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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajaram, Gnanajeyaraman Archana Vishveswari, R.S. Deepa, R. Packialatha, A. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Machine Learning Tamil Nadu Chennai India Varuvan Vadivelan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Dharmapuri India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Mobile Ad hoc Networks (MANETs) have been broadly functional in a wide variety of scenarios, for example, in disaster recovery, health care, video conferencing, and battlefield transmissions. MANET is an independent and peer-to-peer network which builds a chain of nodes to enable data transmission from sender to receiver. Hop count is measured as the most significant metric in MANET. Though several metrics have been introduced as the replacement for the hop count metric, there is a need for outperformance in mobile scenarios. The node mobility will affect the link stability, and it creates congestion. To solve these issues, a Node Mobility and Encounter Rate metrics (NMER) to enhance the stability of the network. It selects the route based on the node mobility and Encounter Rate (ER) metrics. The congestion ingredient measures the congestion level in the MANET. The objective of this route has the least cost for forward data packets and minimizes congestion. The NMER approach simulation results demonstrate that the NMER approach minimizes the packet loss ratio and reduces the network delay. © 2023 IEEE.

关键词： Mobile ad hoc networks

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From Floats To Posits: A Conversion Framework

From Floats To Posits: A Conversion Framework

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2023 Congress in computer science, computer engineering, and applied Computing, CSCE 2023

作者： Vuthaj, Andrin Alhajjar, Elie United States Military Academy Department of Electrical Engineering and Computer Science West PointNY United States Rand Corporation Department of Engineering and Applied Sciences ArlingtonVA United States

ISBN: (纸本)9798350327595

Modern Artificial Intelligence (AI) applications for mobile and embedded applications are shifting away from the IEEE floating point system due to its many inefficiencies. Recently, a new data type called posits was introduced as a potential replacement for IEEE-754 Standard floating point numbers (floats). Posits are a core component of the third generation universal numbers (type III unums). They provide compelling advantages over floats, including larger dynamic range, higher accuracy, better closure, bitwise identical results across systems, simpler hardware, and simpler exception handling. In this paper, we aim to provide a framework for an efficient conversion between the two systems. Namely, we devise a novel conversion algorithm that compares accuracy between representations in terms of computational error and highlights the superiority of the new type over the currently used number systems. © 2023 IEEE.

关键词： Decimal Representation Floating Point IEEE Standard 754 Posit Arithmetic Precision

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Stacking Classifier-based Automated Insurance Fraud Detection System

Stacking Classifier-based Automated Insurance Fraud Detectio...

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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Shanthini, M. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665477192

During the Covid-19 pandemic, the insurance industry's digital shift quickened, resulting in a surge in insurance fraud. To combat insurance fraud, a system that securely manages and monitors insurance processes must be built by combining a machine learning classification framework with a web application. Examining and identifying fraudulent features is a frequent method of detecting fraud, but it takes a long time and can result in false results. One of these issues is addressed by the proposed solution. By digitalizing the paper-based workflow in insurance firms, this paper intends to improve the efficiency of the existing approach. This method also aimed to improve the present approach's data management by integrating a web application with a machine learning stacking classifier framework experimented on a linear regression-based iterative imputed data for detecting fraud claims and making the entire claim processing and documentation process more robust and agile. © 2022 IEEE.

关键词： Information management

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Topological Nonlinear Optics in Twisted h-BN Interface

Topological Nonlinear Optics in Twisted h-BN Interface

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Ni, Xueqi Zhang, Mingjie Lou, Beicheng Fan, Shanhui Mazur, Eric Cao, Yuan Tang, Haoning School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States

Our research demonstrates tunable second harmonic generation in quantum material heterostructures, revealing nontrivial topological properties in their nonlinear optical responses with potential applications in quantu... 详细信息

ISBN: (纸本)9781957171395

关键词： Nonlinear optics

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Uncovering Deception: A Study on Machine Learning Techniques for Fake News Detection 7th

Uncovering Deception: A Study on Machine Learning Techniques...

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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Zafar, Mohd Faiz Rawat, Nikhil Mishra, Rishant Shekhar Pandey, Purnendu Kshetri, Naresh Department of Computer Science Engineering KIET Group of Institutions Ghaziabad India Department of Math CS & IT Lindenwood University St. CharlesMO United States

ISBN: (纸本)9789819951659

As communication advances and social media is growing day by day, the dessimation of fake news is rapidly increasing. This is a rising area of research that has received a considerable amount of attention, with certain constraints making it difficult to progress. This paper examines a machine learning-based technique for identifying deceptive news stories. For feature extraction, Term Frequency-Inverse Document Frequency (TF-IDF) was applied, and Logistic regression, Naïve bias, and Passive aggressive were employed as the classifier. Results obtained demonstrate that Logistic regression (because it can take a set of features that allows for the easy threshold to make a binary decision (real vs fake) and the ability to handle non-linearly separable data) gives the best accuracy as compared to Naïve Bayes and Passive aggressive classifier. With the help of Logistic Regression, the model has achieved an accuracy of 97.9% also this paper proposed the development of an android-based application that will work as a front-end application for fake news detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Text processing

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Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

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computer Systems science & engineering 2023年第3期44卷 2195-2207页

作者： Ashit Kumar Dutta Nazik M.A.Zakari Yasser Albagory Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Nursing College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly *** affect their quality of life drastically and millions of seniors are diagnosed with PD every year *** models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,*** identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of *** recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify *** this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of *** goal of the proposed CBO-OKELM technique is to identify whether PD exists or ***-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of *** addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of *** algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic *** experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.

关键词： Parkinson’s disease colliding bodies optimization algorithm feature selection metaheuristics classification kelm model

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Forest Fire Detection using computer Vision 3

Forest Fire Detection using Computer Vision

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Prasanna Rahavendra, A. Praneash, G.P. Rashmika, T. Natarajan, Aravindhraj Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Every year, approximately 70,000 wildfires occur around the world. Forest fires can have a variety of negative effects on forest cover, soil, tree development, vegetation, and general flora and fauna. Fires destroy several hectares of forest and leave behind ash, rendering it unfit for vegetation growth. Animal habitats are destroyed by the heat created by the fire. Preventing fire disasters and saving people's lives and property requires early detection of fires before they become catastrophic. These forest fires must be detected and put out as early as possible. In this project a computer vision based deep learning model is proposed that can detect and alert in-case of forest fires. The model which is used here is called MobileNet Architecture which employs depth-wise separable convolutions and has greater advantages than using other currently existing models. The model is trained from a dataset that was scrapped from the internet. The computer vision model is built utilizing the packages in Python 3 such as NumPy, OpenCV, Pillow, Matplotlib, and TensorFlow. This method is created by training and testing the model to identify failures and establish recovery strategies. As a result, these computer vision-based systems can provide a warning at an early stage of fire, which is critical for forest fire early warning. © 2022 IEEE.

关键词： Deforestation

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Automated Fracture Detection and Classification Using Siamese Neural Networks

Automated Fracture Detection and Classification Using Siames...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Anderson, K.L. Engelbrecht, A.P. Birkholtz, F.F. Stellenbosch University Department of Computer Science Stellenbosch South Africa Stellenbosch University Computer Science Division Department of Industrial Engineering Stellenbosch South Africa Gulf University of Science and Technology GUST Engineering and Applied Innovation Research Centre Kuwait Institute of Orthopaedics and Rheumatology Mediclinic Winelands Orthopaedic Hospital Stellenbosch University Stellenbosch Division of Orthopaedic Surgery Department of Surgical Sciences Stellenbosch South Africa

ISBN: (纸本)9798350369106

This study aims to address the critical need for accurate and efficient fracture detection and classification in medical radiography, by leveraging recent developments in deep learning techniques. A two-stage pipeline is proposed combining a cutting-edge convolutional neural network (CNN), You only look once (YOLOv9), for wrist fracture detection using a Siamese neural network (SNN) for fracture type classification. The proposed methods aim to overcome the limitations of a single-stage approach by optimizing detection and classification stages independently. The results obtained demonstrate the efficacy of the two-stage pipeline in improving accuracy and reducing class imbalance issues, highlighting the potential of artificial intelligence (AI) driven solutions in enhancing patient care and reducing diagnosis time. © 2024 IEEE.

关键词： Convolutional neural networks

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Modified Integrated Regulator Strategy for High Density Interleaved DC-DC Converter with Enhancement Conversion Ratio and Losses Mitigation 50

Modified Integrated Regulator Strategy for High Density Inte...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Karimi, Mohsen Farsijani, Mohammad Jamil, Mohsin Iranian Research Institute for Electrical Engineering Academic Center for Education Culture and Research Tehran Iran Department of Electrical and Computer Engineering and Applied Science Memorial University of Newfoundland Newfoundland St John’sNL Canada Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL United States

ISBN: (纸本)9781665464543

The high power DC-DC converters are widely used in industrial and renewable application due to variable dc source. For this purpose, the interleaved dual cascade DCDC converter (IDCC) is the most advantageous due to its and low ripple with high boosting factor and high potential efficiency. To optimize IDCC performance, an integrated regulator ensures equal current sharing between levels, with voltage cascading to boost the output voltage. The integrated regulator technique (IRT) is designed to control all regulators at each level to maintain an optimal switching period, thereby reducing switching dissipation and decreasing ripple. Its ability to handle high voltages conversion with a consistent low ripple output and modular structure enhance performance. It has demonstrated a 1.5% current ripple and up to 94% efficiency in high-power density applications. Simulation and experimental results of a 2 kW prototype validate the IRT's performance. © 2024 IEEE.

关键词： DC-DC converters

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