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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Akash, v. Manoj Howale, s. Gupta, Ajit J. Kayarvizhy, N. saranappa, Raj Kumar Department of Machine Learning B.M.S. College of Engineering Bangalore India Department of Computer Science B.M.S. College of Engineering Bangalore India Department of Electrical and Electronics B.M.S. College of Engineering Bangalore India SRIB/SAMSUNG Research Institute India Bangalore India

ISBN: (纸本)9798350305258

With the increasing use of Wi-Fi in our devices at home and in offices, etc. it's imperative to connect to an AP which provides seamless and smooth performance. This research paper addresses the need for an efficient solution to predict and select the most suitable Access Point, thus enhancing the user's quality of application usage. We have worked on a Machine Learning model which takes care of different parameters such as signal strength, bandwidth, distance, frequency, traffic parameters and previous connections with Access Point and predicts the best Access Point user should connect to. The methodology described in this paper has been developed based on the Google scoring algorithm, which we referred as a foundation for our approach. The integration of the Google scoring algorithm empowers our model to further optimize Access Point selection, ensuring an enhanced user experience while seamlessly switching between networks, thus solving the sticky band or sticky Client problem in Wi-Fi. The research which has been proposed by this paper could potentially revolutionize the way transitions are made between networks to make the process smooth and latency free. © 2023 IEEE.

关键词： best Access Point Machine Learning WiFi

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Analysis of Cyber security Attacks using Kali Linux

Analysis of Cyber Security Attacks using Kali Linux

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2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Gururaj H, L. soundarya b, C. Janhavi, v. Lakshmi, H. Prassan Kumar, M.J. Vidyavardhaka College of Engineering Computer Science and Engineering Mysuru India B.G.S Institute of Technology Computer Science and Engineering B.G Nagar India

ISBN: (纸本)9781665483162

In the prevailing situation, the sports like economic, industrial, cultural, social, and governmental activities are carried out in the online world. Today's international is particularly dependent on the wireless era and protective these statistics from cyber-assaults is a hard hassle. The reason for cyber-assaults is to damage thieve the credentials. In a few other cases, cyber-attacks ought to have a navy or political functions. The damages are PC viruses, facts break, DDs, and exceptional attack vectors. To this surrender, various companies use diverse answers to prevent harm because of cyberattacks. Cyber safety follows actual-time data at the modern-day-day IT data. so, far, numerous techniques have proposed with the resource of researchers around the area to prevent cyber-attacks or lessen the harm due to them. The cause of this has a look at is to survey and comprehensively evaluate the usual advances supplied around cyber safety and to analyse the traumatic situations, weaknesses, and strengths of the proposed techniques. Different sorts of attacks are taken into consideration in element. In addition, evaluation of various cyber-attacks had been finished through the platform called Kali Linux. It is predicted that the complete assessment has a have a study furnished for college students, teachers, IT, and cyber safety researchers might be beneficial. © 2022 IEEE.

关键词： Cybersecurity

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binary Growth Optimizer: For solving Feature selection Optimization Problems 15th

Binary Growth Optimizer: For Solving Feature Selection Optim...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Chu, shu-Chuan Dou, Zhi-Chao Pan, Jeng-shyang Kong, Lingping Pan, Tien-szu College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung82445 Taiwan

ISBN: (纸本)9789819700677

The present study introduces a novel binary meta-heuristic optimizer, referred to as the binary Growth Optimizer (bGO), which is specifically developed to address discrete optimization problems. The bGO utilizes transfer functions to convert positions in a continuous space to discrete positions. To assess the performance of the proposed algorithm, we conducted experiments on 14 publicly available datasets from the UCI Machine Learning Repository. We compared the bGO’s performance with seven state-of-the-art meta-heuristics. based on convergence accuracy statistics, the results indicate that the bGO outperforms the seven compared meta-heuristics, providing the most favorable outcomes. © 2024, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Heuristic algorithms

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Diagnose crop disease using Krill Herd optimization and convolutional neural scheme

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International Journal of Information Technology (singapore) 2023年第8期15卷 4167-4178页

作者： Parthiban, K. Rao, Y. venkata Harika, b. Kumar, Ravi shaik, Amjan shankar, s. siva School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering KG Reddy College of Engineering and Technology (Autonomous) Chilkoor Village Moinabad Mandal Telangana Ranga Reddy District India Department of Computer Science and Engineering Department Sri Venkateswara College of Engineering and Technology (Autonomous) R V S Nagar Tirupati Road Andhra Pradesh Chittoor 517127 India Department of Electronics and Communications Engineering Jaypee University of Engineering and Technology Guna 473226 India Department of CSE St. Peter’s Engineering College TS Hyderabad India

Develop a novel Krill Herd-based Convolutional Neural (KHbCN) scheme to identify and diagnose crop diseases accurately. Using an improved krill herd fitness function, the proposed model can identify crop disease reliably and improve detection performance. The krill herd fitness is updated to the convolutional neural network (CNN) to diagnose crop damage accurately. The created framework is executed in Python, and the system is evaluated and trained using the plant villa dataset. After removing mistakes during preprocessing, feature extraction is used to extract texture features from the crop. Finally, the constructed model uses the fitness of the krill herd to identify crop illness. by recognizing and detecting agricultural diseases, the primary goal of building a convolution-based optimization model is to enhance the growth of agriculture. The experimental findings of the framework's development are contrasted with those of other cutting-edge methods that achieve 99.85% accuracy, 98.98% recall, 99.85% precision, and 40 ms execution time. © 2023, The Author(s), under exclusive licence to bharati vidyapeeth's Institute of computer Applications and Management.

关键词： Agriculture Classification Crop disease diagnosis Feature extraction Pixel variation Preprocessing segmentation Threshold value

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A conceptual analysis for the measurement of stress intensity by deep learning using EEG signals

A conceptual analysis for the measurement of stress intensit...

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Nikhil, A.s. banakar, Nitish N Jagadeesh, Pranav bhat M, Madhukesh selva Kumar, s. B.M.S College of Engineering Department of Computer Science and Engineering Bengaluru560019 India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

several Health-related issues are caused due to mental stress. In Initial stages the level of mental stress can be measured using tools which are developed by Physicians and scientists. Many neuroimaging tools have been suggested to measure the stress developed in the workplace. Electroencephalogram (EEG) signals are a key component as they contain vast knowledge related to conditions and states of mind. Existing methods for EEG signal analysis are discussed to measure level of stress in the paper. This review focuses the crucial variations in-between the research results and debates those fluctuations from the data analysis methods give rise to many contrary results. The fluctuations in results may be because of several factors like no proper protocol, the area of interest within the brain, stressor types, experiment period, EEG analysis, methodology used by which properties are extracted, and classifier type. Hence, the major factor relating to detection stress in mind, by choosing the major suitable properties. A convoluted come varied band of properties relating to EEG, with variation in time, working, and dynamic connections of brain, required inclusion of different methods to learn about their connections with mental stress. subsequently, the evaluation recommends merging cortical activations with the integration to the network calculates with approaches of deep learning to enhance the correctness range of mental stress calculation. © 2022 IEEE.

关键词： brain computer interface

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An AI based Chat bot For banking Applications using Intelligent Chat bots

An AI Based Chat Bot For Banking Applications using Intellig...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Kira, Ajmeera vidhya, veda Kamesh, D.b.K. srilakshmi, v. Paulraj, D. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Malla Reddy Engineering College for Women maisammaguda hyderabad 500100 India B V Raju Institute of Technology India Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India

ISBN: (纸本)9798350305708

A chat and voice assistant that is powered by AI and is integrated into a banking application. This assistant translates data in such a way that the text and audio are designed to simulate human conversations. since their inception, voice and chat assistants have seen substantial technological advancement. The advancements that have been made in machine learning and natural language processing have made it possible for them to now learn from their experiences and conduct conversations that are more human-like. because of their more human nature, they are able to assist with a wide variety of tasks, such as calculating loan interests, confirming facts regarding transactions, and checking savings. © 2023 IEEE.

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Gesture Recognition Machine vision video Calling Application Using YOLOv8 22

Gesture Recognition Machine Vision Video Calling Application...

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22nd International symposium on Communications and Information Technologies, IsCIT 2023

作者： Pruthvi Darshan, s.s. Patil, shantakumar b. Patil, bhargav s. Premjyoti Sai Vidya Institute of Technology Department of Computer Science & Engineering Karnataka Bengaluru560064 India B.M.S. College of Engineering Department of Computer Science & Engineering Karnataka Bengaluru560019 India Karnataka Bengaluru560019 India

ISBN: (纸本)9781665457316

Gesture recognition pertains to a computer system's capacity to identify and understand hand gestures, categorizing them into captions or text. This technology holds vast potential for applications in education, communication, and accessibility, especially for individuals with hearing disabilities. This research introduces a gesture recognition video calling application, enabling communication among individuals with and without speech and hearing abilities. To enhance accuracy, the application employs Darknet's YOLOv8 for object detection. YOLOv8 is a robust and efficient system that excels in various computer vision tasks, such as object detection, instance segmentation, and image classification. The paper proposes a trained model for image classification, encompassing ten different hand gesture classes, which processes each video frame during the call, achieving a remarkable 98% accuracy with a low latency of 10ms. The application displays the classification results on top of the interface during the video call. © 2023 IEEE.

关键词： Object detection

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An Exploratory Analysis of GsDMM and bERTopic on short Text Topic Modelling 4

An Exploratory Analysis of GSDMM and BERTopic on Short Text ...

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4th International Conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Udupa, Abhinandan Adarsh, K.N. Aravinda, Anvitha Godihal, Neelam H Kayarvizhy, N. B M S College of Engineering Department of Computer Science and Engineering Bengaluru Karnataka560019 India

ISBN: (纸本)9781665456487

Topic models may be a useful tool for locating latent subjects in collections of documents. short text clustering has become a more important task as social networking sites like Twitter have gained popularity. short text is characterised by high sparsity, high-dimensionality, and large-volume. These characteristics are challenging to overcome. Two of the most well-known short text modelling algorithms are bERTopic and the Gibbs sampling Dirichlet Multinomial Mixture Model (GsDMM). GsDMM is a topic model which can infer the count of topic clusters automatically with a good compromise between the fullness and uniformity of the clustering results, and is fast to converge. bERTopic is a neural topic model that extracts coherent topic representations based on the semantic similarity of words and phrases in the and clustering with the help of a class-based form of TF-IDF. We compare these two algorithms in this paper to determine which model is more effective in short text topic modelling. © 2022 IEEE.

关键词： semantics

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Enhanced IoT system for Real-Time Detection and Monitoring of Cardiac Events Using Machine Learning Algorithms 3

Enhanced IoT System for Real-Time Detection and Monitoring o...

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3rd IEEE Delhi section Flagship Conference, DELCON 2024

作者： safia Naveed, s. Ameen, Nabeena Nulin Jeriba, J. Kcg College of Technology Department of Computer Science and Engineering Chennai India B. S. Abdur Rahman Crescent Institute of Science and Tech Department of Information Technology India

ISBN: (纸本)9798331518592

Heart attacks remain a primary cause of global mortality, highlighting the urgent need for systems that enable early detection and intervention. This study proposes a smart IoT-based system designed to detect and monitor heart attack symptoms in real-time, thus facilitating timely medical response. The system integrates wearable sensors to collect continuous data on heart rate, stress levels, and positional changes, which is then processed through Arduino IDE and transmitted to an IoT server. Machine learning algorithms, including support vector Regression (svR) and Artificial Neural Networks (ANN), are employed to analyze these signals for abnormal patterns indicative of a potential heart attack. Major findings indicate that this system accurately detects heart rate irregularities, and stress fluctuations and can alert healthcare providers, significantly improving remote monitoring capabilities. The implications of this study suggest that IoT-enabled monitoring systems can substantially enhance patient outcomes by allowing for early diagnosis and proactive care in remote settings, thereby reducing the risk of fatal heart events. © 2024 IEEE.

关键词： Wearable sensors

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CovDNet: A Hybrid Multi - Level Densely Connected Neural Network for Improvised Analysis and Classification of COvID-19 Using Chest CT-scans 7

CovDNet: A Hybrid Multi - Level Densely Connected Neural Net...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Deepa, s. Praveenkumar, G.D. suguna, R. Duraivelu, vivek Jamunadevi, C. sundhar, s.G. Kongu Engineering College Perundurai Department of Computer Technology-PG Tamilnadu Erode638060 India College Department of Computer Science Tamilnadu Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur502313 India PG Kongu Engineering College Perundurai Department of Computer Technology Tamilnadu Erode638060 India Kongu Engineering College Perundurai Tamilnadu Erode638060 India

ISBN: (纸本)9798350340600

COvID-19, caused by the new coronavirus sARs-Co v-2, has turned into a worldwide health emergency, needing speedy and precise diagnostic techniques. This abstract provides a thorough evaluation of research works focusing on COvID-19 identification using DenseNet, a cutting-edge convolutional neural network architecture. DenseNet is notable for its innovative design, which fosters feature reuse while relieving the vanishing gradient problem and enhancing network information flow. Researchers have created novel ways for COvID-19 identification using medical imaging, such as chest X-rays and CT scans, by harnessing the capabilities of DenseNet. The analyzed studies demonstrate DenseNet's efficiency in recognizing COvID-19-specific patterns and distinguishing them from other lung diseases. These investigations have shown remarkable accuracy, sensitivity, and precision, exceeding established machine learning approaches and even professional radiologists. This research study also highlights the difficulties and constraints experienced while using DenseNet to identify COvID-19. Issues such as dataset quantity, class imbalance, and model decision-making process interpretability are addressed. Furthermore, future research paths and prospective enhancements, such as multi-modal data integration and the development of explainable AI systems, are investigated. The experimental results indicate that the proposed approach achieves high accuracy, sensitivity, and precision in the identification of COvID - 19. The proposed methodology achieved an accuracy of 89.74%, sensitivity of 87.25%, and precision of 79.56%, which outperforms the existing state-of-the-art methods. The proposed approach is robust and can effectively differentiate between COvID-19 positive and negative cases, which is essential for early detection and prompt treatment. © 2023 IEEE.

关键词： COvID-19

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