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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Malathi, M. Armash, S. M. Mohammed Kavitha, S. Chinthamani, B. Sinthia, P. Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India UG Scholar Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electrical and Electronics Engineering Saveetha Engineering College Chennai India Department of Electronics and Instrumentation Engineering Easwari Engineering College Chennai India Department of Biomedical Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9789819738090

The insurance fraud claim detection is a critical task in the insurance industry. Various methodologies were proposed in terms of detecting fraud claims but the main thing is handling the proportionality between the fraud and non-fraud cases in the dataset before it was given to the model, in that case class imbalance may occur. Class imbalance where the number of fraudulent claims is significantly lower than legitimate claims which pose a challenge for accurate fraud detection. In this paper, we employed [Elreedy and Atiya in Inf Sci 505:32–64, 2019] Synthetic Minority Over-sampling Technique (SMOTE) algorithm to address the imbalance problem and enhance the performance of the fraud claim detection model. SMOTE is a popular technique for oversampling the minority class by creating synthetic samples that are similar to the existing minority class instances. By generating synthetic examples, SMOTE helps in balancing the class distribution and allows the model to learn from a more representative dataset. This technique is particularly effective when the available data is limited and insufficient to capture the complexities of the limited class. This process results in a larger and more balanced dataset, enabling the model to learn from a diverse range of fraudulent claim patterns. By applying SMOTE to our dataset, we are able to overcome the class imbalance issue and improve the performance of the fraud claim detection model. The resampled dataset provides a more accurate representation of the underlying distribution, leading to enhanced detection of fraudulent claims. We evaluate the performance of the model by measuring various metrics such as accuracy, precision, recall and F1-score. Our findings demonstrate the effectiveness of SMOTE in addressing class imbalance with improved random forest and LightGBM model for fraud claim detection process. The utilization of SMOTE contributes to better identification of fraudulent insurance claims, reducing potential losses an

关键词： Crime

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A Systematic Review of Thermoelectric Peltier Devices: Applications and Limitations

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Fluid Dynamics & Materials Processing 2023年第1期19卷 187-206页

作者： M.K.Shilpa Md Abdul Raheman Abdul Aabid Muneer Baig R.K.Veeresha Nagesh Kudva Department of Computer Science and Engineering NMAM Institute of TechnologyUdupi574110India Department of Electrical and Electronics Engineering NMAM Institute of TechnologyUdupi574110India Department of Engineering Management College of EngineeringPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Mechanical Engineering NMAM Institute of TechnologyUdupiKarnataka574110India

Conventional refrigeration processes release ammonia and freon into the atmosphere,which results in global *** problems may be overcome by using thermoelectric modules because of the absence of coolants or refrigerants in these ***,the cooling performances of such modules are relatively small in comparison to those of conventional *** this paper,the working principles of thermoelectric modules are discussed together with a review of different relevant aspects,namely:the thermoelectric materials,and their mechanical properties used to build thermoelectric devices,different types of thermoelectric devices available on the market,mathematical modeling of thermoelectric materials,and various applications of thermoelectric materials in different fields.

关键词： TEG peltier module seebeck effect refrigerator CoP TEC

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Efficient Real-Time Human Activity Recognition with IoT Sensor Data Using Conditionally Parametrized Convolutions 4

Efficient Real-Time Human Activity Recognition with IoT Sens...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Lovanshi, Mayank Tiwari, Vivek Singh, Anurag Ingle, Rajesh International Institute of Information Technology Department of Computer Science Engineering Naya Raipur India ABV-Indian Institute of Information Technology Department of Computer Science Engineering Gwalior India International Institute of Information Technology Department of Electronics Communication Engineering Naya Raipur India Symbiosis Skills of Professional University Department of Computer Science Engineering Pune India

ISBN: (纸本)9798331518981

Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises computational demands. The high operational costs present challenges for real-time HAR on sensor-based devices. Shallow learning approaches, while computationally efficient, generally fail to deliver comparable performance. Consequently, there is a critical need for deep learning methods that can effectively balance accuracy and computational cost, a topic that remains largely underexplored. This paper introduces a computationally efficient CNN that utilizes conditionally parametrized convolution, designed specifically for real HAR on sensor-based devices. The model is tested on the OPPORTUNITY dataset, achieving existing accuracy without increasing computational load. Through extensive ablation studies, we show that this high-capacity network surpasses baseline models while maintaining a similar level of computational efficiency. The proposed method can directly replace existing deep HAR frameworks and be easily implemented on sensor-based platforms for real-time applications. © 2024 IEEE.

关键词： Convolution

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A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System

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Annals of Data Science 2024年第1期11卷 103-135页

作者： Choithani, Tamanna Chowdhury, Asmita Patel, Shriya Patel, Poojan Patel, Daxal Shah, Manan Department of Computer Science and Engineering Indus University Gujarat Ahmedabad India Department of Electrical Engineering Nirma University Gujarat Ahmedabad India Department of Electronics and Communication Engineering Nirma University Gujarat Ahmedabad India Department of Chemical Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382426 India

In recent years cryptocurrencies are emerging as a prime digital currency as an important asset and financial system is also emerging as an important aspect. To reduce the risk of investment and to predict price, trend, portfolio construction, and fraud detection some Artificial Intelligence techniques are required. The Paper discusses recent research in the field of AI techniques for cryptocurrency and Bitcoin which is the most popular cryptocurrency. AI and ML techniques such as SVM, ANN, LSTM, GRU, and much other related research work with cryptocurrency and Bitcoin have been reviewed and most relevant studies are discussed in the paper. Also highlighted some possible research opportunities and areas for better efficiency of the results. Recently in the past few years, artificial intelligence (AI) and cybersecurity have advanced expeditiously. Its implementation has been extensively useful in finance as well as has a crucial impact on markets, institutions, and legislation. It is making the world a better place. AI is responsible for the simulation of machines that are replicas of human beings and are intelligent enough. AI in finance is changing the way we communicate with money. It helps the financial industry streamline and optimize processes from credit judgments to quantitative analysis marketing and economic risk management. The main goal of this research has been investigating certain impacts of artificial intelligence in this contemporary world. It's centered on the appeal of artificial intelligence, confrontation, chances, and its influence on professions and careers. The research paper uses AI to enable banks to generate financial resources and to provide valuable customer services. The application of the growing Indian banking sector is part of everyday life made up of several banks like RBI, SBI, HDFC, etc. and these banks have digitally implemented using chat-bots that have brought benefits to the customers. © The Author(s), under exclusive licence t

关键词： Risk assessment

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Automated Healthcare System Using AI Based Chatbot 1

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International Conference on Intelligent Computing and Networking, IC-ICN 2022

作者： Mendon, Akshay Patil, Megharani Gupta, Yash Kadakia, Vatsal Doshi, Harsh Department of Electronics and Telecommunication Thakur College of Engineering and Technology Mumbai India Department of Computer Engineering Thakur College of Engineering and Technology Mumbai India

ISBN: (数字)9789819900718

ISBN: (纸本)9789819900701

Medical care is vital to having a decent existence. Be that as it may, it is undeniably challenging to get an appointment with a specialist for each medical issue and due to the current global pandemic in the form of Coronavirus, the healthcare industry is under immense pressure to meet the ends of patients’ needs. Doctors and nurses are working relentlessly to treat and help the patients in the best possible way and still, they face problems in terms of time management, technical resources, healthcare infrastructure, support staff as well as healthcare personnel. To resolve this problem, we have made a chatbot utilizing Artificial Intelligence (AI) that can analyze the illness and give fundamental insights regarding the infection by looking at the data of a patient who was previously counselled at a health specialist This will also assist in lessening the medical services costs. The chatbot is a product application intended to recreate discussions with human clients through intuitive and customized content. It is in many cases portrayed as the most moving and promising articulations of communication among people and machines utilizing Artificial Intelligence and Natural Language Processing (NLP). The chatbot stores the information in the data set to recognize the sentence and pursue an inquiry choice and answer the corresponding inquiry. Through this paper, we aim to create a fully functional chatbot that will help the patients/users to know about the disease by simply entering the symptoms they possess. Additionally, they can also get information about certain medicine by simply typing the name of the medicine. Another additional feature is the ability of the bot to answer general questions regarding healthcare and wellbeing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health care

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Intelligent Monitoring System for Automatic Air Conditioning System 5

Intelligent Monitoring System for Automatic Air Conditioning...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Manikandan, P. Swedheetha, C. Ram Mohan, R.L. Kumar, Sandala Likith Reddy, Peram Damodar Sri Sai, Pachabotla Adithya Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Krishnankoil India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331518097

In order to improve indoor air quality, optimize energy consumption and avoid the accidents due to gas leakage, this paper proposes an intelligent air conditioning system that integrates several sensors such as DHT11, PIR sensors, MG6 Gas sensor and PMS7003 sensor and control mechanisms. An Arduino Mega, the system's central component, synchronizes data from several sensors to efficiently control the air conditioner and other linked equipment. This system keeps track of how many people enter and leave the space, enabling the air conditioning system to modify its settings in real time according to occupancy. When the room is empty, this feature saves energy. If gas leakage is present, system notifies users through a buzzer and turn off the AC. Many users often forget to clean their AC filters regularly, which can lead to reduced efficiency, higher energy consumption, and poorer air quality. Our innovative AC system addresses this common issue. Once the dust level crosses a predefined threshold, the system triggers a clean filter alert, reminding to act before it affects performance. Also, the air conditioner kicks on to cool the room if the outside temperature is high and the inside temperature is higher than an acceptable level. In contrast, the air conditioner is shut off to avoid needless cooling if the inside temperature is low. A display unit shows temperature, humidity, occupancy, gas levels, and dust concentration. This integrated technique automatically modifies the system's operation based on conditions, ensuring not only a more hygienic and comfortable environment but also improved energy efficiency © 2024 IEEE.

关键词： Air quality

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Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

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Soft Computing 2025年第3期29卷 1879-1896页

作者： Haque, Sheikh Burhan Ul Zafar, Aasim Haq, Sheikh Riyaz Ul Haque, Sheikh Moeen Ul Ahmad, Mohassin Roshan, Khushnaseeb School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Computer Science Engineering Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India Department of Electronics and Communication Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India

Deep and machine learning models have become pivotal in medical image analysis, especially for diagnosing COVID-19 using X-rays and CT scans. While these models, including transfer learning-based approaches, have achieved high accuracy, they remain highly vulnerable to adversarial attacks, which can manipulate input data and cause misclassification, posing critical risks in clinical applications. This study introduces a novel approach to addressing this issue by systematically evaluating the impact of adversarial attacks on COVID-19 diagnosis models built with two leading architectures, VGG-16 and DenseNet-121, using the Fast Gradient Sign Method (FGSM). The FGSM attack causes a dramatic drop in accuracy, reducing VGG-16’s accuracy from 95.12 to 9.97% and DenseNet-121’s from 96.51 to 10.13%. To counter these vulnerabilities, we propose a novel defense mechanism that combines adversarial training with Gaussian noise data augmentation, a dynamic approach that generates perturbations across various epsilon values during the training phase. This innovative method significantly enhances model robustness, restoring accuracy to over 92% on adversarial examples. These findings emphasize the need for strong defense mechanisms in deep learning models for COVID-19 diagnosis, ensuring reliability and security against adversarial threats in clinical environments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Adversarial machine learning

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Real-time Indian Sign Language Recognition using Skeletal Feature Maps 14

Real-time Indian Sign Language Recognition using Skeletal Fe...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Vishal Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Agarwal, Agrima Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350335095

Sign language recognition is a widely researched problem that has gained momentum since the advent of deep learning. Many researchers have designed models using different deep learning architectures claiming high validation accuracy but low real-time performance. The proposed model achieves consistent prediction in real time through Skeletal Feature Maps using keypoints extracted by MediaPipe. These images are passed to a novel 15-layer CNN architecture for classification. The proposed model can achieve a high validation accuracy of 99.84% over a self-created 106-word Indian Sign Language dataset, that is higher than other machine learning models such as SVM, Gaussian Naive Bias, Decision Tree, Random Forest and some pre-trained standard CNN architectures. The salient feature of the proposed model is it is consistent and accurate independent of the background, subject and lighting conditions. Further, the model is implemented as a desktop application and tested by different signers. © 2023 IEEE.

关键词： Decision trees

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Evaluating and Contrasting the Performance of Web Development Technologies 9

Evaluating and Contrasting the Performance of Web Developmen...

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

作者： Swedheetha, C. Shruthika, C. Preethi, Rubi Vedha Varshini, T.B.J. Maryam, Sabreen Manikandan, P. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350377972

The next generation of webpages is heavily influenced by large scale, high concurrency, and massive amounts of data. Building data-intensive web apps has made *** popular and effective. We employed scenario testing and benchmark testing to examine and contrast the performance of ***, Python-Web, and PHP. Per the testing results, PHP and Python-Web handle a lot less requests in a given amount of time than ***, which is evident from some useful performance metrics. Our findings, taken together, show that *** is the most lightweight and efficient of the three, making it a good choice for I/O-intensive websites;PHP, on the other hand, is best suited for small- to medium-sized applications, while Python-Web is developer-friendly and excels at large web architectures. This research study includes the performance evaluation of web development technologies. © 2024 IEEE.

关键词： Python

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