cial neural network with specific capabilities for processing grid-structured data-especially images-is called a convolutional neural network (CNN). CNNs are particularly good at extracting hierarchical features from visual input. They are made up of pooling layers for spatial dimension reduction, activation functions for adding nonlinearity, and convolutional layers that apply filters to extract local patterns. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Quantum and Classical Algorithms for Heart Disease Prediction

A Comparative Analysis of Quantum and Classical Algorithms f...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathya, S. Ramasamy, Madhumathi Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375442

Quantum Computing (QC) works based on the principle of quantum mechanics, which is different from traditional computers. Heart disease remains the leading cause of mortality worldwide and the development of advanced predictive models for early diagnosis is necessary. In this paper, the Support Vector Machine (SVM), Quantum Support Vector Machine (QSVM), and Variational Quantum Classifier (VQC) are used to predict heart disease. Among these algorithms, QSVM and VQC are used for heart disease prediction using QC principles. The SVM and QSVM achieves the accuracy of 80% and 69% and VQC achieves the highest accuracy of 86% with ZFeatureMap and RealAmplitude in 100 iterations. The FeatureMap and Ansatz are used for turning data into quantum states. The VQC employs a parameterized quantum circuit to minimize the classification error and optimization is done using gradient-free COBYLA. In addition to ZFeatureMap, other FeatureMaps like ZZFeatureMap and PauliFeatureMap are also performed in VQC. © 2024 IEEE.

关键词： Quantum computers

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Privacy-Preserving Energy Forecasting in Smart Homes using Federated Learning and SecureBoost 8

Privacy-Preserving Energy Forecasting in Smart Homes using F...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sadanand, Shwetha Vinothiyalakshmi, P. Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

The quick rise in smart home technologies calls for the development of advanced energy forecasting models that can accurately predict consumption patterns while maintaining user privacy. Existing energy forecasting methods follow a traditional centralized approach that compromises data privacy and scalability. This warrants the exploration of decentralized solutions like federated learning. This paper proposes a novel hybrid approach that integrates Convolutional Neural Networks (CNN), Federated Learning with Hierarchical Clustering (FL+HC) and SecureBoost to enhance the accuracy, efficiency and privacy of smart home energy forecasting systems. Experimental results demonstrated that the proposed method outperforms existing approaches in terms of accuracy, data privacy and computational efficiency, making it a robust solution for energy management in residences. © 2024 IEEE.

关键词： Smart homes

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Text classification using similarity measure and fuzzy function concept analysis

Text classification using similarity measure and fuzzy funct...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Selvi, S. Thilagavathy, A. Shobarani, P. Julian, Anitha Lakshmi Haritha, M. Therasa, P.R. Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

Text Classification during the retrieval of web documents is one of the most challenging tasks in the field of Query Processing. The query processor retrieves a large amount of documents in very less time that contain the query terms. Understanding and categorizing these texts is difficult in the field of Web content mining. This paper aims in categorizing the query terms and retrieving only relevant documents by performing an analysis of term pairs using Fuzzy Function Concept Lattice. In our contribution, terms are grouped using Fuzzy Function Concept analysis (FFCA). Our proposed method uses the conceptual relation for term classification, thereby reducing the time of retrieving relevant documents. Hence a user can avoid wasting his resource in retrieving irrelevant information during his Search process. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Semantics

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Next-Generation IoT Intrusion Detection: Leveraging Hybrid Deep Learning Algorithms for Enhanced Security 2

Next-Generation IoT Intrusion Detection: Leveraging Hybrid D...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Urvashi Agarwal, Parul Raghuvanshi, Kamlesh Kumar Ahmed, Jawed Department of Computer Science and Engineering Jamia Hamdard University Delhi India Department of Computer Science University of Delhi Delhi India

ISBN: (纸本)9798331530389

Wireless sensor node and DDoS (Distributed Denial of Service) is a major attack way that can exhaust the energy of a sensor, disrupt the communication traffic of an IoT network. We must be on the lookout for these attacks. With the quantity of sensor nodes, traditional methods face difficulty in detecting network traffic analysis requiring big data analysis. This paper is dedicated to Network Traffic analysis using Big Data environment and detecting attacks using Deep Learning. Implemented with PySpark in Google Colab, using Keras and Scikit-Learn built-in libraries. To be more specific, training and testing are conducted on the 'CICIoT2023'/'TON IoT' datasets, respectively. When we use a correlation feature reducer, it virtually considers the most significant features for inclusion and reduces all of our features. The first contains a hybrid deep leaning model combining one-dimensional EfficientDet and Gradient Boosting Machine (GBM), comparing it with ten other algorithms. It gen- erated 99.95% binary classification accuracy for the CICIoT2023 dataset and 99.75% for TON IoT data. © 2025 IEEE.

关键词： Network intrusion

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