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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Lal N, Dayanand Hema, K. Spoorthi, T. Chegoni, Ramesh Madhura, K.G. Gitam School of Technology Gitam University Department of Computer Science & Engineering India Cmr Technical Campus Department of It Kandlakoya India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

Nerve pain, eye difficulties, skin issues, renal problems, heart disease, depression, and a host of other illnesses are all linked to diabetes if left untreated. In an effort to lengthen the human lifespan, scientists all around the world have applied a variety of methods for early disease prediction. Diseases of the eyes, heart, and kidneys are prioritised because they can cause serious consequences and even death if left untreated. After conducting an in-depth analysis of the strategies used by several researchers, the following models were constructed and tested using the datasets most frequently favoured by leading scientists throughout the world. Diabetic retinopathy (DR) is an eye condition, and this research seeks to construct a hybrid learning model for early prediction of DR. The proposed approach combines elements of both machine learning and Deep learning. Score Level Fusion is the last arbiter of the model's precision. Images from the first dataset of its sort reflecting the Indian population, the Indian Diabetic Retinopathy Imaging Dataset, were used to make the prediction. Predicting the DR and recommending the necessary treatment to patients is possible based on severity levels, which ultimately leads to the elimination of blindness. © 2023 IEEE.

关键词： Forecasting

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Exploratory Data Analysis and Classification of Employee Retention based on Logistic Regression Model 9

Exploratory Data Analysis and Classification of Employee Ret...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Bongale, Anupkumar M Dharrao, Deepak Urolagin, Siddhaling University Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Pune India University Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Dubai International Academic City Birla Institute of Technology & Science Department of Computer Science and Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350397376

Nowadays, a huge number of companies are using employee data records to understand the employee churn rate in the company. Companies use predictive analytics to predict the chances of an employee leaving the company. Organizations are effectively using Human Resource (HR) predictive analytics to increase revenue and turnover. This paper is about building a predictive model on the employee dataset to predict if an employee is likely to leave or continue service in the employed company. The steps carried out for visualization and Exploratory Data Analysis (EDA) has been highlighted in this paper by bringing out valuable insights from the employee dataset. Observations were made to the features that determine the chances of an employee leaving the company. For the prediction and classification of employee retention, a Logistic Regression (LR) model is applied to the dataset. The article focuses on the analysis of data and understanding the working of the LR model for predicting categorical features. The performance of the applied model was evaluated using a confusion matrix. The accuracy of the model was calculated from values in the confusion matrix. © 2023 IEEE.

关键词： Predictive analytics

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Smart Patrolling System Using machine learning 3

Smart Patrolling System Using Machine Learning

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Megala, P. Prabhu, R. Devi, V. Yamini, S. Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore641202 India Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi642003 India Hindustan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore641032 India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore641021 India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The implementation of Smart Patrolling System using IoT is to provide a safety to the people those who are present in the area by the help of fixing a Sound Sensor, Ultrasonic sensor, Raspberry pi, Node MCU, Night vision camera, GPS, GSM in street lights. The Smart Patrolling System using IoT continuously monitor the place and it gives the signal to the people those who are living in the place first then the control room. The proposed system can be used in Residential areas for giving better safety to the people. The proposed system gives a very most importance to Women and children safety. By the use of our proposed system several crime attacks can be found immediately and it can be avoided from crucial situations. © 2024 IEEE.

关键词： Sensor nodes

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A Novel AI-Assisted e-Consult Platform Integrating Deep learning for Enhanced Healthcare Access and Diagnostic Precision

A Novel AI-Assisted e-Consult Platform Integrating Deep Lear...

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Multi-Agent Systems for Collaborative intelligence (ICMSCI), International Conference on

作者： G Vijayasekaran Rajasree R S Parvez Alam Abhishek Suryavanshi Vinayak Ranjanagi Umar Farooq Mulla Dept of Computer Science and Engineering New Horizon College of Engineering Bengaluru Dept of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This study introduces the ‘AI-Assisted E-Consult’ initiative. The rapid digitization of healthcare is reshaping how medical services are delivered. This paper outlines the design of an AI-Driven E-Consult platform, serving as a digital interface that links patients, healthcare provider, and medical facilities. Utilizing state-of-the-art technologies such as AI and machine learning, the platform streamlines predictive health insights and appointment scheduling, thereby improving access to quality medical care. The platform employs Natural Language Processing (NLP) via the NLTK library and uses a Recurrent Neural Network (RNN) architecture trained on medical conversation datasets to power an AI chatbot. This chatbot analyzes symptoms and predicts potential health conditions, offering users preliminary insights into their health status. Furthermore, the system incorporates Haversine distance calculations to measure the geographical proximity between patients and doctors, enhancing logistical planning for consultations. To evaluate the platform's accuracy and performance, metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) will be used, ensuring precise predictions and operational effectiveness. The AI- Powered E-Consult platform is designed to eliminate healthcare barriers and maximize resource efficiency. Offering scalable, adaptable solutions tailored to the needs of contemporary healthcare, this platform represents a significant advancement toward a more accessible, efficient, and time-saving healthcare model, particularly for underserved populations.

关键词： Measurement Deep learning Recurrent neural networks Medical services Oral communication Chatbots Libraries Planning Medical diagnostic imaging Collaborative intelligence

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Meta Heuristic-Based Community Detection of Social Network Using Cuckoo with InfoMap Algorithm 1

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International Conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2022

作者： Devi, S. Rajalakshmi, M. Saranya, S. Shana, J. Department of Information Technology Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning Coimbatore Institute of Technology Coimbatore India

ISBN: (数字)9789811984976

ISBN: (纸本)9789811984969

Facebook and Twitter are one of the social networks for communicating our thoughts, feelings, opinions of current scenarios. By using this communication platform rapidly, people can share information. In the analysis of the social network, identifying such communities and sharing information are a challenging task. These fast shares of information create scalability, inaccurate, genuine issues. Many existing techniques can be used for the community detection of the social network. However, these techniques are ineffective and high time-consuming for quick detection. This paper proposed a novel metaheuristic technique-based community detection of the social network by using the hybrid of cuckoo search with InfoMap algorithm (CIMA). In this proposed work, CIMA is used for community detection. This proposed work increases the intracommunity of the social network and improves its performance in an accurate way by detecting the community of the social network. The accuracy rate for AKSHO got 48%, ant lion got 52%, lion optimization got 66%, fish swarm optimization algorithm got 78%, and our proposed work CIMA got 94%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Speech Emotion Recognition with Web Speech API 9

Speech Emotion Recognition with Web Speech API

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Anand, M. Vijay Rajasekaran, A. Rajendra Thilahar, C. Bharathiraja, N. Department of Computer Science and Engineering Chennai India Simats Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Chennai India VelTech MultiTech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350384208

Speech is one of the natural ways that we as humans communicate with one another. We rely on it so much that we have come to understand its significance in electronic communication, including text messages and emails, where we frequently utilize emojis to convey the emotions we are feeling at the time. Since emotions are a major component of communication, it is critical to identify and analyze them in the digital world of remote communication that we live in today. We use the SER technique because emotion detection is a difficult task because emotions are subjective. The goal of Speech Emotion Recognition (SER) is to identify the affective components of speech, independent of its semantic content. The goal of research on automatic emotion recognition systems is to develop effective, real-time techniques for identifying emotions in a range of application domains, such as caller-agent conversation analysis and interactive voice assistants. In this project, Web-Speech API - pre-existing software for analyzingthe nuances and annotations of speech - is used to analyze and identify the emotional aspects of *** we will be recognizing the words we speak using the scores which are predefined in the AFINN database. The outputwill be displayed in the webpage in the form of emojis as we speak in real-time. The approximate accuracy obtained in this project is around 80%-85%.Thus the sentiment in any content published as a speech or a live recording can be analysed through this project. © 2023 IEEE.

关键词： Speech recognition

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Particle Swarm Optimization for Efficient Brain Tumor Classification Using InceptionV3 Deep learning Model 9

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Akhila, P. Prabaharan, G. Pandiiyan, K. Swarna, S.L. Hussain, A. Sakthivelu, U. B V Raju Institute of Technology Department of Artificial Intelligence and Machine Learning Telangana Narsapur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Srm Arts and Science College Department of Computer Science with Artificial Intelligence Kattankulathur Chengalpattu India Sona College of Technology Department of Computer Science Science and Engineering Junction Main Road Tamil Nadu Salem India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350377972

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Secure Healthcare Monitoring: Algorithms and Techniques for Heart Disease Detection and Data Privacy

Secure Healthcare Monitoring: Algorithms and Techniques for ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Poonam Sujata V. Mallapur Department of Computer Science and Engineering Sharnbasva University Kalaburagi India Department of Artificial Intelligence & Machine Learning Sharnbasva University Kalaburagi India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

This survey offers the review of Healthcare Monitoring Systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope.

关键词： Heart Surveys Privacy Accuracy Reviews Scalability Medical services Real-time systems Monitoring Diseases

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A Review of machine learning and Deep learning algorithms for Chronic Kidney Disease Prediction 2

A Review of Machine Learning and Deep Learning algorithms fo...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Nagre, Pratik Verma, Prateek Kumar, Praveen Gavankar, Nitin L. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India Walchand College of Engineering Department of Computer Science and Engineering Maharashtra Sangli416415 India

ISBN: (纸本)9798331528713

Chronic Kidney Disease (CKD) is a common disease worldwide that is manifested by slow progressive loss of kidney function that can result in complications and kidney failure. That is why conventional approaches to CKD diagnosis and treatment have certain difficulties: late identification of the disease, inaccuracy of the diagnosis, and high costs for the therapy. The new approaches for solving these problems are opened with the help of machine learning (ML) and deep learning (DL). These technologies can help existing information sets in large volumes in diagnosing, predicting, and in early detection of diseases to enhance the well-being of the patients. However, the outcome comes with a handful of constraints, including the quality of the data used in developing the models, the ability to interpret results from the generated model, and the complexity of the computation process. The following section describes the various ML and DL algorithms used for CKD, their strengths, weaknesses, and the possibility of implementing these technologies in clinical settings for better CKD outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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Spiralizer Strategy with Wolf Pack Algorithm and Temporal Convolution Network – Gated Recurrent Unit for Coconut Crop Yield Prediction

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SN computer science 2024年第8期5卷 1108页

作者： Niranjan, S.J. Raviprakash, M.L. Babu, J. Ananda Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of Artificial Intelligence and Machine Learning Kalpataru Institute of Technology Tiptur Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of Information Science and Engineering Malnad College of Engineering Hassan Affiliated to Visvesvaraya Technological University Karnataka Belagavi India

The estimating coconut crop yield across large areas is crucial for ensuring food security and early to predicted. However, current spatial heterogeneity learning methods face limitations, such as selecting irrelevant features across regions and difficulties in achieve better performance in crop yields. In this research, Spiralizer Strategy with Wolf Pack Algorithm (SWPA) for feature selection and Temporal Convolution Network – Gated Recurrent Unit (GRU) for classification combined to performed efficiently and achieve better performance. The SWPA efficiently select relevant feature using a spiral search patter, leading to refined feature set that is fed into classification model. The TCN is designed to learn sequential data by applying convolutional layer that maintain the order of sequence. The GRU technique specifically designed to handling sequential data and long term dependencies, reducing the risk of overfitting. Initially,data acquisition and pre-processing involved Min–Max normalization, label encoder which efficiently scaling and categorized values into 0 and 1. The proposed method achiever better outcomes such as better MAE of 1.50, MAPE of 8.05, MSE of 7.23, RMSE of 0.35, R² of 0.654, respectively. The existing method such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are compared to proposed method. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coconut crop yield predication Convolutional neural network Long short-term memory Spiralizer strategy with wolf pack algorithm and temporal convolution network – gated recurrent unit

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