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A Comprehensive Evaluation of Radial Basis Function Networks for Feature Selection and Classification 2

A Comprehensive Evaluation of Radial Basis Function Networks...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Singh, Jagendra Nidhi Sharma, Preeti Kumar, Vinish Sharma, Meenakshi Singh, Ramendra School of Computer Science Engineering & Technology Bennett University Greater Noida India Ghaziabad India Ghaziabad India Inderprastha Engineering College Department of Electronics & Communication Engineering Ghaziabad India Ghaziabad India

ISBN: (纸本)9798331519056

This research investigates the application of Radial Basis Function Networks (RBFNs) to support feature selection and classification efforts. Four types of RBFN s were used including Multi-Layer RBFN, Gaussian RBFN, Thin-Plate Spline RBFN, and K-Means RBFN. Their utility was examined heavily in the course of research across different datasets. Such examination sought to determine how these four varieties of RBFNs can assist with feature selection and classification problems. The average experimental results indicated that Multi-Layer RBFN was highly robust as the accuracy scores varied from 0.79 to 0.92 with high precision, recall and F1 score values for 10 experiment trial. Both Gaussian RBFN and Thin-Plate Spline RBFN were highly effective in the regression task as the mean absolute error, Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values were low. Unlike both networks, it may be highly suitable for classification tasks as the accuracy and precision values were high. The presented research is of great importance since it extends the current knowledge of RBFN s' utility for feature selection and classification. Thus, this study will allow RBFN s to be considered as viable options for a broader range of real-life applications. In the future, it would be advisable to consider exploring the issue of interpretability enhancement, as well as the design of novel feature selection tools, or even the comparison of RBFN types. © 2024 IEEE.

关键词： Radial basis function networks

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Adaptive Intelligence System based on the Internet of Things for Patient Monitoring in Remote Area 12

Adaptive Intelligence System based on the Internet of Things...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Saranya, D. Chinnasamy, P. Nalinipriya, G. Jeipratha, P.N. Wise, D.C. Joy Winnie Kalaiarasi, A. Mailam Engineering College Department of Computer Science and Engineering Mailam India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Saveetha Engineering College Department of Information Technology Chennai India St.Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering India Sri Shakthi Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9781665480352

In the modern world, the mortality pace has increased and many people are still dying every day from several health complications. The proposed structure of an interconnected healthcare device is to regularly monitor the health condition of a patient as well as provide prompt notification to overcome such difficulties when they're out their regular lifestyle. To supply essential medical information for the patient in the early stages of an unusual event, the device has integrated sensors into it. When sensing a suspicious incident, by connecting with a personalized mobile app over the web, the device is to notify close relatives and physicians and it can send those emails and text messages. An alarm button allows patients to request assistance. The system tracks the physical activity, cardiac activity, and temperature of the patient to help the physician assess the illness. An electronic Sensor Module circuitry monitors the heart rate. The three-axis MEMS accelerometer sensor helps in the direction of identifying the daily body action of the monitored women. © 2022 IEEE.

关键词： Patient monitoring

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An Efficient Classification of Cassava Leaf Disease using VGG 16

An Efficient Classification of Cassava Leaf Disease using VG...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Soujanya, A. Cherukuvada, Srikanth Murugan, V. Ramya, Singaraju Agalya, K. Gayathri, S. Ibrahimpatnam India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India CARE College of Engineering Department of Artificial Intelligence and Data Science Thayanur Trichy India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Samayapuram Trichy India

ISBN: (纸本)9798350319200

Cassava is an essential food item in the tropics, it may origin enormous productivity decreases in cassava, leading to significant financial losses and a shortage of basic foods. Small-scale farmers grow the essential food and nutrition crop cassava because it can withstand harsh conditions. In many African nations, it provides a substantial source of carbohydrates. Leaf infections can occasionally harm cassava crops, limiting total productivity and farmers' income. In this study, Pre trained models are utilized for the determination of disease identification and classification on plant leaf issues. VGG 16 has been put into use, and its performance has been examined. An exploratory study is carried out using an online Cassava image collection, which contains pictures of Cassava leaves afflicted with five different disease categories. The vgg16 classifier that was suggested surpasses its competitors greatly and achieves an accuracy of 97.58%. The results of the test demonstrate that improving classification performance by using a database of images that is well balanced is possible. © 2023 IEEE.

关键词： Classification CNN Deep learning Transfer learning VGG16

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Image to Audio Conversion to Aid Visually Impaired People by CNN 4

Image to Audio Conversion to Aid Visually Impaired People by...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sivaganesan, D. Venkateshwaran, M. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798350300093

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired. © 2023 IEEE.

关键词： Convolutional neural networks

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Effective Summarization Techniques for Legal Document Analysis 4

Effective Summarization Techniques for Legal Document Analys...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gurrapu, Neelima Krishna, J S V Gopala Ram Pavan Kumar, V.T.M. Dhumpati, Raghu Mantena, Jeevana Sujitha Reddy Penubaka, Kiran Kumar Sr University Department of Computer Science and Artificial Intelligence Warangal India Sir C R Reddy College of Engineering Department of Cse Eluru India Kakaraparti Bhavanarayana College Department of Computer Science Vijayawada India Bahrain Polytechnic College Department of Cse Bahrain Srkr Engineering College Department of Cse Bhimavaram India Mlr Institute of Technology Department of CSE-AIML Hyderabad India

ISBN: (纸本)9798331523923

Summarizing legal texts is a critical task in natural language processing (NLP), which addresses the growing demands for efficient processing of large legal documents. Due to the complexity and structured nature of legal texts, the development of models capable of generating accurate and concise summaries presents significant challenges. This study examines the use of three state-of-the-art transformer-based models for summarizing legal texts. Each model is evaluated for its ability to capture nuanced semantics and preserve the contextual integrity of legal documents. The research uses a specialized dataset containing different types of legal documents, including summaries of case law, statutes and regulatory texts. Preprocessing steps include tokenization, normalization, and removal of non-informative content, ensuring that model inputs reflect the structure and verbosity contained in legal texts. Performance benchmarking is done using standard metrics such as ROUGE, BLEU, and BERT Score, along with domain-specific criteria to assess factual consistency and relevance. Experimental consequences exhibit that BART-base excels in generating fluent and human-like summaries, LED-base proves effective for longer documents due to its prolonged context-handling ability, and T5 showcases its versatility in domain edition. A specified analysis famous the strengths and limitations of every version in dealing with domain-unique demanding situations consisting of felony terminology, quotation references, and logical reasoning. © 2025 IEEE.

关键词： Abstractive Summarization BART BLEU CLS Tokenization Distil BART LED Legal Text Summarization ROUGE T5 Transformer Models

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SVM-based Predictive Modeling of Drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure 2

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ganesh, B. Jai Vijayan, P. Vaidehi, V. Murugan, S. Meenakshi, R. Rajmohan, M. R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Vinayaka Mission's Kirupananda Variyar Arts and Science College Department of Computer Science Tamil Nadu Salem India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. Data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety. © 2024 IEEE.

关键词： Internet of things

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Predictive Analysis of Kidney Diseases with Artificial Intelligence and Data science

Predictive Analysis of Kidney Diseases with Artificial Intel...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： G Eswar C Hariharan S Lavein V Akilandeswari Department of Computer Science and Engineering Velammal College of Engineering and Technology

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the Field of Medical diagnosis, nowadays there are many new methods are getting introduced each day and also gets advanced by many people around the world. The Kidney is one of the most vital organ of a Human body that helps a lot by filtering blood and throws the waste toxic contents through excretory organs. But the food habits and other bad habits can affect the process of Kidney and further even affects kidney. As a result our kidney can also suffer from various disease. Most vital Kidney diseases can include Chronic Kidney Disease(CKD), Diabetic Nephropathy at the 2nd stage of Diabetes, and still many more. In the process of predicting of these diseases, we can use machine learning algorithms like Random Forest with Decision Tree and Supervised Learning algorithms we can train a model that can use the previous datasets to clarify if the new person is diagnosed with the particular Kidney disease. The Random Forest algorithm provides around 90% accuracy that is better in prediction of results for the collected information. Using these techniques we can detect the problem at early stage which can give a greater chance to prevent it from getting worse.

关键词： Analytical models Machine learning algorithms Accuracy Predictive models Prediction algorithms Data models Diabetes Kidney Random forests Diseases

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Untrustworthy Mobile Node Isolation by Belief Factor in Autonomous Mobile Network 2

Untrustworthy Mobile Node Isolation by Belief Factor in Auto...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Dhivya, K. Suganya, J. Premkumar, K. Balasaranya, K. Dhanalakshmi, S. R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Mailam Engineering College Department of Electronics and Communication Engineering Tamil Nadu Vilupuram India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Ramapuram Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350347579

An Autonomous Mobile Network (AMN) is a wireless network in which no structure is accessible. AMN has significantly assisted in many applications recently. The AMN is often employed in such environments where humans do not exist. Therefore, each mobile node should be understood in case of a malfunction or connection failure between mobile nodes. Ant colony optimization (ACO) techniques have helped to develop AMN routing. An Untrustworthy mobile node Isolation by applying the Belief Factor (UIBF) system to detects the untrustworthy mobile nodes efficiently. The node energy, node cooperativeness, node packet losses, and node delay measures are combined to compute the belief factor. This mechanism uses the ACO algorithm to choose the optimal route from the sender to the recipient. Simulation results evaluate the UIBF approach to provide a better untrustworthy node detection ratio and raise the throughput with minimum network delay. © 2023 IEEE.

关键词： Ant colony optimization

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Automated Medical Record Summarization for Diagnosis and Training Doctors Using AI 1

Automated Medical Record Summarization for Diagnosis and Tra...

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1st IEEE International Conference on Green Industrial Electronics and Sustainable Technologies, GIEST 2024

作者： Singh, Kulvinder Gupta, Himanshu Singh, Balkar Wadhwa, Shivani Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Gujranwala Guru Nanak Khalsa College Post Graduate Department of Computer Science Punjab Ludhiana India

ISBN: (纸本)9798331540142

This paper investigates the potential of computers and artificial intelligence (AI) to fundamentally alter the landscape of the healthcare industry. It emphasizes how computers can refine the accuracy of diagnosing diseases by processing medical records and leveraging AI-based tools. The paper also sheds light on the profound impact that analyzing electronic health records (EHRs) can have on enhancing healthcare delivery and empowering patients. It also considers the use of internet search data as an innovative approach for the early detection of diseases, while also addressing the concern that access to health information online could lead to excessive worry among patients. The paper discusses advanced methodologies such as neural networks and deep learning and acknowledges the challenges they present, including the danger of overfitting and the necessity for effective data visualization. It also considers the revolutionary potential of EHRs in redefining healthcare delivery and in giving patients more control over their health. In addition, the paper looks at the significant changes that the adoption of electronic health records (EHRs) can bring to healthcare delivery and patient empowerment. © 2024 IEEE.

关键词： Electronic health record

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Advanced Grape Leaf Disease Detection using Neural Network 2

Advanced Grape Leaf Disease Detection using Neural Network

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Kavithamani, V. Renuka Devi, K. Pradeep, G. Janani, M. Balasamy, K. Rithani, B. Jai Shriram Engineering College Department of Ece Tirupur India Dr.Mahalingam College of Engineering and Technology Department of It Pollachi India Sri Krishna College of Engineering and Technology Department of M.Tech. Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641 407 India Bannari Amman Institute of Technology Dept of Ai & Ds Sathyamangalam India Bannari Amman Institute of Technology Department of Bio Technology Sathyamangalam India

ISBN: (纸本)9798350346640

In the realm of image categorization, the most recent iteration of convolutional neural networks, often known as CNNs, has produced quite excellent results. This study explores a novel method for the construction of a plant disease detection model that is based on the classification of leaf images and makes use of deep convolutional networks. The results of this research are shown below. Innovative ways of training and the methodology that is employed make it possible to put the system into operation quickly and without difficulty. The model that was constructed has the capability of distinguishing plant leaves from their surrounding environment, which enables it to identify various sorts of plant illnesses that may be seen on healthy leaves. This approach for identifying plant diseases has just been put forth for consideration for the first time. Beginning with the collection of images for the purpose of creating a database will be evaluated by agricultural specialists, every key step that is necessary for implementing this disease detection model is clearly outlined in this study. In order to perform disease prediction, a neural network has been used. The results of the experiments conducted on the newly constructed model has obtained a level of accuracy ranging from 85% to 95% for individual class assessments with an average of 96.3%. © 2023 IEEE.

关键词： Convolutional neural networks

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