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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Roomi, S.Mohamed Mansoor Maheswari, P.Uma Shanmugavadivu, P. Priya, K. Thiagarajar College of Engineering Department of Ece Madurai India Velammal College of Engg. and Tech. Department of It Madurai India The Gandhigram Rural Institute Dept. of Computer Science and Applications India

ISBN: (纸本)9798350399646

Forensic Odontology is a specialised field of victim identification that includes the identification of salvaged humans as well as the proof of identity including whole or scrappy bodies. Contour information of dental x ray images provides better matching for person identification. In this paper, we propose an automatic Wavelet Sliding Neighborhood (WSN) method for human forensic identification which comprises of preprocessing, integral intensity projection, contour extraction and shape matching. The first step preprocessing is used to remove the noise present in the dental x ray or radiographic images. The second step entangles integral intensity projection for untying the lower jaw, upper jaw and individual teeth. Third stage uses wavelet based sliding neighborhood to extract the shape of the dental image without any discontinuities in the edges of an image. The final stage, shape matching is used for estimating the distance measure for matching ante-mortem (AM) and post-mortem (PM) dental records. The experimental results demonstrate that our proposed WSN method outperforms and achieves a highest similarity than the existing approaches with a computation time of 1.25s. © 2023 IEEE.

关键词： Extraction

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Optimized Deep Neural Network for Accurate Detection of Malignant and Benign Brain Tumors 3

Optimized Deep Neural Network for Accurate Detection of Mali...

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

作者： Visumathi, J. Kalaivani, N. Hemanth, S.V. Balasubramanian, K. Karthikeyan, S. Amirthayogam, G. Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai600062 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Mathematics Tamilnadu Chennai600062 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India Kalasalingam Academy of Research and Education Deemed to be University Department of Computer Applications Tamil Nadu Krishnankoil626126 India Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai606603 India Hindustan Institute of Technology and Science Deemed To Be University Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

The rapid development of medical technology has ushered in a new age of crucial medical records. A brain tumor is an aberrant mass of rapidly expanding brain cells, some of which have the potential to metastasize. Brain tumors are often diagnosed in clinical practice using magnetic resonance imaging (MRI) scans. These tumors appear as clusters of irregular cells that could endanger patients by invading nearby tissues. However, manually spotting and segmenting brain tumors via MRI scans present complex challenges and are prone to mistakes. Thus, analysis of such data has influenced the prognosis, monitoring, diagnosis, and treatment of tumor-related conditions. To overcome these issues, we propose an Optimized Deep Neural Network (ODNN) method to accurately predict benign or malignant brain tumor diseases. Moreover, pre-processing uses an Adaptive Histogram Contrast Normalization (AHCN) technique to enhance the distribution and contrast of MRI image density. Afterward, features can be extracted using a Texture-Based Gray Level Co-Occurrence Matrix (TGLCM) method to separate the dark and bright parts of the image. Finally, a Machine Learning (ML) based ODNN method is employed to classify benign or malignant diseases. Utilizing sensitivity, specificity, recall, and accuracy metrics, the simulation results of the recommended ODNN approach are contrasted with those of preexisting approaches. © 2024 IEEE.

关键词： Deep neural networks

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A Deep Convolution Network-Based Pneumonia Identification from Thoracic X-Ray Imagery Scans 6th

A Deep Convolution Network-Based Pneumonia Identification fr...

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6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022

作者： Upreti, Kamal Mishra, Prateet Maheshwari, Manish Vats, Prashant Dhankar, Aisha Batra, Reenu Yadav, Saneh Lata Chawla, Tanvi Bhardwaj, Jayant Department of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management Delhi New Delhi India Department of Computer Science and Information Technology Symbiosis University of Applied Sciences MP Indore India Department of Computer Science and Applications Makhanlal Chaturvedi National University of Journalism and Communication MCNUJC Madhya Pradesh Bhopal India Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India Department of Electronics and Communication Bhagwan Parshuram Institute of Technology GGSIPU New Delhi India

ISBN: (纸本)9789811976599

Pneumonia is an infectious ailment which affects the respiratory system and is caused by microbes that infect the human by affecting their lung air sacs and fill them with fluid. Using chest X-ray is the most popular approach for detecting pneumonia, and the results must be calculated and examined by medical experts. The inconvenient way of detecting pneumonia results in a loss of human life owing to incorrect approach and treatment. With the advancement of computer and information technology, developing an autonomous system that can help in the diagnosis and prevention of pneumonia is now viable, especially if the patient is in a place where the medical facilities and expert doctors are limited. In order to solve this challenge, this project uses deep learning technologies. The neural network was customized to accomplish the difficult task of detecting diseases such as pneumonia in order to aid medical experts in their diagnosis and treatment options. CNN applies a filter to a photo in order to extract data from a chest X-ray. The above-mentioned goal was achieved using Python programming language, as well as deep learning and neural network methodologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chest X-rays Deep CNN Recognition of pneumonia Rectified linear function Thoracic X-ray component

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FAOACA-SND: Fuzzy selfish node detection employing arithmetic optimization algorithm and cell automata in DTN

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Measurement: Sensors 2024年 31卷

作者： Sharma, Rakhi Dinkar, Shail Kumar Haryana Radaur India Department of Computer Science Uttarakhand Technical University Dehradun India Department of Computer Science and Applications Govind Ballabh Pant Institute of Engineering and Technology Garhwal Uttarakhand India

In recent years, the number of smart devices has grown exponentially. However, the security and privacy of these devices are often neglected, which can lead to serious consequences. In this paper, we propose a method for detecting selfish nodes in DTN. Our method is based on analyzing the behavior of nodes in the network and identifying those that are not following the protocol. Cluster read selection using the hybrid optimized algorithm and cellular automata is used as the first phase of selfish node detection. The second phase uses fuzzy logic to categorize the nodes as selfish or cooperative. Fuzzy logic receives four network parameters from the simulation in MATLAB in a dynamic environment. The four network parameters are packet drop, average delay, residual energy, and nodes' reputations. The simulation results depict that the proposed method is a very accurate way to find selfish nodes. In the proposed mechanism, the proportion of false positives has decreased by 61.53 % compared to the existing mechanism. © 2023

关键词： Internet of things

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UAV Databased Temperature Patterns Analysis with Carbon Emission Detection Using Deep Neural Network

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 512-523页

作者： Mohanty, Sachi Nandan Dash, Bibhuti Bhusan Shanmugasundar, G. MGM, Johar Aswani, Inakollu Sundaram, Ajith Varghese, Issac K. School of Computer Science & amp Engineering (SCOPE) VIT -AP University Andhra Pradesh Amravati India School of Computer Applications KIIT Deemed to be University Bhubaneswar India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai 600044 India Management and Science University Shah Alam Selangor Malaysia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist.Andhra Pradesh Vaddeswaram 522302 India Amrita School of Business Kochi Amrita Vishwa Vidyapeetham Amrita Nagar Edappally Kerala Ernakulam 682041 India T A Pai Management Institute Manipal Academy of Higher Education Manipal India

Unmanned aerial vehicle (UAV) imaging methods have drawn a lot of interest lately from academics and industry professionals as an affordable option for agro-environmental uses. To improve the UAV capabilities of diverse applications, machine learning (ML) methods are specifically applied to UAV-based remote sensing data. Spatiotemporal properties were analysed and the city-level carbon emissions statistics were estimated. This research proposes novel technique in UAV-based climate temperature pattern analysis and carbon emission detection utilizing the deep learning (DL) model. Here the input is collected as UAV-based weather data which is processed for noise removal and smoothening. Processed data is extracted and classified utilizing Gaussian belief deep neural network and spatial convolutional Q-swarm colony metaheuristic optimization. A metaheuristic optimisation algorithm uses gradient information-free iterative evaluation of the objective function in order to identify a global optimum. Experimental analysis has been carried out in terms of detection accuracy, average precision, F-1 score, recall and AUC for various UAV-based weather dataset. The proposed technique attained mean average precision was 92%, recall was 94%, AUC was 87%, detection accuracy was 96% and F1-score was 93%. This work shows that remotely sensed data can be used to support more advanced evaluations that are more successful, particularly in areas with extensive selective logging and diverse forest conditions, and to help quantify carbon emissions from selective logging using conventional methodologies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Carbon emission detection Climate temperature Deep learning model Pattern analysis UAV

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Crude Oil Price Forecasting Using Hybridization of Optimized Deep Learning and Shallow Machine Learning Models 3rd

Crude Oil Price Forecasting Using Hybridization of Optimized...

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3rd International Conference on Computing, Communication and Learning, CoCoLe 2024

作者： Purohit, Sourav Kumar Panigrahi, Sibarama Jena, Aditya Narayan Department of Computer Science Engineering and Applications Sambalpur University Institute of Information Technology Jyoti Vihar Odisha Burla768019 India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela769008 India

ISBN: (纸本)9783031790409

Accurate forecasting of Crude oil price (COP) is paramount for financial markets, energy sector stakeholders, and a nation’s economy. The COP is affected by periodic, aperiodic, and sporadic factors like geopolitical events of importing and exporting countries. Therefore, accurate forecasting of COPs is imperative and challenging. Motivated by this, in this project, extensive studies are made to make point and interval forecasting of COPs employing machine learning models. Optimized deep learning models are used, and deep features are extracted. The deep features are transformed using principal component analysis (PCA). The transformed features are modelled using shallow machine learning models. Once the point forecasts are obtained, error modelling using different distributions is performed to compute the interval forecasts at different significance levels. Several alternatives are considered in optimized deep learning models, machine learning models and distribution functions to make the methodology reliable and robust. Since the machine learning models are stochastic, the simulations are repeated, and the mean values are considered from different point and interval forecasting methods for drawing reliable conclusions. Simulation results suggests that the GRU-Linear Regression model with optimized hyper-parameters provides the best in MAE and MASE. At the same time, the CNN-Huber Regressor provides the best RMSE and SMAPE than all other alternatives. . © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Traffic Congestion Control System Using Machine Learning

Traffic Congestion Control System Using Machine Learning

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Rohilla, Charu Kumar, Krishan Rohilla, Neha Manav Rachna International Institute of Research & Studies Faculty of Engineering & Technology Department of Computer Science & Engineering Faridabad India Vaish Arya Kanya Mahavidyyalaya Department of Computer Applications Bahadurgarh India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

The aim of this paper is to introduce the traffic congestion control system by using machine learning technology. Machine Learning is the property by which a computer system act without being explicitly programmed. It can be supervised, unsupervised & reinforcement learning. Traffic congestion is a critical problem in many areas around the world. Many policies have been implemented to reduce traffic congestion This paper will discuss various work done on traffic congestion control & proposed new traffic congestion control system. For the system implementation Python programming will be used. © 2022 IEEE.

关键词： Traffic congestion

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IoT-Enabled Asset Management and Tracking in Organizations 2

IoT-Enabled Asset Management and Tracking in Organizations

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kumar, Brajesh Anjali, Y. Mudholkar, Megha Mudholkar, Pankaj Bharati, P. Vijaya Abdullayeva, Nilufar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India Mlr Institute of Technology Department of Cybersecurity Hyderabad India Marwadi University Department of Computer Engineering Gujarat Rajkot360003 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India Vignans Institute of Engineering for Women Department of Cse Andhra Pradesh Visakhapatnam530049 India Tashkent State University of Economics The Department of Budget Accounting and Treasury Tashkent Uzbekistan

ISBN: (纸本)9798350364699

The manner in which assets are monitored, controlled, and utilized has been substantially altered by organizations that have implemented IoT-enabled asset management and monitoring technologies. Businesses can achieve improved operational efficiency and cost savings by perpetually monitoring the location, activities, and performance of their assets through the use of IoT technologies. Minimized periods of inactivity and enhanced asset lifecycle management are among the three benefits of this predictive maintenance approach. When it comes to resource allocation and strategic decision-making, the data insights provided by IoT-enabled devices are of paramount importance. Companies that implement these technologies should anticipate an increase in productivity, a decrease in losses, and an improvement in their ability to comply with regulatory standards. © 2024 IEEE.

关键词： Predictive maintenance

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Embedded Cognition in Virtual Environments: An Ecological Approach to AI Study

Embedded Cognition in Virtual Environments: An Ecological Ap...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Chung, Jun-Ki Singh, Bharat Sengar, Alok Singh Singh, K. Ranjit Mukuntharaj, C. Karnik, M.P. University Kwangshin University Institute for Pietatis Theologia Metropolitan City Gwangju Korea Republic of Iimt University School of Computer Science & Applications Uttar Pradesh Meerut India Ccsit Tmu Uttar Pradesh India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350344387

In order to investigate artificial intelligence (AI) over the long run and scale it up, this research article suggests using virtual reincarnation in video games. By using the realistic and engaging features of virtual worlds, it is intended to create AI systems they may comprehend and reason at a level comparable to that of human beings. The study makes the case that conventional methods of AI, which concentrate on single-sense jobs, are constrained in their capacity to produce all-encompassing intelligence. Virtual the incarnation, on the other hand, enables multi-modal learning as well as progressive evolution, offering an infrastructure for moral white box experimentation and the investigation of intricate moral dilemmas in AI. The research emphasizes the significance of ground semantic in virtual vision and the need for more sophisticated and interactive game experiences. © 2023 IEEE.

关键词： Artificial Intelligence Depth of online worlds Ethical Whitebox testing Gradual Innovation Machine Intelligence Online Gaming Virtual Learning

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OMN-IBUS Type of Networking System for the Communication System in 6G 4

OMN-IBUS Type of Networking System for the Communication Sys...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Devi, A. Sangeetha Bhatt, Deepanshu Bansal, Saloni Keerthika, A. Mohanraj, T. Santhosh, S. Tamil Nadu T.M.Palayam India Ies Institute of Technology and Management Ies University Department of Mechanical Engineering M.P Bhopal India Gla University Department of Computer Engineering and Applications Mathura India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India TN India Einstein College of Engineering Department of Electronic and Communication Engineering Tamil nadu India

ISBN: (纸本)9798350360165

The scientific community and industry have been working together in recent years to shift computational resources off centralized cloud systems to decentralized computing, or 'edge' computing. This change is a result of the cloud system's inability to keep up with the growing demands of emerging relationships, which are marked by the spread of time-sensitive applications like sophisticated robotics, augmented reality/virtual the truth technologies, self-driving cars, and vital remote control of Internet of Things applications. Decentralized computing at the border is still in its infancy and lacks a full platform, despite the critical role that it will play in future distributed systems. This article presents OMNIBUS, a novel decentralized edge architecture. This approach uses moving vehicles as resources for processing and storage, enabling the continuous delivery of computational capability to end-devices across many locations. Scalability and flexibility are two characteristics that set OMNIBUS distinct from other edge computing solutions. The suggested method, which is based on mathematical models of prediction, determines computational capacity for particular locations by learning the direction and time of vehicle movements. It also presents a theoretical foundation for learning, improvement, and caching amid dynamic conditions caused by moving vehicles, specifically for sequential concurrent conversion. OMNIBUS's scalability allows for almost infinite growth, which might result in a significant increase in network speed. © 2024 IEEE.

关键词： Scalability

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