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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Thongkong, Patchara Promphanich, Wiboon Udomluksananon, Patt Jeenmuang, Siwakorn Wiangtong, Theerayod Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Electrical Engineering Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350374605

The main insulation used in plastic underground cables is cross-linked polyethylene (XLPE). The XLPE underground cable which has been utilized in service for a long time, has experienced various stresses, or so-called TEAM stresses. Consequently, the XLPE insulation will be degraded over time and lead to insulation properties degradation. This paper represents the dielectric response analysis technique for characterizing the insulation condition of the service-aged XLPE medium voltage underground cable. The dielectric response analysis was performed on the 22 kV XLPE underground cables that have been in service for more than 10 years and were directly buried. The dielectric response analysis measurement consists of a combination of polarization and depolarization current (PDC) measurement in the time-domain and frequency-domain spectroscopy (FDS) measurement. After that, test results were analyzed to evaluate the insulation condition and compare it to the new 22 kV underground cable. The analyzed dielectric response analysis test results will be reported in this paper. © 2024 IEEE.

关键词： Cable sheathing

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Feature-enhanced deep learning technique with soft attention for MRI-based brain tumor classification

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International Journal of Information technology (Singapore) 2024年第3期16卷 1617-1626页

作者： Mohanty, Bipin Ch. Subudhi, P.K. Dash, Ratnakar Mohanty, Bidyadhar Department of Electronics and Communication Engineering BPUT Odisha Rourkela 769015 India Department of Electronics and Communication Engineering Nalanda Institute of Technology Odisha Bhubaneshwar 754005 India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela 769008 India

Brain tumor classification using Magnetic Resonance Imaging (MRI) is a pivotal area in medical diagnostics, with the potential to influence early detection and subsequent treatment strategies. Over the years, various machine learning and deep learning models have been proposed to enhance the accuracy of MRI-based tumor detection. In this evolving landscape, this paper introduces a distinctive deep-learning model that harnesses the power of a soft attention mechanism. We employ a meticulously designed Convolutional Neural Network (CNN) comprising four convolution layers. One of the significant innovations in our approach is the method of feature extraction. Instead of extracting features solely from the final layer, as is common in many models, our approach aggregates and combines features from all layers. This ensures that the vital characteristics intrinsic to each layer are not lost but rather amalgamated into a robust and comprehensive feature vector. The incorporation of a soft attention mechanism at the terminal stages ensures that the most salient and clinically relevant features are emphasized, enhancing classification accuracy. To validate the efficacy of our proposed model, we employed standard datasets for training and testing. A comparative analysis with existing state-of-the-art models affirms the superiority and potential of our approach in the realm of MRI-based brain tumor classification. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Brain tumor CNN Deep learning MRI Soft-attention

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Machine Learning Yield Forecast in the IoT System for Cultivating Accuracy 2nd

Machine Learning Yield Forecast in the IoT System for Cultiv...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Jayakarthik, R. Kumar, Santosh Goswami, Sohan Mahaveerakannan, R. Hafeez, Shaik Abdul Tamilnadu Chennai India Department of Computer Science ERA University Uttar Pradesh Lucknow India Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Panchrakhi India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9789819776023

Crop production is a very complicated attribute that depends on a number of variables, including genotype, environment, and their interactions. The functional relationship between yield and these interaction elements must be fundamentally understood for accurate yield prediction to be possible, and both large datasets and potent algorithms are needed to show this relationship. In order to accurately estimate crops, we applied cutting-edge machine learning techniques. Our ability to gather data for huge agricultural areas spread throughout far-flung regions of the globe is made possible by the Internet of Things’ architectural principles. So that we may make forecasts about crops, our machine learning system can use this data. Nitrogen, phosphorus, potassium, temperature, humidity, and rainfall all have a role in determining the best crop to grow. Recommendations are made in light of these factors. There are 2200 total occurrences in the data collection, and there are 8 associated attributes for each one. For each of the 8 possible characteristic combinations, around twenty-two distinct plant species are available as potential options. The most effective model can be produced by utilizing the supervised learning approach and few of the machine learning methods available in WEKA. As prospective possibilities for the methods of machine learning that would be used in the classification process, the decision table classifier and the multilayer perceptron rules-based classifier JRip were selected. The system’s design took into account both the growing Internet of Things and the essential measurements required for farming. The average weighted value of the Receiving Operator Attributes has been found to be 1, the performance measured by the classifiers that were chosen has a value of 98.2272%, and the maximum time needed to construct the model is 8.03 s. Employing machine learning in agriculture aims to increase the production and nutritional value of the plants generated

关键词： Adversarial machine learning

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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Real-Time Object Detection on Edge Devices Using Mobile Neural Networks 2

Real-Time Object Detection on Edge Devices Using Mobile Neur...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Jose Reena, K. Nigam, Charul Kirubasri, G. Jayachitra, S. Aeron, Anurag Suganthi, D. Vistas Department of Computer Science Tamilnadu Chennai India Institute of Information Technology and Management Department of It New Delhi India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India Dit University Dehradun School of Computing Department of Computer Science and Engineering Dehradun India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350306415

In an era when edge computing rapidly evolves, this study addresses the significant difficulty of real-time object identification on resource-constrained edge devices. We provide a unique neural network model for edge scenarios in this paper. Our model covers object recognition system accuracy, computational efficiency, and speed. Object detection methods that need processing are impractical for edge computing. This paper proposes a Mobile Neural Network tailored to the limitations. Pruning, which decreases model size by 30%, and quantization make the model efficient. On several edge devices, the model had an average accuracy of 92% on Dataset 1 and 89% on Dataset 2. The model's 40-millisecond inference time and 2.3-watt power consumption were impressive. It outperformed standard CNN models and edge-optimized algorithms like YOLOv3 and SSD MobileNet under identical conditions. This study shows that the proposed paradigm can revolutionise edge computing real-time object detection. The model is suitable for high-responsiveness, energy-efficient applications. © 2024 IEEE.

关键词： Edge Computing Mobile Neural Networks Optimization Techniques Real-Time Object Detection

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Age Based Hybrid Recommendation System using Machine Learning 3

Age Based Hybrid Recommendation System using Machine Learnin...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Karthikeya, Andhoju Kumar, Alle Aniesh Manicharan, Chennuri Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350303360

The recommendation system is one of the bigger developments brought about by machine learning in the machine-human interactions. In practically every area, including entertainment, e-commerce, farming, health, etc., recommendation systems are the primary players in delivering greater personalization. When it comes to content-based recommendation systems, there are many social platforms which uses recommendation system for providing improved personalization to their users and to access these social platforms the user need to have an account in that particular social platform. These platforms have different categories of content where not everyone is eligible to a particular content category. There are scenarios where different users may use the same account to access an entertainment application, or if a user inadvertently watched some inappropriate content, the recommendation system will make inappropriate recommendations, which can have an impact on the user's mental health-children. Therefore, after conducting research on the problem statement and reviewing various existing research works, machine learning model is proposed that offers recommendations to the user after determining the user's age and gender in order to deliver appropriate recommendations to the users. © 2023 IEEE.

关键词： Recommender systems

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VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance 7

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramitha Rajeswari, J. Chamanthi, Reddy Mary Posonia, A. Albert Mayan, J. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Chennai India

ISBN: (纸本)9798350359299

This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat Rate (HBR) and Blood Oxygen (SpO2) Sensor, DHT11 humidity and temperature sensor, OLED display, and gyroscope sensor, to provide a holistic approach to vital signs. The LM35 sensor is employed for accurate temperature measurement, providing crucial data for monitoring the patient's thermal status. This information is essential for detecting fever or abnormal temperature variations, aiding in the early identification of potential health issues. The HBR and SpO2 sensor plays a vital role in cardiovascular vital signs. The system can assess the patient's cardiac and respiratory well-being by measuring the heart rate and blood oxygen levels. Abnormalities in these parameters can trigger timely alerts, enabling swift medical intervention. The DHT11 sensor enhances the system's capabilities by monitoring ambient temperature and humidity. This additional environmental data contributes to a more comprehensive understanding of the patient's surroundings, ensuring that external factors are considered in the health assessment process. Integrating an OLED display serves as an intuitive user interface, providing real-time feedback on the measured parameters. This ensures that patients and healthcare providers can easily interpret and respond to the health data presented by the system. The display can showcase temperature, humidity, heart rate, blood oxygen levels, and other relevant information in a user-friendly format. Furthermore, the gyroscope sensor adds a layer of sophistication to vital sign surveillance by enabling the assessment of body movement and posture. This feature is particularly beneficial for patients with mobility issues or those prone to falls. The gyroscope data can be analyzed to detect sudden movements or abnormal postures, triggering alerts

关键词： Heart

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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An Effective Cyber Security Threat Detection in Smart Cities Using Dueling Deep Q Networks 4

An Effective Cyber Security Threat Detection in Smart Cities...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Stanly Jayaprakash, J. Kodati, Sarangam Kanchana, A. Al-Farouni, Mohammed Ramachandra, A.C. Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India Cvr College of Engineering Department of Information Technology Hyderabad India Mahendra Engineering College for Women Department of Computer Science and Engineering Namakkal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350352931

In recent days, smart city technologies are developing rapidly with the incorporation of IoT devices and AI-driven systems has modernized and thus facilitated for enhancing productivity, sustainability, and connectivity. The motive is to mitigate the rising cyber threats which are affecting the interrelated organizations of smart cities. Despite advancements in threat detection technologies, challenges persist, including real-time anomaly detection in vast data streams, managing resource constraints on IoT devices, and ensuring adaptability against evolving attack vectors. Therefore, this research suggests a novel approach by using Dueling Deep Q Networks (DDQN) method. Initially, the data is collected from the dataset and then Inter Quartile Range (IQR) is imposed at the preprocessing stage for detection of outliers. Then, extraction of optimal features is done by using Term Frequency-Inverse Document Frequency (TF-IDF) and Recursive Feature Elimination (RFE) which transforms the data into statistical vectors and eliminates the least significance feature respectively. Finally, the obtained optimal features are detected using DDQN method as it has the ability to detect effectively with respect to the state of action. The proposed DDQN has resulted with accuracy of 99.32% when compared with Improved Bacterial Foraging Optimization using optimum deep learning for Anomaly detection (IBFO-ODLAD). © 2024 IEEE.

关键词： Cyber attacks

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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

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computer Systems Science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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