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Secure Data Transmission Based on Artificial Intelligence Using IoT in Security Systems 5

Secure Data Transmission Based on Artificial Intelligence Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Senthil Kumar, S. Maruthaveni, R. Jayanthi, N. Yadav, Ajay Kumar Kalaiarasan, T.R. Sri Ramakrishna Mission Vidyalaya College of Arts and Science Department of Information Technology Coimbatore India Dr.S N S Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Deemed to Be University Periyar Maniammai Institute of Science and Technology Department of Commerce Thanjavur India United Institute of Management Department of Computer Application U.P. Prayagraj India Sri Krishna College of Engineering and Technology Dept of Information Technology Coimbatore India

ISBN: (纸本)9798350383522

The Internet of Things (IoT), a rapidly expanding and constant technology today, enables the efficient exchange of information and control of electronic equipment. As technology grows more and more integrated into our daily lives, IoT serves as a bridge between human intelligence and artificial intelligence. Although the Internet of Things (IoT) presents numerous benefits for efficiency and creativity, it also poses serious privacy and security concerns. The main topic of the research is the enormous challenges in protecting IoT environments caused by the sheer number and variety of connected devices. This study's main goal is to look into how machine learning methods may be used to lessen the ongoing security vulnerabilities in IoT environments. We intend to create more resilient and intelligent security systems that can adapt to the changing threat environment by utilising machine learning's capabilities. The proposed study provides a thorough examination of the various elements and traits that make up IoT systems, with a focus on the current security measures. By taking a look at these elements, we may determine which strategies and techniques stand the highest chance of improving IoT security. This makes it possible for IoT deployments to be safer and more dependable in a technological setting that is quickly changing. © 2024 IEEE.

关键词： Camera Drowsiness Eye Face detection Hardware device Opencv Python

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumit Saklani Mahesh Manchanda Raman Sharma Devendra Singh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments.

关键词： Deep learning Accuracy Rain Smart cities Scalability Urban planning Traffic control Real-time systems Safety Internet of Things

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Research on Optimization Algorithm of Real Estate Enterprise Value Evaluation Based on BP Neural Network 2

Research on Optimization Algorithm of Real Estate Enterprise...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Niranjanamurthy, M. Saravanan, S. Mahaveerakannan, R. Surendran, S. Nakrani, Tulsidas Arun, M.R. Department of AI and ML BMS Institute of Technology and Management Avalahalli Karnataka Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Tamilnadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department of Computer Application Sankalchand Patel College of Engineering Sankalchand Patel University Gujarat Visnagar India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India

ISBN: (纸本)9798350305418

Because of its professional skills and expertise, it is able to persuade both the buyer and the selling and make the transaction easier. An optimal design scheme for real estate businesses' worth appraisals is proposed here, and it is based on BPNN (BP neural network). A worth appraisal system is developed by doing an analysis of several worth indicators pertaining to real estate companies, and then the system is optimized with the use of BPNN. In the final step, an analysis of the simulation test is performed. According to the findings of the simulation, the arithmetic has a certain accuracy, which is 8.34% higher than the accuracy of traditional arithmetic. On the premise of ensuring the efficient processing of data in batches and finding the rules therein, reduce the impact of human subjectivity, improve the accuracy and scientificity of real estate appraisal, and provide certain reference for the national and local governments to reasonably regulate house prices, so as to promote the stable operation and healthy development of the real estate market. In doing so, you will be able to promote the growth of the real estate market in a healthy manner. © 2023 IEEE.

关键词： BP neural network Enterprise worth appraisal Real estate

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A Review on Breast Cancer Classification using Neural Networks

A Review on Breast Cancer Classification using Neural Networ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Aashita Rajput Muskan Sachin Yadav Megha Chhabra Arun Prakash Agarwal Department of Computer Science & Application School of Engineering &Technology Sharda University Greater Noida India

Breast cancer is the second most common type of cancer due to which females have such a high mortality rate. Several computer-assisted diagnostic systems have recently been proposed to operate with the least amount of data necessary due to the high number of unnecessary breast biopsies. The following review paper discusses the entire process of breast cancer classification along with various kinds of neural networks used to build such models. It further includes the various to-date research done in this domain and the different accuracy obtained by them.

关键词： Analytical models Computational modeling Biological system modeling Neural networks Breast biopsy Breast cancer Research initiatives

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Spam Email Detection using K-Nearest Neighbors: An Enhanced Approach

Spam Email Detection using K-Nearest Neighbors: An Enhanced ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sumit Saklani Kalyan Thapa Devendra Singh Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Spam emails represent a large portion of global email traffic and pose several risks, such as phishing, fraud, and malware attacks. Traditional spam filters are ineffective in combating spam that is ever-evolving, which makes the problems more complex in nature. Therefore, advanced machine learning techniques need to be adopted. This study attempts to integrate an optimized K-Nearest Neighbors (KNN) model for spam detection that implements Term Frequency-Inverse Document Frequency (TF-IDF) for text representation, along with Principal Component Analysis (PCA) for feature selection. Classification is performed with relevance to the neighbors with more distance using an adaptive distance weighting algorithm. Enhanced KNN techniques improve the metrics on Naive Bayes and Support Vector Machines (SVM) with respect to accuracy, precision, recall, and F1 score. Experimental results demonstrated the classification accuracy to amount 96.4 %, exceeds the existing systems. This study develops efficient spam detection systems having the capability of adapting novel spam methods.

关键词： Support vector machines Adaptation models Accuracy Computational modeling Unsolicited e-mail Heuristic algorithms Nearest neighbor methods Feature extraction Bayes methods Principal component analysis

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An Explainable Deep Learning Approach for EEG- Induced Motor Imagery Classification 20

An Explainable Deep Learning Approach for EEG- Induced Motor...

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20th IEEE India Council International Conference, INDICON 2023

作者： Ganguly, Biswarup Ghosh, Ahona Dey, Sharmistha Sarkar, Indranil Saha, Sriparna Meghnad Saha Institute of Technology Electrical Engineering Department Kolkata India Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India University Institute of Computing Chandigarh University Punjab Gharuan India Guru Nanak Institute of Technology Department of Computer Application Kolkata India

ISBN: (纸本)9798350305593

Movement interpretation from the motor imagery (MI) signal of electroencephalogram (EEG) is an emerging field that can potentially upgrade the quality of life of individuals with motor impairments. In this manuscript, an explainable deep learning (x-DL) approach has been proposed to identify four class MI form EEG signals. The acquired EEG signals are preprocessed and then converted to time-frequency (TF) spectra for extracting the frequency content of the MI signals. The obtained TF images are then fed to a deep learning (DL) framework employing residual convolutional neural network for proper classification. The classification score, obtained from the network, has been propagated through the layers of the DL framework up to the input layer for obtaining layer wise relevance maps. These relevance maps signify the explainability of the proposed network, in terms of visual results. Comparative analysis reveals the effectiveness of the proposed x-DL module using a benchmark dataset. The obtained results and the utilization of signals from only two EEG channels demonstrate the possibility of employing the suggested strategy for developing a cost-effective brain-computer interfacing architecture applicable in real-time healthcare applications. © 2023 IEEE.

关键词： Convolutional neural network electroencephalogram explainable deep learning motor imagery wavelet transform

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Epidermis Bruise Analysis Using Deep Learning

Epidermis Bruise Analysis Using Deep Learning

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Vansheta Sharma Rupa Rani Priya Pathak Sudeshna Chakraborty Department of Computer Science & Engineering- Data Science ABES Institute of Technology Ghaziabad U.P India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad U.P India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan Department School of Computer Application and Technology Galgotias University UP India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model’s suitability for medical applications.

关键词： Deep learning Analytical models Accuracy Biological system modeling computer architecture Feature extraction Skin

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Novel Deep Learning Network Architectures for Time Series Forecasting

Novel Deep Learning Network Architectures for Time Series Fo...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Katariya, Jitendra Kumar Yadav, Dhananjay Gayathri, C. Kolekar, Vikas K. Daivagna, Umesh M. Pushparajesh, V. Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper presents novel deep-learning network architectures for time series forecasting. First, a singular deep gaining knowledge of network architecture is proposed and tested for the usage of the Google tendencies dataset as a benchmark. The proposed structure combines 1-dimensional convolution neural networks, recurrent neural networks, and long-term cells to seize and study lengthy-term styles in the time series information. The structure is compared to current deep getting-to-know architectures and is shown to outperform them in phrases of accuracy. Moreover, the proposed structure is applied to a secondary time collection dataset, the COVID-19 confirmed infection dataset, to attain compelling effects. Eventually, an open-source implementation of the proposed architecture is made available for use in addition to research. © 2023 IEEE.

关键词： Network architecture

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Analysis and Prediction of Stock Market Movements using Machine learning 4

Analysis and Prediction of Stock Market Movements using Mach...

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4th International Conference on Inventive Research in Computing applications, ICIRCA 2022

作者： Bhardwaj, Vivek Rahul, K. Venkata Kumar, Mukesh Lamba, Vikas School of Computer Science and Engineering Lovely Professional University Jalandhar144411 India School of Computer Application Lovely Professional University Punjab Phagwara India Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

As vast amounts of data are generated every day in many organizations, health, education, transport, business, marketing, communication, etc. All the data cannot be used for every analysis and processing as it contains some redundant data, outliers, some corrupted data, missing values and so on. To gain the knowledge-based and predictive data from this data, Data science plays a crucial role. Data science is a process of analysis and differentiating historical data to evaluate patterns or predict the occurrence of a future event using its robust algorithms. Many Organizations nowadays realized the importance of Data science and started implementing it in their strategic analysis and planning processes. This research study analyses the historical data of some stock market companies between certain time periods and perform analysis by using a data science algorithm and check the accuracy of predictions. Finally, the prediction results are compared with the accuracy of other algorithms and the best possible algorithm will be selected. © 2022 IEEE.

关键词： Forecasting

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CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models

CiGeN 1.0: A Generative AI Enabled Medical Image Augmentatio...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Dash, Archana Nayak, Debasish Swapnesh Kumar Swarnkar, Tripti Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Bhubaneswar751030 India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar752050 India National Institute of Technology Department of Computer Application Raipur492010 India

ISBN: (纸本)9798331530259

Deep Learning methods have gained a significant momentum in medical image classification tasks, including many ranges of datasets. However, these datasets often suffer from class imbalance, where the minority class (e.g., diseased cases) is underrepresented. This imbalance leads to biased predictions, affecting the model's performance. Generative Adversarial Networks (GANs) offer a potential solution by generating synthetic images for the minority class, helping balance the dataset. Methodology: In this study we further evaluated our Deep Learning Model CoVaD-GAN with separate set of datasets to test the dynamics behind it. We applied a Deep Convolutional GAN (DCGAN) to generate synthetic images for underrepresented classes in the chest X-ray (CXR) and Break His histopathological dataset. Augmented synthetic images were made a part of training set, and the model's performance was evaluated before and after augmentation using VGG-16, ResNet-50 with necessary metric measures as accuracy, sensitivity, and specificity were compared pre- and post-GAN augmentation. We also evaluated the quality of GAN-generated images using Fréchet Inception Distance (FID) and manual expert reviews. The results demonstrate significant improvements in model accuracy, sensitivity, and F1-scores after applying GAN-based augmentation. The use of GANs for data augmentation enhances the ability of deep learning models to generalize, particularly for minority classes. GAN-generated images were of high quality and contributed positively to model performance. The Proposed model (CiGeN 1.0) dynamically generalizes the other medical dataset introduced to it and derived a fair resulting diagnosis. © 2025 IEEE.

关键词： Chest X-rays CNN Deep Learning GAN Medical data augmentation

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