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Design and Development of Smart Beach Debris Collection and Segregation System 2

Design and Development of Smart Beach Debris Collection and ...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Pavithra, S. Parvathi, R. Shah, Hrut Rajesh Eshan, S.V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science Engineering VIT University Chennai Campus Chennai India

ISBN: (纸本)9798331504106

Marine debris has become a global problem, concerning all marine ecosystems, and humans alike. It's known to cause severe damage to the marine and coastal habitats, since a part of it washes up along the shores and reefs, and the remaining is swept off the coast leading to the sinking and persisting of the debris in the form of sediments for years. This submerged marine garbage contains items such as microplastics, large plastic fragments, fishing strings/nets, organic waste, and a lot more. Accumulation of such debris causes damage to the ecosystem, damaging the livelihood of all effected aquatic species, and also the people around these waters, as the microplastics in such waters is ingested by the fish, eventually making its way up the food chain. The motivation for building this project arose from the need to address the adverse effects of introducing trash into our marine ecosystems around the globe, and its detrimental impact on the ecosystem and beaches. To overcome these issues, we introduce the beach cleaning robot, an innovation in artificial intelligence and robotics. This bot improves upon an existing idea Bebot, thriving to provide better results in a more cost effective and easy-to-use manner. By using mapping technology and sensor detection techniques, this approach aims to pick up the trash from our beaches and coasts before it is washed out by the tides, helping us to cut down the amount of marine debris that makes it into the oceans from these sources. The final product will be a machine capable of distinguishing various types of waste that it collects, and segregating it into metallic and not metallic, cleaning the beach it is deployed at autonomously and providing long-term sustainability against beach debris. We envision a world where deploying such machines will not only result in cleaner beaches but also preserve the marine life in the surrounding area, allowing future generations to experience the beauty we enjoy right now. ©2024 IEEE.

关键词： Debris

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A Comprehensive Study on the Role of Artificial Intelligence in Animal Husbandry

A Comprehensive Study on the Role of Artificial Intelligence...

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Surya Deveswar Reddy, Kovvuri Uday Shaik, Ayesha Balasundaram, A. Nivedita, M. Ram Kakarla, Lakshmi Sai Nithin, Mattapally Sai School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Vellore Institute of Technology Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350394580

Distinguishing the breeds of animals is one of the major examination areas in animal welfare. Recognition of animal breeds demands numerous determining elements which are necessary to be explored and following classification of variety. Animals such as cows, buffaloes, sheep, goats, and other diverse creatures play a huge role in the production of milk, and sometimes meat. Every creature has different species and provide unique benefits which contribute a lot to the Indian economy. India is currently holding the first rank in milk export. Hence, it is compulsory to introduce automated methods that will differentiate breeds of various species. In addition, identifying diseases that effected to animals is also an important area related to animal protection and it is necessary to automate the detection as it would be effective for identify the diseases in primary stages. This paper carries out a full-scale study on how deep learning techniques are used in animal husbandry. © 2023 IEEE.

关键词： Animal Breeds Automate Methods Classification Deep learning Diseases Machine Learning

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Stock Market Prediction Using Machine Learning Techniques: A Comparative Study 7th

Stock Market Prediction Using Machine Learning Techniques: A...

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7th International Conference on Big Data and Cloud Computing Challenges, ICBCC 2022

作者： Chinthan, P. Mishra, Rashmi Prakash, B. Saleena, B. School of Mechanical Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819910502

Stock market prediction is one of the most important and time-consuming processes in the financial sector. This is mainly due to the fact that many factors could influence particular stock prices dynamically. In recent days, algorithm-based predictions have become widely popular among researchers because it provides better results in terms of improved accuracy and reduced error rates. That in turn helps the investors to make precise and informed decisions. In this study, a comparative analysis of various machine learning models was performed and the results were evaluated based on performance metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and accuracy. The objective of this comparative study is to help investors take better decisions based on the consistent results provided by the algorithmic models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mean square error

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A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Paul, Tushar Dhanalakshmi, R. Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350315127

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier - convolutional neural network (CNN);2) a wind speed filter;and 3) a detector - Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%). © 2023 IEEE.

关键词： Tropical cyclone

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Outage Analysis of Multiple IRS-aided Cooperative NOMA System 15

Outage Analysis of Multiple IRS-aided Cooperative NOMA Syste...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Murugadass, Arthi Kumaravelu, Vinoth Babu Kennedy, Helen Sheeba John Anjana, B.S. Dipinkrishnan, R. Bilim, Mehmet Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Department of Communication Engineering Vellore India Nuh Naci Yazgan University Department of Electrical and Electronics Engineering Kayseri Turkey

ISBN: (纸本)9798350370249

The traditional cooperative non-orthogonal multiple access (CNOMA) technique improves the outage and data rate performance for cell-edge users, albeit at the expense of near users. Due to the ability to control the propagation environment, introducing intelligent reflective surfaces (IRS) into a multi-user network can reduce costs, signal processing complexity, and energy consumption. In this work, a comprehensive downlink framework for a blind-IRS-supported CNOMA system is established to enhance outage performance for both cell-edge and near users. The closed-form analytical expressions for the outage probability of the near and cell-edge users within this proposed system are derived. Furthermore, these analytical expressions are validated through Monte-Carlo simulations. The cell-edge user of our proposed model outperforms traditional NOMA and IRS-assisted NOMA variants. To achieve a target outage probability of 10-3, our proposed system requires ∼ 4 dB lower signal-to-noise ratio (SNR) compared to other IRS-assisted NOMA variants. © 2024 IEEE.

关键词： Image coding

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Multilevel Product Classification Using Machine Learning Algorithms Sowjanya 3

Multilevel Product Classification Using Machine Learning Alg...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sowjanya Arjunan, R. Vijaya Babu, D. Vijendra Manipal Institute of Technology Manipal Academy of Higher Education Computer Science and Engineering Karnataka Manipal India Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Platforms for e-commerce commonly employ a preestablished, organized structure of product classifications. Traditional product categorization methods have primarily relied on text-based data, which can occasionally fall short of providing a comprehensive understanding of a product's characteristics. However, employing image data in classification models has several advantages. The pictures offer a wealth of visual knowledge by capturing details such as color, shape, texture, and size that text explanations may miss. By incorporating text and image data, the classifier gains greater knowledge of each product, resulting in higher categorization reliability. Furthermore, the inclusion of image data enables the machine learning model to discover aesthetic patterns and features that are tricky to convey through text alone, expanding its ability to precisely assign items to their corresponding categories. The current study validates a new classifier for predicting categories using both textual and image data. This suggested approach is being attempted to enhance the accuracy and fullness of product classification. © 2024 IEEE.

关键词： Image classification

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Proximity Detection Based Low-Cost and Handheld IoT Device for Tracking Lost Objects 10

Proximity Detection Based Low-Cost and Handheld IoT Device f...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Sachan, Kanishk Kumar Natarajan, Anisha School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India

ISBN: (纸本)9798331533229

This paper presents a novel device leveraging the ESP32 micro controller to determine the proximity of a Wi-Fi signal source such as a smartphone hotspot or Wi-Fi router based on the received signal strength indicator (RSSI) values. The device connects to a smartphone hotspot, monitors RSSI values, and provides audio feedback proportional to the distance from the signal source. The proposed implementation can be employed as a low-cost handheld device for locating a misplaced Wi-Fi enabled smart device and also as a proximity tracker for items like bags. This paper discusses the design, implementation and potential applications of the proposed system. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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A Comparative Study of RESNET-Pretrained Models for computer Vision 2023

A Comparative Study of RESNET-Pretrained Models for Computer...

引用

15th International Conference on Contemporary Computing, IC3 2023

作者： Prajwal, Thode Sai Ilavarasi, A.K. School of Computer Science and Engineering Vellore Institute of Technology-Chennai India School of Computer Science and Engineering Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology-Chennai India

ISBN: (纸本)9798400700224

The science of computer vision has recently seen a revolution with deep learning models, and Residual Networks (ResNets) have become one of the most popular and efficient designs. ResNets have outperformed conventional convolutional neural networks in several image classification tasks, demonstrating outstanding results. An overview of ResNets and their variants, including Res2Net, COVID-ResNet, ResNeSt, SENet, and others, is presented in this review. We assess the performance of the ResNet design variants using benchmark datasets, highlight the major ideas and technological advancements underlying them, and their pros and cons. We also discuss some recent developments in ResNets research and applications, including object identification, picture segmentation, and transfer learning. This review would be beneficial to the research community in selecting the appropriate models based on the intended usage. © 2023 ACM.

关键词： computer vision

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Malware Analysis of Cryptojacks: Crackonosh and Winstar Nss Miner

Malware Analysis of Cryptojacks: Crackonosh and Winstar Nss ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Raj, Sparsh Basta, Aditya Laksha, S. Sinha, Samarth Asha, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India National Institute of Technology Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350319200

Malware analysis is an essential process for understanding the behaviour and purpose of a piece of malware. Cryptojacks are malware designed to exploit the computational resources of a machine to mine cryptocurrency. The year of 2017 saw the rise of Cryptocurrency which led to the rise of Cryptojacks. Despite being a very deadly piece of malware, Cryptojacks aren't researched much because unlike other pieces of malware, they don't cause any damage to the data or ask for any ransom. In 2018, with the end of Coin-hive project, browser based Cryptojacks disappeared but machine based Cryptojacks are still in the wild. In our work, we are analysing 2 Cryptojacks one taken from 2017 era (WinstarNssMiner) and another (Crackonosh) which was released recently(last week of June). We aim to find out how these Cryptojacks work and also about how they have changed over time. In turn, we can understand how the attitude of hackers has changed towards Cryptojacks. © 2023 IEEE.

关键词： crackonosh cryptojacks dynamic analysis Malware analysis static analysis winstarnssminer

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Automated Breast Cancer Diagnosis Using Deep Learning CNN Models 2

Automated Breast Cancer Diagnosis Using Deep Learning CNN Mo...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Saravanan, G. Jeevanantham Prabin, S.M. Parthasarathy, R. VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331534950

The study introduces a deep learning model, CCNN, developed to identify and classify eight types of breast cancer: normal adenosis, normal fibroadenoma, normal phyllodes tumor, normal tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. This model was integrated with five pre-existing models - Xception, InceptionV3, VGG19, MobileNetV2, and ResNet101 - all trained for image classification using the ImageNet database, to categorize breast cancer MRI images. The dataset, sourced from Kaggle's repository, was further augmented using the Generative Adversarial Network (GAN) technique. For 30 experiments carried out to evaluate the introduced model and the transfer learning of five and proposed CCNN models, the F1-score values accuracy yielded were respectively: 98.04%, 96.13%, 98.54%, 97.11%, 94.20% and 98.98%. This study emphasizes the importance of improving the accuracy of early breast cancer detection to enhance the survival rates of patients. © 2024 IEEE.

关键词： Generative adversarial networks

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