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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Sathyamoorthy, Malathy Vanitha, C.N. Praveen Raja, S. Sharma, Ajeet Kumar Sharma, Bhisham Chowdhury, Subrata Kongu Engineering College Erode India GLA University Department of Computer Engineering and Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India

ISBN: (纸本)9798350346961

Dustbins are used for disposing the waste. When it gets overflowed, people used to throw the waste outside the dustbin until the garbage collector clears the dustbin. It makes the place surrounded by mosquitoes, bugs etc., and mainly affecting the environment and creates several unhygienic problems. To overcome this problem, we have proposed a system called 'Smart City Waste Management System using IOT'' to manage the waste. The sensors are placed in the top of normal garbage bins to measure the amount of trash and find the formation of any toxic gases. These measures make the sensors send the notifications in form of messages to waste management board. So that they can identify the location and garbage collector reach the place as soon as possible and clean the place. The proposed system is developed using Arduino, ultrasonic sensor, temperature sensor, gas sensor and RFID tag. It offers several benefits to the users such as Protect from bacteria and germs- motion sensor trash can protect you from harmful bacteria and germs and gives hygiene surroundings. Because of sensor technology, there is no need to touch the trash can for opening or closing.. © 2023 IEEE.

关键词： Bacteria

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The Outlook of Food Industry: Effectual Traceability Practice in Supply Chain Management using Internet of Things 14

The Outlook of Food Industry: Effectual Traceability Practic...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Gurumoorthi, E. Balamurugan, S. Maruthamuthu, R. Kumar, N. Naveen Department of IT CMR College of Engineering and Technology Telangana India School of Computers Madanapalle Institute of Technology and Science Andhra Pradesh India Dept of Computer Applications Madanapalle Institute of Technology and Science Andhra Pradesh India

Traceability can be used to improve processes, increase productivity, and ensure the quality and safety of perishable foods. But it's frequently seen as unnecessary red tape and a financial bur-den. Small and medium-sized businesses frequently exhibit this. Since they make up the majority of the food industry, the adoption of traceability systems is relatively sluggish and primarily done to meet legal requirements. In this paper, we propose effective food traceability techniques using the Internet of things and derive a solution for data communication. The improved Naïve Bayes Classifier is developed for food traceability using the enhanced prediction data. The purpose of the food traceability is used to maintain the computerization, reduced cost and reasonable system complexity. The most important parameter for this system is the food transportation from the manufacturer to the consumer. The Internet of Things is developed to connect the manufacturer to the consumer with an efficient transportation system. An economical solution is achieved using the IoT based food traceability. The Naïve Bayes Classifier is simulated and the investigational results proved that the proposed algorithm is more efficient for food traceability techniques compared to the SVM and KNN methods. © Grenze Scientific Society, 2023.

关键词： Internet of things

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AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction

AI for Smart Farming: Machine Learning Models for Precision ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Bhavana, Munugapati Nijhawan, Ginni Raju, Hemanth Bansal, Saloni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Dept. of Comp. Sc. & Engg. Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331512965

AI and other advanced technologies are gradually beginning to invade the agriculture sector to serve the increasing demand of the consumers for better methods of food production. A key component of precision agriculture, this work focuses on how to use machine learning models to predict crop yields with high accuracy. Specifically, the nature and volatility of the environmental context - such as the nature and quality of soils, as well as the climatic and water conditions - present a major challenge to the application of the conventional modeling approaches aimed at forecasting productive capacity in agriculture. Still, when comparing, the AI-driven models will allow sorting through much information gathered from different sources, including sensors, satellite imagery, and yield history. This work examines the differences of several machine learning models. Such models are the Random Forests, SVMs, and CNNs and other Deep Learning methodologies respectively. A quality model is evaluated based on scalability, computational performance, and prediction accuracy. Also in scope of the study, there is an opportunity to employ IoT together with data collected with remote sensing to make and provide realtime forecasts and evaluations. By the utilization of the results attained, it is established that the yields expected by the machine learning models are much more precise, which aids in enhancing agricultural decision-making concerning resource utilizing, crop handling, and risk minimization. The purpose of this study is focused on the promotion of the agricultural sustainability and production through the development of the innovative data driven farming practices. © 2025 IEEE.

关键词： Smart agriculture

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Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering

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SN computer science 2024年第8期5卷 1022页

作者： S, Kavitha Rao, Deepak Dasaratha Jain, Anupriya Sharma, Seema Pandit, Shraddha V. Pandey, Rajeev Christ University Karanataka Banglore India Indian Institute of Technology Patna India School of Computer Applications Manav Rachna International Institute of Research and Studies(MRIIRS) Haryana Faridabad India Department of Artificial Intelligence and Data Science PES Modern College of Engineering Pune Shivajinagar 411005 India Department of Computer Science & amp Engineering University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal Madhya Pradesh India

Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) point—indicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： FLECH IoT LEACH WSN

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Smart Piscis Monitoring System Using IoT 6

Smart Piscis Monitoring System Using IoT

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Sathyamoorthy, Malathy Vanitha, C.N. Kaliswary, K. Kumar, Rakesh Sharma, Bhisham Chowdhury, Subrata Kongu Engineering College Erode India GLA University Department of Computer Engineering and Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India

ISBN: (纸本)9798350346961

In the world, there are many species gifted by the nature. One of the most important and beautiful creatures in the world is fish species. It creates the marine world and plays a main role in aquaculture. In this modern generation, many lives going on endangered species, due to inadequate feeding of food also not having the proper maintenance to enhance the species. This causes the food chain to be affected. So, this can become one of the major environmental issues. Using IOT sensors we can closely monitor marine species. To rectify these difficulties, sensors are replaced in the place of humans, so that an immediate solution is provided for the problem. By using the PH water sensor is familiar with sensing the water quality system. Temperature sensor will find the water temperature to achieve a good environment. To feed the fish we use the feeding valve is rotated by a servo motor and it is automated (on or off) using an Android device where data are read by Node MCU. It helps to improve the efficiency of the aqua species' health. An IOT makes humans free from the workload and provides a good environment for the fish. Tools used here such as Android device, Node MCU, PH sensor, Servo motor, and temperature sensor. The paper's goal is to keep track of the factors required for aquaculture's survival.. © 2023 IEEE.

关键词： Temperature sensors

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Predicting Rainfall Using an Artificial Neural Network-Based Model 6

Predicting Rainfall Using an Artificial Neural Network-Based...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Shukla, Aasheesh Sharma, Manish Tiwari, Kripanshu Vani, V Divya Kumar, Naveen Pooja GLA University Computer Engineering and Applications Uttar Pradesh Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Rainfall estimation poses a significant and intricate environmental challenge in today's context. Achieving accurate forecasts often necessitates the deployment of sophisticated machine models and simulations because weather patterns and rainfall dynamics are typically highly nonlinear and constantly changing. In India, where agriculture serves as the cornerstone of the economy, special emphasis is placed on the relationship between agricultural productivity and rainfall. Farmers rely on rainfall forecasts to gauge potential crop yields. Precipitation forecasting leverages scientific and technological advancements to assess atmospheric conditions. Accurate monitoring of rainfall is pivotal for optimal crop growth, efficient water resource management, and strategic planning of water systems. To emulate the behavior of such intricate nonlinear systems, artificial neural networks (ANN s) have proven to be a valuable tool. Over the years, numerous researchers in this field have effectively harnessed ANN s. This research highlights several approaches employed by various researchers to predict rainfall using ANN s. This proposal underscores that forecasts based on ANN s outperform those derived from conventional mathematical and numerical methods, offering greater precision in rainfall predictions. © 2023 IEEE.

关键词： Accuracy Agriculture Artificial Neural Network Crop Productivity Prediction Rainfall

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Next-Gen Telehealth Ecosystems Using IoT for Virtual Consultations and Support 10

Next-Gen Telehealth Ecosystems Using IoT for Virtual Consult...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kathija Nasreen, A. Priya, S. Gupta, Nukala Sujata Shoba, L.K. Mohankumar, N. Arunkumar, S. Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Computer Applications Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The research discussed focuses on improving virtual medical consultations and support using Internet of Things (IoT) technologies in telehealth services. It is predicted that IoT devices can assist with creating a new telehealth environment where patients and doctors can communicate easily for remote monitoring, diagnosis, and treatment. The main objective of this unique approach is to expand access to healthcare, especially in poor and rural areas. Connected medical equipment, smart sensors, and wearable technology all play an important role in this system. These IoT components enable gathering real-time and complete patient data, helping doctors make better, more individualized choices and care plans. Data safety, privacy, and system compatibility are some areas where this integration hits a snag. Sensitive patient data and the integrity of the IoT-enabled systems need robust safety measures. These IoT improvements in telemedicine have had a revolutionary effect. Proactive interventions, continuous health monitoring, and individualized care are ways patients might benefit from improved medical care. Virtual consultations are increasingly used in the medical field, and a more connected and data-driven framework for the patient experience is emerging. © 2024 IEEE.

关键词： Internet of things

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Client Segmentation and Customization in E-Commerce: applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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DLIoMT: Deep Learning Approaches for IoMT-Overview, challenges and the future

DLIoMT: Deep Learning Approaches for IoMT-Overview, challeng...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Harshni, V. Singh, Satyendr Indhumathi, R. Kumar, Neelam Sanjeev Chowdhury, Subrata Patra, Sudhansu Shekhar Srm Institute of Science and Technology Department of Bme Ramapuram campus Chennai India Bml Munjal University Computer Science & Engg Department Gurugram India Vadapalani Campus Chennai India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Deemed to be University School of Compter Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331513894

An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine learning, conventional neural networks, recognition, and fuzzy logic are a few of the subsets of AI with unique capabilities and functions that can enhance modern medical science. Such innovations simplify human interference in clinical diagnostic procedures, decision making, and medical imaging. To improve human health, connected biomedical network devices and software applications are interconnected to form the Internet of Medical Things (IoMT), the next generation of bioanalytical tools. This review paper provides in-depth analysis of the IoMT encasing its definition, technologies, applications, challenges, and future prospects. © 2025 IEEE.

关键词： Electronic health record

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JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep Learning

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SN computer science 2023年第6期4卷 840页

作者： Verma, Aryan Gupta, Nidhi Bhatele, Pushpraj Khanna, Pritee Department of Computer Science and Engineering National Institute of Technology HP Hamirpur 177005 India Department of Computer Applications National Institute of Technology HR Kurukshetra 136119 India NSCB Medical College MP and MRI Scan Centre Nagpur Road MP Jabalpur 482003 India Computer Science and Engineering Discipline PDPM Indian Institute of Information Technology Design and Manufacturing MP Jabalpur 482005 India

The growth of abnormal cells in the brain gives rise to a deadly form of cancer known as a brain tumor. The mass of brain tumors proliferates and rises very fast, and if not appropriately treated, the patient’s survival rate is less or can rapidly lead to death. A very exigent task for radiologists is early brain tumor detection which may help to evaluate the tumor and plan treatment for an effective prognosis. In this work, we used deep learning to detect and localize brain tumors from MRI slices. After the detection of the tumor, we generate a color heatmap to show the tumor’s location, severity, and possible affected regions. Our novel image processing algorithm swiftly segments the skull region from the background of MR images, augmented over heatmap for better observation. We analyzed the performance of VGG 16, Resnet 50, and EfficientNet deep learning architectures. Transfer learning on Resnet 50 model with custom top layers achieved excellent performance with 99.37% accuracy, 99.68% precision, and 99.69% specificity, which is better than most of the works in brain tumor detection using machine learning. Expert radiologists critically examined our model outcomes in detail. They found our method effective to triage people with brain tumors and helpful in decision making for the clinicians by severity indication through heatmaps. They observed the prime cause for false positives to be unbalanced contrast in some MR images. Through this work, we also made the novel dataset named as JMCD (Jabalpur Medical College Dataset) publicly available for research which contains all four MR pulse sequences annotated by expert radiologists for 140 patients. Each patient has 15–20 sequences containing 250 images. Through this work and accompanying data, we aim to give better methods for brain tumor detection and help research in the same field. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Brain tumor Deep learning Glioma Medical image analysis MRI

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