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A Comprehensive Review for Optimizing the Design of Cluster Head Selection to Enhance the Network Lifetime in WSN

A Comprehensive Review for Optimizing the Design of Cluster ...

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

作者： Bhuvaneswari, S. Abirami, A. Suvetha Sri, R.P. Srinithy, R. Swathi, R. Easwari Engineering College Faculty of Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665492942

Large numbers of cooperative sensor nodes make up wireless sensor networks (WSNs). Sensors are the integral component in WSN IoT. Sensors separate and organize in the clusters by similar characteristics. A cluster head is in charge of gathering information from the nodes in every cluster and transmitting it to its base station (BS). Because every node are powered by batteries, lessens energy usage and elongating lifetime of network are crucial concerns for WSN. The primary goals of this analysis are to address issues that previous writers have not addressed and to provide beginning researchers with a fundamental understanding of how to choose cluster head (CH) based on various methodologies. To designate a node as CH, some enhancing parameters are chosen, such as residual energy, packet transmission rate, throughput, distance between BS and Cluster Node (CN), and betweenness centrality. Data packets are transferred using the optimized protocol to reduce delay time and increase transmission rate after many CH are chosen. Analysis of the many literatures was done. An Optimization Algorithm is needed to enhance network lifetime from this analysis. A comparison of various algorithm based on the features and difficulties from the analysis is done. There are many drawbacks, such as a cluster head is chosen at random, data transmission rate is not considered, no proper formation of clusters, importance for load and temperature is not given, stability is not taken into account. Thus, there is a need for optimized algorithm to overcome all the mentioned issues. © 2023 IEEE.

关键词： Packet networks

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Medicinal Plants Recognition Using Deep Learning

Medicinal Plants Recognition Using Deep Learning

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2023 International Conference on Multimedia Computing, Networking and applications, MCNA 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Tarawneh, Monther Sharieh, Ahmad Isra University Dept. of Computer Science Amman Jordan Isra University Dept. of Software Engineering Amman Jordan The University of Jordan Dept. of Computer Science Amman Jordan

ISBN: (纸本)9798350339277

The use of medicinal plants has been a longstanding practice in traditional medicine worldwide. Accurately identifying medicinal plants is crucial for determining their medicinal properties and potential applications. However, it can be a challenging task due to the complexity of their appearance. Variations in growth stage, lighting, and imaging conditions can make classification challenging, which limits the application of traditional methods for plant identification. This paper proposes a deep learning-based approach that uses a convolutional neural network (CNN) based on the VGG-16 model. With a dataset of 25,686 images, the CNN is capable of learning and representing complex features in images, enabling it to recognize and classify medicinal plants with high accuracy. The proposed approach can efficiently classify plants with different growth stages, lighting conditions, and imaging settings, providing a reliable tool for plant identification. We achieved an impressive recognition rate of 98%, demonstrating the feasibility of using deep learning techniques for accurate plant classification. The proposed approach has enormous potential for providing healthcare professionals and herbal medicine researchers with a reliable tool for identifying herbal plants. the study represents an essential advancement in the use of deep learning techniques for medicinal plant recognition, overcoming the challenges posed by their complex appearance. The proposed approach has farreaching implications and can significantly impact the field of herbal medicine research, enabling researchers and healthcare professionals to identify and classify medicinal plants more accurately © 2023 IEEE.

关键词： Classification (of information)

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Overview of Wireless Body Area Networks 7

Overview of Wireless Body Area Networks

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Kulkarni, Nitya N. Nagaraja, G.S. R v College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

Accessing everything remotely has become necessity as well as comfort of everyone in this technology advanced world. Monitoring health of a person remotely has also been the requirement with increase in ageing population. Wireless Body Area Networks (WBAN) have proved to be the most efficient solution to this problem. WBAN are short range communication network that include sensor nodes attached or implanted to the human body capturing the vital signs to further be used in decision making of the application. The applications of WBAN are widespread from remote health monitoring of patients in healthcare sector to assessing the fatigue of a soldier at the war front. Consequently, the procedures and protocols used by WBAN have to be efficient and reliable. However, there are several challenges faced in the successful real time implementation of WBAN as the devices used in it have limited energy and have to cope up with the movements and activities of a human body. With the intention to gain insights into these special networks, this manuscript presents a comprehensive overview of key principals, routing protocols and challenges of WBAN. Also, existing routing protocols are analysed with respect to energy consumption, packet delivery ratio, average delay, energy efficiency and average rise in temperature parameters. Analysis has led to the inference that there are very few routing protocols that can achieve efficient communication in WBAN. Thus, paving the path for further investigation to achieve a healthier world wherein people can proactively decide upon their health conditions. © 2023 IEEE.

关键词： Routing protocols

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An Android App Scanner for Detecting the Freshness and Age of Poultry Eggs using Image Processing

An Android App Scanner for Detecting the Freshness and Age o...

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Aniyan, Nisha Jacob, Pramod Mathew Pranav, P. Raj, Reshmi Ghosh, Gopika Suresh, Amitha Providence College of Engineering Dept. of Computer Science and Engineering Kerala Chengannur India

ISBN: (纸本)9798350397208

Grading food items based on their quality and freshness is essential for a healthy life. Quality and freshness are two incredible factors that people consider while buying food items. Quality prediction of food items like eggs is not possible by examining their appearance. Large cracks are the only defects that can be found through this observation. Traditional egg quality and freshness detection methods are destructive, time-consuming, inaccurate, and require constant monitoring. This paper proposes a model with an Android app scanner for detecting the freshness and age of poultry eggs using image processing techniques. This model can be used for checking the freshness and age of eggs by classifying them with various grades. The results of this model will be available in the Android app connected to this device. This model can be used for commercial as well as domestic usage. This model is non-destructive, time-saving, easy to use, flexible, and accurate. © 2023 IEEE.

关键词： Grading

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Predicting COVID-19 Fourth Wave Incidence in India Using Machine Learning Algorithms and SEIR Model 9

Predicting COVID-19 Fourth Wave Incidence in India Using Mac...

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9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2022

作者： Prakash, Satya Pathak, Pooja Jalal, Anand Singh GLA University Dept. of Computer Engineering and Applications Mathura India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

The world is witnessing COVID - 19 Pandemic for quite some time now. India has seen three waves of COVID-19 in the last 700 days. The curiosity still lies in the occurrence and timing of the fourth wave. The current study tries to solve this and predicts the COVID-19 daily incidence in India in the future. State-of-the-art methodologies both from Machine learning (LSTM, KNN, SVR, Random Forest, and Multi Linear Regressor) and Mathematical models (SEIR) have been tried out to train and predict the Daily New Cases of COVID19 in India. Further prediction for the next 200 days has been tried out using the trained models. As per the forecast from most of the models, it is evident that no fourth wave is going to be witnessed in India in the next 200 days. © 2022 IEEE.

关键词： COVID-19

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Phishing Perception and Prediction 4

Phishing Perception and Prediction

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4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Ashwitha Noble, P. Hubert Veyannie, V. Summia Parveen, H. Sri Eshwar College of Engineering Dept. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494144

A practice known as URL phishing involves cybercriminals creating fake websites in order to lure victims and steal sensitive information. The attacker disguises themselves in an email, instant message, or text message, pose as a reliable source to persuade the recipient to open it. Once the recipient clicks the link, they realize they were deceived into clicking a potentially dangerous link. In this case, ransomware can be set up on the recipient's PC using a malware that can be used to lock it down or even worse the private data can be released. In spite of the fact that fraudulent websites often resemble the real thing, checking for warning signals alone is not enough to prevent URL phishing. A study found that around 90% of the data breach is due to phishing. The variety of phishing schemes has expanded over the years and they may now be more dangerous than ever before. © 2023 IEEE.

关键词： Machine learning

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Fig Leaf Disease Prediction and Severity Estimation UsingDeep Learning

Fig Leaf Disease Prediction and Severity Estimation UsingDee...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Prasanna Kumar, K.R. Gunasekar, M. Logeswaran, K. Dhanya Sree, P. Malathi, P. Vignesh, V. Dept. of Computer Science and Design Kongu Engineering College Tamil Nadu Erode India School of Computer Science and Engineering VIT Tamil Nadu Vellore India Dept. of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331510299

Fig leaf diseases are among the major causes of crop loss. Better methods for detection and further increased accuracy in the estimation of the severity of the disease are demanded. In basis of what is previously researched, this project continues with not just the identification of the disease but also the identification of the level of infections. Having a large dataset of 1, 871 images that comprises 971 healthy samples and 900 infected samples, the analysis will be more elaborate to determine the disease in comparison with previous estimates. We have used two deep learning models - GoogLeNet and AlexNet with outstanding performance in preceding research to make reliable predictions on leaf diseases. The novel contribution of our work is estimating the disease severity with the help of Convolutional Neural Networks that could extract features and classify disease. For the estimation of the disease severity, we will use the VGG16 model to be able to categorize the infection severity into three classes- healthy, mild, and high. The diseases addressed in this paper are Cerotelium fici (fig rust) and Fig mosaic *** goal is to offer a comprehensive tool designed to identify diseases in fig crops and assess their severity for fig farmers and the researcher so that timely disease management strategies could be undertaken. This integrated approach not only aids in taking prompt intervention but also contributes significantly toward sustainable agriculture by reducing adverse impacts on fig leaf diseases. © 2024 IEEE.

关键词： AlexNet Deep Learning GoogLeNet Plant Disease prediction Severity Estimation VGG16

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Benchmark Study on YOLOv8 Variants in Localized Multiclass Fault Detection in PCBs 6

Benchmark Study on YOLOv8 Variants in Localized Multiclass F...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Najib, Taeef Muntasir, Fahim Wasi, Wasif Al Wazed Sidetrek Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering and Technology Dept. of Electrical and Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Detecting surface faults on PCBs is critical for ensuring device dependability and performance. Manual PCB inspection methods like X-ray imaging struggle to ensure quality control due to limitations in speed and defect characterization. However, advanced automated technologies, such as object detection models like YOLOv8, have emerged as a more efficient solution. This study focuses on evaluating the performance of different YOLOv8 variants in identifying and classifying various defects on PCBs. A benchmark of how each modification of the model performs detection and the factors behind were investigated here. It was determined that, YOLOv8 is sensitive to the amount of data it is being trained on, as the larger models tend to overlearn on smaller datasets. YOLOv8-m had the best performance, 97.1% precision, concerning the time taken and epoch count, over others. Large YOLOv8 variants, such as YOLOv8x, are also computationally expensive. The models were trained for 200 epochs with a patience value set at 50. Both increasing the number of data points and the resources will improve the performance and facilitate the overall training procedures. A significant gap in automated electronics production is addressed by this study: precise PCB fault localization for higher reliability of the product. © 2024 IEEE.

关键词： Fault detection

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Evaluation of Machine Learning Algorithms on Finding Drinking Water Quality Based on Feature Selection Methodologies 9

Evaluation of Machine Learning Algorithms on Finding Drinkin...

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

作者： Priya, K. Devi Sai, Sanga Monish Pagadala, Venu Gopal Reddy Kumar, Dasari Praveen Lakireddy Balireddy College of Engineering Dept. of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350397376

Production of wastage plastics, heavy metals, pesticides from the variety of the domestic and non-domestic organizations like industries, offices, people, hospitals, agriculture etc., nearer to the surface and groundwater bodies destroyed the natural resources useful to public health. Polluted water leads to the ill health to the people and effected with several diseases. Timely monitoring of quality of the water is required for the public health. Quality of the drinking water is based on several factors like pH value, hardness, solids, chloramines. Here, analyzed the factors that decide the quality of the water using 11 AI based classifiers with feature selection techniques and evaluated performance metrics. Results shows that ensemble based random forest classifier with gradient boost feature selector produced the highest accuracy to decide the potability of the drinking water. © 2023 IEEE.

关键词： Potable water

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Oral Cancer Detection using Deep Learning Techniques

Oral Cancer Detection using Deep Learning Techniques

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Tenali, Nagamani Desu, Vasavi Sripriya Boppa, Charishma Chowdary Chintala, Varshith Guntupalli, Bhavana Sr Gudlavalleru Engineering College Dept. of Computer Science and Engineering Gudlavalleru India

ISBN: (纸本)9798350397208

One of the most serious tumors that affects the oral cavity is oral cancer. Smoking cigarettes and increased tobacco use are the main risk factors for mouth cancer. When oral cancer is found in its early stages and treated successfully, many lives can be saved Histological analysis of an oral cavity tissue sample is the accepted method in medicine for identifying oral cancer. This method requires more time and is more invasive than obtaining a brush sample and then performing a cytological analysis. For a better prognosis, treatment plan, and chance of survival, early diagnosis is essential. Therefore, this paper suggests deep learning techniques to perform early detection of oral cancer and eventually leads to its prevention. Deep learning techniques enable early detection of disease to provide precision medicine. According to the recent research reports, this method has significantly advanced the extraction of data and interpretation of crucial information related to medical imaging. It has the potential to identify oral cancer with a cost-efficient, non-invasive, and effective method, having substantial clinical implications. © 2023 IEEE.

关键词： Tobacco

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