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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Sharmin Sharmin Ismail Ahmedy Rafidah Md Noor Habibah Ismail Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Computer Science Unit Centre of Foundation Studies Univesrsiti Teknologi MARA Dengkil Selangor Malaysia

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes with distinct resources working together to achieve a common objective efficiently. A critical issue is extending the network lifetime of WSNs, especially for data transfer, as sensor nodes depend on their internal batteries to carry out essential functions and communicate with one another. Efficient energy use per sensor is essential for ensuring the overall energy efficiency of the network. Clustering has been the most energy-efficient approach but lacking a method for selecting the most effective cluster head results in decreased data aggregation efficiency, leading to the increased power consumption of sensor nodes. The project aims to enhance energy efficiency and extend network service life by using Particle Swarm optimization (PSO) for clustering in WSNs. PSO is used to select the cluster head based on member node distance from the base station and remaining energy. The proposed PSO technique was tested against Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Zonal Stable Election Protocol Z-SEP, and Extended Stable Election Protocol (EZ-SEP) and found to be more effective in reducing energy usage and prolonging the network’s lifespan. The recommended technique can extend the service life of WSNs and reduce energy consumption, ultimately leading to more cost-effective and eco-friendly solutions for various applications such as environmental monitoring, surveillance, and healthcare.

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Gender Prediction Using Real-time Convolutional Neural Network

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Procedia computer science 2025年 258卷 497-506页

作者： Emmanuel O. Balogun Sunday Adeola Ajagbe Abidemi E. Adeniyi Temitope O. Olayinka Elijah Adetoyese Adeogun Gbadegesin Adetayo Taiwo Ogheneruona Maria Esegbona-Isikeh Pragasen Mudali ICT Unit Ajayi Crowther University Oyo Oyo Nigeria Department of Computer Engineering Abiola Ajimobi Technical University 200255 Nigeria Department of Computer Science Bowen University Iwo Nigeria Department of Computer Science University of Maine Orono United State Department of Computer Science Ladoke Akintola University of Technology Ogbomoso Nigeria School of Computing Science & Engineering University of Salford Manchester United Kingdom Amey Services Limited United Kingdom

This work presents real time gender prediction using Convolutional Neural Network. Automatic classification of gender has become an area that has garnered importance due to the emergence of breakthroughs in the world of computing particularly with the advent of machine learning and Artificial intelligence. The goal of gender prediction in computer vision involves accurate prediction of gender from visual data. Gender prediction is an indispensable biological metric as it plays a significant role in many human applications. The application varies from immigration, border access, law enforcement, defence and intelligence, citizen identification and banking. Image processing combined with machine learning algorithms have been employed to build solutions from image representation of biological attributes such as facial images, human skeletal radiographs (most notably skull and pelvic bones), gait, smiles and non-biological features such as social media activities, names and other means that could be employed to determine gender by extracting regions of interest, applying necessary filters and normalizing the matrix values obtained. For the purpose of this work, a total of 6760 hand-bone radiographs were acquired from the Radiological Society of North America Repository. The dataset was divided into 70% training datasets and 30% test dataset using Random Sampling Cross-Validation Method. The acquired data images were pre-processed using image cropping, histogram equalization and segmentation techniques. Performance evaluation was carried out on the developed system using the metrics: Accuracy, False Positive Rate (FPR), Recognition Time, Specificity and Sensitivity, these metrics were evaluated at threshold values of 0.25, 0.35, 0.5 and 0.75. Optimal values were gotten at optimum threshold value of 0.75, the values are; 4.79, 93.67, 95.21, 94.53 and 137.87 respectively for metrics (FPR, Sensitivity, Specificity, Accuracy and Time).

关键词： Convolutional Neural Network Gender Prediction computer Vision Image Processing Classifiers Image Recognition

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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A Model for Optimal Assignment of Non-Uniquely Mapped NGS Reads in DNA Regions of Duplications or Deletions 2

A Model for Optimal Assignment of Non-Uniquely Mapped NGS Re...

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2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Sinha, Rituparna Pal, Rajat K. De, Rajat K. Heritage Institute of Technology Department of Information Technology Kolkata India University of Calcutta Department of Computer Science and Engineering Kolkata India Indian Statistical Institute Machine Intelligence Unit Kolkata India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Massively parallel sequencers have enabled genome sequences to be available at a very low cost and price, which opened huge scope on analyzing human genome sequences from different perspectives, thereby the association of diseases with genetic alterations gets further enlightened. However, the sequencing process and alignment of NGS technology based short reads suffer from various sequencing biases which needs to be addressed. In this work, the mappability bias occurring with respect to repeat rich regions of the DNA have been addressed in a novel approach. A model has been designed which considers all non-uniquely mapped reads and performs a pipeline of computations to allocate the reads to an optimal location, due to which the precise detection of breakpoints in the region of duplications and deletions are obtained. In addition, the application of this model for mappability bias correction, prior to the detection of structurally altered regions of the genome, leads to a better sensitivity value. © 2022 IEEE.

关键词： DNA

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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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2023 IEEE EMBS Special Topic Conference on Data science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Plati, Daphni K. Konstantakopoulos, Fotios S. Kalatzis, Theofanis Manousos, Dimitrios Kassiotis, Thomas Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Forth Institute of Computer Science Crete HeraklionGR 70013 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy

ISBN: (纸本)9798350383386

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition;(ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital. © 2023 IEEE.

关键词： Hospitals

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Design and Implementation of a Hybrid Deep Learning Framework for Handwritten Text Recognition 3rd

Design and Implementation of a Hybrid Deep Learning Framewor...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Anand, Harshit Singh, Milind Rawade, Vivian Sahoo, Shubham Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Jadara Research Center Jadara University Irbid21110 Jordan

ISBN: (纸本)9789819708918

Text recognition technology has seen significant advancements in recent years, particularly with the use of Optical Character Recognition (OCR) to evaluate computer-generated text. However, there is much more work to be done in the field of Handwritten Text Recognition (HTR). The challenges posed by handwritten text, such as significant variations in strokes across writers, the vast variety of handwriting styles, human error and damages to the paper, present substantial difficulties in accurately identifying and recognizing handwritten alpha-numeric data. To address these challenges, we proposed a deep learning method that combines long short-term memory (MD-LSTM) and convolutional neural networks (CNN) architectures. This model can identify numbers and characters of the English language from input images. Based on MNIST dataset, bidirectional recurrent neural networks were used to construct the output sequence which was developed using the TensorFlow Framework. The accuracy for alphabets, numbers and alpha-numeric texts are 95.2%, 94.9% and 94.7% respectively. The mean character match index is computed to be 93.2%. The proposed model can substantially boost HRTs precision and efficiency making it more accessible. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Exploring the impact of stratification on the dynamics of bioconvective thixotropic fluid conveying tiny particles and Cattaneo-Christov model:Thermal storage system application

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Propulsion and Power Research 2024年第3期13卷 416-432页

作者： T.Oreyeni M.D.Shamshuddin A.M.Obalalu A.Saeed Nehad Ali Shah Department of Physical Sciences Mathematics Programme UnitPrecious Cornerstone UniversityIbadanNigeria Department of Computer Science and Artificial Intelligence(Mathematics) SR UniversityWarangalTelangana State 506371India Department of Mathematics Augustine University Ilara EpeLagosNigeria Center of Excellence in Theoretical and Computational Science(TaCS-CoE) Faculty of ScienceKing Mongkut’s University of Technology Thonburi(KMUTT)126 Pracha Uthit Rd.BangModThung KhruBangkok 10140Thailand Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh 11623Saudi Arabia

Stratified thermal storage promotes energy sustainability by storing excess energy during times of low demand for later use making it possible to integrate renewable energy sources like solar and *** communication discusses the significance of triple stratifica-tion using the Cattaneo-Christov model in the bioconvectiveflow of thixotropicfluid coexisting with nanoparticles and gyrotactic *** Cattaneo-Christov heat and massflux is incorporated into thefluid model allowing more accurate prediction of heat and mass phenomena in thefluid *** governing partial differential equations that describefluidflow are parametrized to yield an ordinary differential equation system by adopting suitable *** series solutions are obtained by applying the homotopy analysis method(HAM).The effects of relevant parameters on the various profiles are revealed and accurately *** is observed that amplified thermal stratification lowers the temperature of ***,Brownian motion is used to illustrate the random movement of small particles suspended in liquids,and it is envisioned that the concentration distribution is significantly influenced by the Brownian motion of nanoparticles.

关键词： Cattaneo-Christov model Stratification Bioconvection Nanofluid

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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

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computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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Counting of spanning trees in the corona edge product network 1

Counting of spanning trees in the corona edge product networ...

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1st IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Yakoubi, Fouad El Khattabi, Noussaima El Marraki, Mohamed Université Moulay Ismail Computer Science Faculty of Science and Technology Errachidia Morocco University Mohamed v Laboratory of Conception and Systems Faculty of Sciences Rabat Morocco University Mohamed v Lrit Associated Unit Cnrst URAC29 Faculty of Sciences Rabat Morocco

ISBN: (纸本)9798350344158

The problem of counting the spanning trees is related to various interesting problems. The number of spanning trees is applied in several practical areas. But computing the number of spanning trees in general networks using standard algorithms (Matrix tree theorem is the first method) is computationally arduous. In this paper, we aim to compute the number of spanning trees in the corona edge product, investigating an effective combinatorial method, providing analytic formulas for this product graph of some classes graphs. © 2023 IEEE.

关键词： Trees (mathematics)

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Application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society 3rd

Application of Gradient Boosting Classifier-Based Computatio...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kumar, Ashutosh Sinha, Abhigyan Bakshi, Tamoghno Choudhury, Sibashish Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Jadara Research Center Jadara University Irbid21110 Jordan

ISBN: (纸本)9789819708918

Currently, drug and alcohol addiction has become a major menace to society's youth. As responsible citizens of this country, we must act now to keep these young brains from succumbing to this lethal addiction. In this research, a method based on intelligent analytics is designed for forecasting the threat of drug addiction using machine learning algorithms. Initially, we identify several reasons for drug addiction by speaking with health professionals, individuals addicted to drugs, and reading related studies and articles. Then we gather information from both addicts and abstainers. After preprocessing the data, we use Random Forest, Naive Bayes, Adaptive Boosting, and Gradient Boosting machine and assess the interpretation of each of these classifiers using certain well-known performance criteria. Outcome with Gradient Boosting showed promising with the highest accuracy rate of 95.4% and minimum error rate of 0.0854. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive boosting

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