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Accidental Face Recognition and Detection Using Machine Learning 1

Accidental Face Recognition and Detection Using Machine Lear...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sharma, Ashish Agrawal, Piyansh Krishan Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications UP Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms. © 2023 IEEE.

关键词： Face recognition

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Improved Moth Flame Optimization with Deep Convolutional Neural Network for Colorectal Cancer Classification using Biomedical Images 2

Improved Moth Flame Optimization with Deep Convolutional Neu...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Parimala, S. Kumutha, R. Iram, Faiza Sunena Rose, M.V. Nanmaran, R. Angelpreethi, A. Department of Computer Science and Applications SRM Institute of Science and Technology Vadapalani India S. A. Engineering College Chennai India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India St.Joseph's College Tiruchirappalli India

ISBN: (纸本)9798350383867

Colorectal cancer (CRC) affects the large intestinal colon. CRC treated radiation, chemotherapy, and immunotherapy. If aberrant cells, which are predicted to become cancer cells, are detected early, CRC may be effectively managed. Histopathological analysis of tissue specimens is the most common way to diagnose CRC. Histopathological examination and appropriate therapy are the only ways to improve cancer survival. AI in colon and lung cancer histopathology analysis may help doctors identify instances faster, cheaper, and easier. Medical image processing may use ML devices for pattern recognition and resolution. This paper presents an Improved Moth Flame Optimisation with Deep Convolutional Neural Network (IMFO-DCNN) method for biomedical image-based colorectal cancer classification. The IMFO-DCNN approach uses the DCNN model to automatically extract biomedical picture properties. The IMFO method fine-tunes DCNN model hyperparameters to improve detection performance. The last step uses a Random Forest (RF) classification algorithm to effectively recognise and categorise biological pictures. Methodically estimating the IMFO-DCNN methodology on biological pictures improves CRC classification accuracy and resilience over traditional techniques. CRC detection and classification are efficient and reliable using IMFO-DCNN. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis and Optimization of Neural Networks for Image and Speech Recognition

Analysis and Optimization of Neural Networks for Image and S...

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

作者： Komala, C.R. Purushothaman, R. Ande, Pavan Kumar Dinesh, P.M. Anitha Rani, K.S. Yamuna, P. HKBK College of Engineering Department of Information Science and Engineering Karnataka Bengaluru560045 India Sri Ramachandra Institute of Higher Education and Research Faculty of Engineering and Technology Tamil Nadu Chennai600116 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur522502 India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem636005 India REVA University School of Computer Science and Applications Karnataka Bengaluru560064 India Acharya Institute of Graduate Studies Department of Computer Applications Karnataka Soladevanahalli 560107 India

ISBN: (纸本)9798350319200

The strategy of transforming vocal discourse signals into text using records is known as programmed discourse acknowledgment (ASR). In the ongoing time of the PC unrest, the ASR has a huge impact in further developing the client experience while communicating with the PCs in a characteristic manner. Conventional instruments like a console and mouse can't be utilized, and the client might do a limitless number of undertakings like controlling of contraptions and correspondence with client care. This study presents an air terminal enquiry framework in view of ASR. The program was made solely for the Telugu language. In view of the most generally posed inquiries about air terminals, a data set was created. Convolutional Brain Organization (CNN) has been utilized for preparing and testing the information base in view of its extraordinary exhibition. Convolution brain organizations (CNNs) have as of late been demonstrated to perform better on the telephone discovery test than regular completely associated profound brain networks when utilized in a half and half profound brain organization/stowed away Markov model (DNN/Gee) design. © 2023 IEEE.

关键词： Image Networks Neural Optimization Speech Recognition

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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Utilizing Blockchain Technology for Financial Services in Parallel, Distributed, and Grid Computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

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

作者： Chokkamreddy, Prakash Palagati, Anusha Rao, Akurathi Lakshmi Pathi Maheswari, P. Mahadevan, Vanitha Balan, Santhosh Kumar School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798331510299

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. Exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector. © 2024 IEEE.

关键词： Smart contract

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Thiyagarajan, Gomathi Swathi, Y. Cmr Institute of Technology Department of Computer Applications Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates. © 2025 IEEE.

关键词： Marketplaces

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Decentralized Intrusion Detection in Peer-to-Peer Networks using Stacking Ensemble Model 1

Decentralized Intrusion Detection in Peer-to-Peer Networks u...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Devi, R. Renuga Riadhusin, Rame Anu, D. Swarnateja, R. Revathi, R. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, due to the rise in Peer to Peer (P2P) networks has brought a significant way in which the data is shared and services are offered over the internet. These P2P networks make use of decentralized architecture in which nodes directly interact and exchange resources. However, due to the use of decentralized architecture, the P2P networks are exposed to various security threats such as data breaches, and other malicious attacks. In this research, Stacking Ensemble Model (SEM) is proposed for network intrusion detection in P2P networks. Initially, the data is considered from network intrusion detection dataset and then preprocessed with data imputation and min-max normalization effectively handles the missing values and rescales the data into significant range. After that, Recursive Feature Elimination (RFE) is incorporated to select optimal features from preprocessed data based on feature importance. Finally, the proposed SEM effectively detects the intrusion in P2P networks which enhances the security. From the results, the proposed SEM model obtained a better outcome in terms of accuracy, precision, recall, and F1-score compared to the existing K-Nearest Neighbor (KNN) by accomplishing 97.20%, 94.71%, 96.62%, and 95.65%, respectively. © 2025 IEEE.

关键词： Network intrusion

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Deep Reinforcement Learning for Resource Allocation in Edge Computing for IoT 6

Deep Reinforcement Learning for Resource Allocation in Edge ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Ashish Bordoloi, Dibyhash Saini, Ashish Chari, S Lakshmana Kumar, Naveen Pooja GLA University Department of Computer Engineering and Applications Mathura India Graphic Era University Dept of Computer Science & Engg. Dehradun India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engg Dept of Computer Science Engg Hyderabad India Lloyd Law College Greater Noida India Lloyd Institute of Management and Technology Greater Noida India

ISBN: (纸本)9798350304480

The Web of Vehicles (IoV), which utilizes portable correspondence advances, has acquired critical consideration since the improvement of smart terminals. High calculation intricacy, correspondence costs related with collaboration, and obliged network data transmissions the arrangement of inertness delicate IoV administrations with huge troubles. To tackle these issues, In uneven IoV conditions, we foster a cloud-edge helpful substance conveyance strategy to By offering the most ideal computational, reserving, and correspondence resources, network dormancy can be decreased. allocation to meet the severe idleness and dependability prerequisites of Web of Things (IoT) applications, a successful resource allocation is expected because of the Web of Things (IoT) gadgets'quick expansion, which has created an uncommon volume of information at the organization's edge. A powerful worldview for settling the issues with resource allocation in edge computing conditions is profound reinforcement learning (DRL). © 2023 IEEE.

关键词： Computing Edge IOT Reinforcement Resource Allocation

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Comparative Analysis of Deep Learning Algorithms for Image Recognition in Medical Imaging 2

Comparative Analysis of Deep Learning Algorithms for Image R...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Ashokkumar, S. Kumar, S. Magesh Sugumaran, D. Rajaraman, R. Saranya, N. Naga Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Tamil Nadu Chennai India

ISBN: (纸本)9798350383867

This research paper compares deep learning models through the imaging tasks of medical imaging. Modern-age architectures such as CNNs i.e., AlexNet, VGG, Resnet, Densensnet, and U-Net are the foundations of state of the art medical image analysis. They are applied to various tasks like detection of cancer, organ segmentation, and classification of diseases among others. Reports indicate that there are specific networks of neurons which perform optimization for specified applications in this sense of which the ResNet architecture is better than others in tumor detection and U-Net is better than SegNet in organ segmentation tasks. Leveraging the transfer learning techniques trained on the ImageNet, our approach to disease classification succeeded;in addition, we utilized ensemble methods that combined the predictions from multiple models, which were pretty effective. This research explores the choice and fine tuning deep learning strategies for medical image analysis and deals with a huge push to diversify imaging techniques diagnostics and treatment planning. ©2024 IEEE.

关键词： Convolutional neural networks

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Design and Evaluation of IoT Prototypes: Leveraging Test-Beds for Performance Assessment and Innovation 4

Design and Evaluation of IoT Prototypes: Leveraging Test-Bed...

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

作者： Bhadula, Shuchi Adnan, Myasar Mundher Kumar, Rakesh Kaliyaperumal, Gopal Rao, Bolleddu Devananda Boob, Nandini Shirish Graphic Era Deemed to be University Department of Computer Science & Eng Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Eng Najaf Iraq Gla University Department of Computer Eng. & Applications Mathura India New Horizon College of Eng Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Eng Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu India

ISBN: (纸本)9798331518981

The Internet of Things' (IoT) explosive growth has created new opportunities for creative solutions in a variety of sectors. However, since real-world surroundings are complicated and variable, creating and assessing IoT prototypes may be difficult. The usage of test beds as a vital instrument for evaluating performance and fostering creativity in the creation of IoT prototypes is the main topic of this research study. Test-beds allow developers and researchers to analyse critical performance parameters like latency, scalability, energy efficiency, and interoperability by offering repeatable, controlled settings that mimic real-world situations. This paper examines many IoT test-bed frameworks and their uses in a range of industries, such as industrial automation, smart cities, and healthcare. It also looks at methods for assessing the dependability and effectiveness of IoT devices and the difficulties brought about by the ever-changing nature of IoT ecosystems. Before deploying a prototype, developers may improve its scalability and robustness by iterating iteratively on test-beds. With a focus on their potential to support large-scale IoT deployments and address the emerging challenges of security, privacy, and data management, the paper concludes by discussing future trends in IoT prototype evaluation and the evolving role of test-beds in fostering innovation. © 2024 IEEE.

关键词： Scalability

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