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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kumar V, Sunil Jyothi, A.P. Nagamani, H.S. Sumanth, S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Bengaluru India Department of Computer Science Bengaluru India Department of Computer Science and Computer Applications Kolar India

ISBN: (纸本)9798350382952

Mangoes are one of the most popular fruits worldwide due to their taste and nutrition. Plant diseases may reduce plant quantity and quality. Disease management to improve crop quality and productivity requires early plant illness detection. Automation of plant disease detection reduces large farm surveillance. Since plants feed us, leaf problems must be diagnosed quickly and accurately. Our advanced learning method automatically detects leaf disease in several mango plant varieties. This study creates the RAM-EfficientNet, a novel illness classification network. The RAM block in EfficientNet extracts additional features and handles slight leaf lesion changes. Transfer learning was used to train EfficientNet on ImageNet to overcome the mango leaf disease sample size issue. The Cat Swarm Updated Black Widow Model (CSUBW), a hybrid optimization model, changes RAM-EfficientNet's weights to improve classification accuracy. Black Widow Optimization Algorithm (BWO) and Cat Swarm Optimization Algorithm (CSO) are combined to create the hybrid optimization model. The model's performance is then assessed to confirm its efficacy. © 2024 IEEE.

关键词： Fruits

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Personalized Care for Multiple Sclerosis with IoT Wearables and Deep Learning

Personalized Care for Multiple Sclerosis with IoT Wearables ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Vetriselvi, D. Sampathrajan, Rajeshkumar Deepa, A. Sujatha, V. Deepa, P. Pandi Murugan, S. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Agni College of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness. © 2024 IEEE.

关键词： Patient treatment

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A Centralized Blockchain Architecture with Optimum Sharing 8

A Centralized Blockchain Architecture with Optimum Sharing

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Ramasamy, Viswanathan Padmapriya, S. Kavitha, G. Lekha, D. Kl Univrsity Department of Computer Science and Engineering Andhra Pradesh Gundoore India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Namakkal India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396638

The monolithic network architectures are used in Bitcoin and other big cryptos. Different hierarchical ways to protect the decentralized blockchain have been discussed to improve its usability. However, no debate in the literature on how an appropriate shard size can be calculated to optimize the output and how deceptive or unreliable nodes can affect the optimum shard size range. This study includes an exchange and authentication scheme for hierarchical blockchain architecture in order to fix these issues. The hierarchy splits the network nodes into many disjoint fragments, with most transfers unregulated between these shards. Taking into account the performance and precision of the transaction validation in the case of malicious or defective nodes, the optimum shard size is calculated. It ensures that most of the good shards are available with the right scale, according to the highest acceptable threshold of defective nodes. It also offers a method to distinguish damaged shards by overlapping a tiny proportion of purchases across all shields and discarding all transactions. The theoretical efficiency analysis and the influence of network sharing on performance were based on experimental findings conducted on Amazon EC-2 nodes. © 2023 IEEE.

关键词： Authentication

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Smart irrigation system using IoT and machine learning methods 5

Smart irrigation system using IoT and machine learning metho...

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Esmail, Ahmed A. Ibrahim, Manar A. Abdallah, Sayed M. Radwan, Ahmed E. Elsonbaty, Haya K. Elsayed, Mohamed A. Elnakeib, Noure A. Dawoud, Merit Samir El-Ghamry, Amir Fouad, Khaled M. Moawad, Ibrahim F. New Mansoura University Artificial Intelligence Science Faculty of Computer Science and Engineering Dakhlia Egypt Mansoura University Faculty of Computer Science and Engineering New Mansoura University Information Technology Faculty of Computer and Information Sciences Egypt Mansoura University Faculty of Computer Science and Engineering New Mansoura University Faculty of Computer and Information Sciences Egypt University of Hertfordshire Hosted by Global Academic Foundation School of Engineering and Computer Science Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Ain Shams University Cairo Information Systems Faculty of Computer and Information Sciences Egypt

ISBN: (纸本)9798350381030

Water loss and improper scheduling are problems with traditional irrigation techniques, making it difficult to meet the growing demand for food production while also preserving precious water resources. To address these challenges, this paper introduces a cutting-edge smart irrigation system that leverages the power of the Internet of Things (IoT), data analysis, and machine learning to determine the most efficient way to apply and schedule water for irrigation. The cornerstone of this innovative system is the utilization of a carefully curated dataset, "Crop Irrigation Scheduling,"sourced from Kaggle. This dataset comprises six crucial attributes: Crop Type, Crop Days, Soil Moisture, Temperature, Humidity, and Irrigation. These attributes, described in detail in the dataset's metadata, provide the foundational information required for precise irrigation management. The system operates seamlessly by deploying IoT sensors to collect real-time data from the field. This data is then preprocessed to ensure its quality and consistency. Subsequently, a machine learning model is trained using this dataset to make intelligent irrigation decisions in real time. The model's predictions are seamlessly integrated with control mechanisms that govern the irrigation process. Moreover, the system offers an intuitive API for easy data access and management, allowing agricultural professionals to monitor and adjust irrigation strategies effortlessly. Experimental findings validate the system's utility, with particular emphasis on the decision tree model, which demonstrates the best balance of accuracy, precision, recall, and F1 score. This system maximizes crop yield and minimizes water waste, contributing to a more sustainable and productive agricultural ecosystem. © 2023 IEEE.

关键词： Internet of things

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Exploring the Use of Machine Learning Techniques for Enhancing Real-Time data Access 3

Exploring the Use of Machine Learning Techniques for Enhanci...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rampal, Sourav Shreenidhi, H.S. Shrivastava, Manish Singh, Akhilendra Pratap Reena, R. Kumar, Santosh D. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Actual-time statistics to date has updated, increasingly vital as the quantity of virtual information maintains updated growth. Effective choice-making depends on the capability to update access and analyze records. Device up-to-date techniques provide the capacity up-to-date enhance actual-time statistics up-to-date by using offering insights from disparate pieces of information. This paper addresses the potential benefits of using a system with date knowledge and updated, up-to-date strategies to broaden extra clever statistics up to date updated systems. It summarizes the contemporary nation of the art and highlights studies in machine learning and up-to-date imaginative and prescient real-time information up-to-date. It additionally discusses present-day challenges and shows viable instructions for future studies. The studies network up-to-date recall how gadgets are up to date know that updated strategies may be used to facilitate real-time get entry updated the maximum precious pieces of facts. © 2023 IEEE.

关键词： Machine learning

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Intelligent Crop Recommender System for Yield Prediction Using Machine Learning Strategy

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 979-987页

作者： Maheswary, Atchukatla Nagendram, Sanam Kiran, Kasi Uday Ahammad, Shaik Hasane Priya, Putcha Poorna Hossain, Md. Amzad Rashed, Ahmed Nabih Zaki Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering & ampTechnology Thirupatchur India Department of Artificial Intelligence KKR & ampKSR Institute of Technology and Sciences Guntur India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur522502 India Department of Electronics and Communication Engineering Dadi Institute of Engineering and Technology College Anakapalle India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Saveetha University Tamilnadu Chennai India

For most developed nations, agriculture is a significant economic force. The realm of contemporary agriculture is consistently growing with evolving farming techniques and agricultural innovations. Farmers face challenges in keeping pace with the evolving demands of the planet and meeting the requirements of profitable initiatives, characters, and various other stakeholders. Climate change brought on by industry emissions and soil erosion, soil's nutrient deficiency due to mineral's absence, which results in reduced crop growth, and the cultivation of the same crops repeatedly without trying out new varieties are a few of the difficulties farmers face. Without considering the lower quality or quantity, they arbitrarily infuse fertilizers. Using two separate metrics, entropy and Gini indexes, the study analyzes well-known procedures with K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classifier practices. Moreover, the precision under the agriculture paradigm, particularly "crop recommender systems," includes these methods. Based on the outcomes, the random forest strategy outperforms the other approaches to model accuracy and reliability. © The Institution of Engineers (India) 2024.

关键词： Fertilizers

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Predicting Pedestrian Movement in Unsignalized Crossings: A Contextual Cue-Based Approach

Predicting Pedestrian Movement in Unsignalized Crossings: A ...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Kaliprasana Muduli Vikas Sahu Indrajit Ghosh Civil Engineering Department Indian Institute of Technology Roorkee Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Civil Engineering Department & Joint Faculty Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India

To ensure safe and secure coexistence between pedestrians and autonomous vehicles (AVs), AVs must be able to anticipate pedestrian behavior and respond to it. This research gathers video data from real traffic scenes to predict pedestrian crossing intentions at unsignalized crossings. Computer vision techniques such as YOLOv4, Deep SORT, and perspective transformation are employed for road user detection, tracking, and mapping image coordinates to world coordinates to prepare trajectory datasets. Using trajectory data, several features influencing pedestrian intention like walking speed, location in the road environment, count and direction of approaching traffic, speed and type of closest approaching vehicle upstream, etc., are extracted. The dataset for this study was obtained by analyzing 1,411 pedestrians, resulting in 223,136 samples. To predict pedestrian crossing intentions, LSTM and Bi-LSTM with an attention mechanism model were built and trained to anticipate the pedestrian crossing intention at unsignalized crossing. The proposed model successfully combined the characteristics and surrounding dynamics of pedestrians to produce accurate predictions, Bi-LSTM with an attention mechanism outperformed LSTM, achieving an AUC of 95.3%, 91.1%, 89.2%, 87.5%, and 84% on the testing dataset at unsignalized crossing on the 0.6 sec, 1.2 s, 1.8 s, 2.4 s, and 3 s time horizons. These outcomes can be used to improve Connected and Autonomous Vehicle (CAV) technologies, infrastructure-to-vehicle (I2V) connectivity, and driver assistance systems to enhance pedestrian safety while navigating through pedestrian crosswalks.

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness data 5

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Clarinda, K. Deepa, N. Dhamotharan, R. Krishnan, R. Santhana Raj, J. Relin Fransis Anchitaalagammai, J.V. SCAD College of Engineering and Technology Department of Management Studies Cheranmahadevi India PSNA College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331530013

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of artificial Neural Networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management. © 2024 IEEE.

关键词： Adversarial machine learning

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Conversational Symptom Checker Chatbot for Disease Prediction and Suggesting Nearby Hospitals using Machine Learning and Location Services

Conversational Symptom Checker Chatbot for Disease Predictio...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Rushikesh Burle Swapnil Gundewar Sanskruti Gaurkhede Bhushan Fulkar Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Computer Science And Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

A chatbot, sometimes known as a conversational agent, is software that uses natural language to interact with people. Travelling to and from a hospital for treatment takes a lot of time for people with hectic schedules, which makes many of them disregard their health issues. People frequently postpone going to the hospital for small problems that might develop into serious illnesses. For these people, interacting with chatbots to get helpful medical information is a cost-effective, time-efficient, and convenient option. This paper suggests a framework for a machine learning-based chatbot that combines natural language, machine learning, and statistical analysis. gaining knowledge of the technologies in a Spark cluster to help create an intelligent chatbot. In this work, we conducted a thorough analysis of current literature, looking at a large number of chatbot-related papers over the last five years. Next, we introduced many related tasks to our chatbot, which uses user symptoms to predict diseases and, depending on the user’s location, recommends hospitals in the area. Users will be able to easily engage with the chatbot using the system’s online interface.

关键词： Recurrent neural networks Hospitals Statistical analysis Machine learning Chatbots Tokenization Sparks Medical diagnostic imaging Long short term memory Diseases

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Electromyography (EMG) Signal Acquisition for Exoskeleton Control: Enhancing Precision in Human-Machine Interaction

Electromyography (EMG) Signal Acquisition for Exoskeleton Co...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Rajendra Thilahar, C. Manoharan, Madhanagopal Sudalai, Perumal Kumaravelu, R. Sadayan, Rajendra Boopathy Kaliaraj, Gobi Saravanan Saveetha Engineering college Department of Computer Science and Engineering Chennai India Makerspace Center of Excellence for Additive Manufacturing Sathyabama Institute of Science and Technology Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Mechanical Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India College of Engineering Guindy Anna University Department of Mechanical Engineering Chennai India Center of Excellence for Additive Manufacturing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331528126

Electromyography (EMG) signals, which measure the electrical activity of muscles during contraction, deal with the hope of controlling the actions of the exoskeleton. This paper exposes the acquisition and application of EMG signals for precise and responsive control of exoskeletons. Moreover, the natural neuromuscular signals generated by the user aim to create a seamless interface between the human body and the assistive device, enhancing functionality and user experience. The EMG signals are obtained by placing the electrodes in the respective area of our body, filtering the data, signal amplification, and using noise reduction techniques. Furthermore, EMG data is processed for noise removal, where artefacts ensure the clarity and reliability of the acquired signals. Those signals were used to develop a control algorithm to translate muscle activity into corresponding exoskeleton movements. The challenges and limitations associated with EMG-based controls are signal variability, cross-talk, and calibration. Finally, this paper presents results, which demonstrate that EMG-based control offers a high degree of accuracy and responsiveness, especially requiring to control the fine motor rotation and varying force levels. This approach not only improves the usability of exoskeletons for individuals with mobility impairments but also has certain potential applications like EMG-controlled exoskeletons in various domains, including rehabilitation, assistive technology, various military settings and human-machine interfaces. EMG-driven systems to revolutionize the field of wearable robotics, paving the way for more intuitive and effective human-machine interfaces. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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