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An Intriguing and Predictive Machine Learning Method for the Detection and Identification of Diabetes 12th

An Intriguing and Predictive Machine Learning Method for the...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Srinivasula Reddy, A. Raja, Mandapati Tejaswini, V. Ramesh Kumar, R. Sri Vidya, G. Ragini, K. Department of Electrical and Electronics Engineering CMR Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering CMR Engineering College Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Master of Business Administration CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

According to WHO estimates from 2016, diabetes has been named one of the chronic illnesses with the greatest rate of growth and is the seventh largest cause of mortality. For a clinically significant outcome, early identification of diabetes is always preferred due to the relatively long asymptomatic phase. Diabetes is a leading cause of stroke, heart attacks, kidney failure, blindness, and amputation of lower limbs. If it is discovered in its early stages, they could be prevented. The digital age has made an enormous quantity of clinical data available, which has allowed deep learning algorithms to provide accurate medical diagnosis and prognosis. To train the machine to predict it, we employ the diabetes dataset. The dataset was studied using the random forest, deep neural network, and logistic regression algorithms, together with a DNN that contained embedded data for the categorical features. With a F1 rating of 1.0 on test data, the DNN with insertions correctly predicts the majority of test set examples, or almost 100% of the cases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Revolutionizing Plant Disease Management Through Image Processing technology

Revolutionizing Plant Disease Management Through Image Proce...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Kumar, Ankit Singhal, Manoj Singh, Kamred Udham Kumar, Lalan Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Information Technology India School of Computing Graphic Era Hill University Dehradun India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity. © 2023 IEEE.

关键词： Fuzzy logic

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An Effective Diagnosis of Alzheimer's Disease with the Use of Deep Learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks 6

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Venkateswaran, N. Ramprabu, J. Marimuthu, Ramaprabha Ramana, Nagella Venkata Kotha, Mahesh Mallala, Balasubbareddy Department of Master of Business Administration Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Management Studies Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle517325 India CMR Technical Campus Telangana Hyderabad501401 India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9798331522667

With the improvement in EV’s popularity, controlling the load demand of EVCS’s has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. The PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). The proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management. © 2025 IEEE.

关键词： Charging stations

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Proposing Machine Learning as the technology to Deal with the Liver Disease

Proposing Machine Learning as the technology to Deal with th...

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2022 OPJU International technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Yadav, Nageshwar Dev Singh, Vaibhav Kant Research & Development Product Specialist at Informatica Master Data Management Cloud Product Karnataka Bengaluru India A Central University School of Studies of Engineering and Technology Guru Ghasidas Vishwavidyalaya Department of Computer Science & Engineering Chhattisgarh Bilaspur India

ISBN: (纸本)9781665492942

In this work the Authors majorly focuses on the Disease that is Liver Disease and tried to work out a Strategy that could be handy to Deal with the Problem under Question. The implementation of the Work is solely in Python. The Major Python Libraries are used to come up with a Solution. There are various ML approaches Available to Handle the Problem. In our Implementation we made a Utilization of the Data-Set received from Kaggle Repository. The Implementation makes usage of the technology that is Support Vector Machine. We in our work made utilization of the Four Kernels Available for Implementation in Python Programming. In the Implementation Section we will observe the Classification Report Generated in the Coding done by the Authors. SVM is a very nice approach to take care of the Medical Issues. © 2023 IEEE.

关键词： Python

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IoT to Digital Twin: A Futuristic Smart Farming

IoT to Digital Twin: A Futuristic Smart Farming

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Akshay, M.J. Premasudha, B.G. Hegde, Seema B. Jawaharlal Nehru New College of Engineering Visvesvaraya Technological University Department of Information Science and Engineering Belagavi Shivamogga India Siddaganga Institute of Technology Visvesvaraya Technological University Department of Master of Computer Applications Belagavi Tumakuru India Siddaganga Institute of Technology Visvesvaraya Technological University Department of Electronics and Communication Belagavi Tumakuru India

ISBN: (纸本)9798350364040

The global population is rising and there is a need for smartness in agriculture. With the advancement in technology, it is possible to handle agricultural activities in a smart manner. The Internet of Things, Digital Twins, Artificial Intelligence, Machine Learning and Big data can be used for agriculture. Improving the farming efficiency and quality is a major challenge. Most of the day-to-day activities of the farms can be automated and monitored by the farmers. Moreover, there is a need for real-time analysis of the collected data, which can be more informative and realistic and helps in prediction of the future and decision making. The digital twins can be introduced to agriculture, which can help in various activities and making predictions. The digital twins and IoT can be a useful combination for agricultural activities. The various applications, parameters used, and the modeling adopted for digital twins for agriculture is highlighted in this article. © 2024 IEEE.

关键词： Decision making

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An Exploration of Deep Learning-Based Object Detection and Classification in Yakshagana Imagery with YOLOv3

An Exploration of Deep Learning-Based Object Detection and C...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Anantha Murthy Prarhwini Sanjeev Kulkarni Department of Master of Computer Applications Karkala and Research Scholar NMAM Institute of Technology Nitte (Deemed to be University) Institute of Computer Science and Information Science Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology Nitte (Deemed to be University) Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The Indian theatrical genre known as yakshagana has its roots in the coastal districts of Karnataka and parts of Kerala. With a distinct style and shape, it is a colorful fusion of dance, music, conversation, clothing, makeup, and stage tactics. This research uses YOLOv3, a cutting-edge object detection framework, to recognize people and crown jewelry in order to tackle the problem of identifying Yakshagana characters. Yakshagana, a traditional Indian theatrical style, is distinguished by its distinctive and ornate headdresses and costumes, which complicates automated identification. In order to do this, three classifications were taken into consideration: “person,” “yedekattu,” and “kireeta”. A carefully annotated dataset was used to train the model, which allowed it to capture all of the fine characteristics of these classifications. With a mean Average Precision (mAP) of 93.83%, our method showed impressive accuracy. With the improvement of picture identification and classification technologies, deep learning approaches have the ability to preserve and promote cultural heritage, as demonstrated by this high accuracy.

关键词： YOLO Deep learning Accuracy Art Clothing Real-time systems Cultural differences Character recognition Object recognition Global communication

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Improved AO based Classification Framework for Eye Diseases

Improved AO based Classification Framework for Eye Diseases

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Binju Saju V Asha Jincy C Mathew Sengcheng Sangma Shouvik Ghosh Sivaguru K Department of Computer Science - AI Engineering Adi Shankara Institute of Engineering & Technology Kalady Kerala Master of Computer Applications New Horizon College of Engineering Bengaluru India

A sensory organ that reacts to visible light, the human eye is a component of the sensory nervous system. A new approach is needed since, although physical detection of the eye condition is still a possibility, it is challenging and time-consuming. The study recommends a hybrid paradigm to classify various eye illnesses. Prior to further processing, pictures are first reduced in size, made grayscale, and noise-free using a combination of the Weiner and Median filters. Histogram equalization based on a Gaussian Mixture is also used to enhance pictures. Picture features are recovered with the aid of a thick convolutional neural network. An improved Aquila optimized ResNet101 model is used for classification. The effectiveness of the models is contrasted with that of related studies in the literature. A F1 score of 98.18%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 98.91%, and precision of 98% were attained by the suggested model.

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Long Short-Term Memory (LSTM) Networks for Stock Market Prediction 4

Long Short-Term Memory (LSTM) Networks for Stock Market Pred...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Dhabliya, Dharmesh Al-Jawahry, Hassan M. Sharma, Viklap Jayadurga, R. Jasmin, M. Vishwakarma Institute of Information Technology Department of Information Technology Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Master of Business Administration Chennai India New Prince Shri Bhavani College of Engineering and Technology India

ISBN: (纸本)9798350393248

The amazing capacity of Long Short-Term Memory (LSTM) networks to record complex temporal connections in sequential data has drawn a lot of attention in recent years. In the context of stock market prediction, a field where precise price movement forecasting is crucial, this study investigates the use of LSTM networks. We explore the basic ideas behind LSTM networks and explain how they may be used for time series forecasting because of their ability to analyze and understand sequential data. The first section of the study gives a brief introduction of LSTM architecture and its benefits over more conventional models. We talk about how important feature engineering and data preparation are to improving LSTMs' predictive power for stock market forecasting. We also explore how model assessment methods and hyperparameter adjustment may be used to maximize LSTM performance. In the study, we investigate several approaches, such as single-step and multi-step forecasting, for using LSTM networks in stock market prediction. We look at how external influences affect the performance of the model, including sentiment analysis of news and economic indicators. We also discuss potential remedies to the problems of overfitting and data scarcity that are inherent in LSTM-based stock market prediction. © 2023 IEEE.

关键词： Long short-term memory

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An Integrated Approach to Energy Harvesting and Power Management Systems: A Comprehensive Analysis

An Integrated Approach to Energy Harvesting and Power Manage...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Vikash Sharma, Bhavesh Kumar Singh, Teekam Varshney, Neeraj Agrawal, Dheeraj Graphic Era Hill University School of Computing Dehradun India Appventurez Mobitech Pvt Ltd Greater Noida201310 India Gl Bajaj College of Technology & Management Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Guru Ghasidas Vishwavidyalaya Department of Information Technology Bilaspur India

ISBN: (纸本)9798350382723

Implantable devices have significantly proved their reliability and effectiveness in the field of medicine as a result of their comprehensive assistance in a variety of areas that are related to the monitoring of patients and treatment in a variety of different ways. Furthermore, they contribute to the medical community's efforts to identify and prevent illnesses via the use of their services. The fact that the devices are powered by batteries, on the other hand, is a reliance that has a number of negative consequences. For instance, its depletion might result in hindrance during surgical procedures or leakage from the body. Implicit research has proposed the use of biocompatible harvesters that are capable of sustaining the device consumption by producing power. This methodology has been proposed as a means of reducing the size of the battery or, in some circumstances, completely eliminating its utilisation. In order to develop harvesting systems, a wide variety of biocompatible materials, including as piezoelectric and triboelectric materials, may be used. Ultrasonic transducers, electromagnet generators, and photovoltaic panels are just a few examples of the low-grade power sources that may be used to power implanted medical equipment via the utilisation of bioenvironmental energy harvesting. Ultrasonic transducers have been used in a variety of research projects as energy harvesters for the purpose of providing power to implanted medical devices. A piezoelectric oscillator was implanted under the goat's skin in order to collect the vibrational energy that was created by an ultrasonic transducer that was located outside of the power that was seen to be produced by the gearbox from the model that was provided. In their study, Tower et al. presented a system that has the potential to be utilised for the purpose of biological assessment and monitoring. The energy of an ultrasonic beam that is applied to the surface is converted into a high-frequency current by this

关键词： Energy harvesting

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