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6th International Conference on information technology Research, ICITR 2021

作者： Wickramasinghe, H.C.P. Thebuwana, T.D. Wijesinghe, G.K.H.S. Dissanayake, U.N. Kodagoda, Nuwan Suriyawansa, Kushnara Faculty of Computing Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665420006

Unemployment is a huge problem around the world because a lack of job opportunities. People are unable to find the job opportunities according to their preferences and qualifications. As a solution for this, many countries are attempting to empower self-employment. Most of current world problems have been solved using modern technologies. Therefore, the development of self-employment also can be achieved through modern technology. The objective of our proposed platform, HIRELANCER, is empowering self-employment using modern technologies. HIRELANCER is bringing the consumers, service providers, and suppliers into the same platform. HIRELANCER will consist of innovative features that go beyond comparatively to other platforms such as an advanced mechanism to find best suitable service providers/suppliers for the service, handling the virtual front-desk, cost estimation for the services prior to contacting a service provider, and advanced facility to find a suitable career path for the people who are seeking career guidance. This research paper discusses how the innovative features of HIRELANCER will be beneficial for consumers, service providers, and suppliers and ultimately achieve our main objective, which is empowering self-employment in Sri Lanka. © 2021 IEEE.

关键词： Employment

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Exam Cheating Classification Using a Deep Learning Approach

Exam Cheating Classification Using a Deep Learning Approach

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Bishaw, Molla Ejigu Salau, Ayodeji Olalekan Vats, Satvik Kaushik, Keshav Alemu, Wubie Belay Tin, Ting Tin Debre Tabor University Department of Information Technology Debre Tabor Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248007 India Amity School of Engineering and Technology Amity University Mohali Punjab India Debark University Department of Information Technology Debark Ethiopia Inti International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia

ISBN: (纸本)9798350379716

Exam cheating has become a significant concern in educational institutions due to its potential negative impact on academic integrity. Researchers have attempted to overcome the issue. However, the previous research did not use multiple cheating activities in their datasets and the attributes of the studies used were not strong enough to represent exam cheating, particularly about paper positions. The study utilizes the dataset containing cheating and non-cheating images to develop a model using deep learning approaches. The dataset consists of images collected from network colleges, confirming the presence of cheating and non-cheating instances. Preprocessing techniques, including image resizing, noise removal, histogram equalization, augmentation and feature extraction are applied to the dataset, and both original and augmented versions of the datasets are used in experimental studies. Different input image sizes are employed for the deep CNN, AlexNet, GoogLeNet, and ResNet architectures and evaluated for their performance in classification in a paper based exam cheating. As the experimental results demonstrate from the models used in this study the customized CNN model achieves the highest accuracy of 93% and exhibits excellent performance in terms of loss function and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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BloodCell-Net: A lightweight convolutional neural network for the classification of all microscopic blood cell images of the human body

arXiv

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arXiv 2024年

作者： Mondal, Sohag Kumar Talukder, Md Simul Hasan Aljaidi, Mohammad Sulaiman, Rejwan Bin Tushar, Md Mohiuddin Sarker Alsuwaylimi, Amjad A. Electrical and Electronic Engineering Khulna University of Engineering and Technology Bangladesh Khulna Khulna Bangladesh E-12/A Agargaon Dhaka1207 Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer science and Technology Northumbria University United Kingdom Electrical and Electronic Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University Bangladesh Department of Information Technology College of Computing and Information Technology Northern Border University Arar Saudi Arabia

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.25% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier's performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split t

关键词： Blood

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Joint Resource Allocation for IRS-Aided VLC Network with Energy Harvesting

Joint Resource Allocation for IRS-Aided VLC Network with Ene...

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IEEE International Conference on Communications Workshops, ICC

作者： Ding Yao Zheng Chang Geyong Min School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland Department of Computer Science University of Exeter Exeter U.K.

To satisfy the explosive growing traffic demands for high-speed service, visible light communication (VLC) has become a promising technology these years. This paper presents a novel intelligent reflective surface (IRS)-aided VLC network system, which takes into account the existence of both information users (IUs) for receiving data information and energy harvesting users (EHUs) for capturing light energy. We determine the IRS association, power allocation, as well as direct current (DC) offset distribution to maximize the sum rate at the IUs, subject to the energy constraints at EHUs. As a solution to such a complex non-convex optimization problem, we propose a two-stage alternating optimization algorithm by decomposing it into two subproblems, with each subproblem being solved iteratively. We solve the subproblem of IRS association with a heuristic method and tackle another subproblem of power allocation and DC offset distribution with a successive convex approximation (SCA)-based algorithm. Based on our numerical results, we conclude that the proposed algorithm is convergent, and a significant improvement in sum rate can be achieved by joint resource management while meeting certain requirement of harvesting energy. Moreover, IRS can reduce the sensitivity to obstacles and improve sum rate performance in VLC.

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Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

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computers, Materials & Continua 2021年第7期68卷 643-654页

作者： Belal Al-Khateeb Kawther Ahmed Maha Mahmood Dac-Nhuong Le College of Computer Science and Information Technology University of AnbarRamadiIraq General Directorate of Scientific Welfare Ministry of Youth and SportBaghdadIraq Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization(RHSO)inspired by the behavior of rock hyraxes swarms in *** RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this *** hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the ***-eight(22 unimodal and 26 multimodal)test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm.A comparative efficiency analysis also checks RHSO with Particle Swarm Optimization(PSO),Artificial-Bee-Colony(ABC),Gravitational Search Algorithm(GSA),and Grey Wolf Optimization(GWO).The obtained results showed the superiority of the RHSO algorithm over the selected algorithms;also,the obtained results demonstrated the ability of the RHSO in convergence towards the global optimal through optimization as it performs well in both exploitation and exploration ***,RHSO is very effective in solving real issues with constraints and new search *** is worth mentioning that the RHSO algorithm has a few variables,and it can achieve better performance than the selected algorithms in many test functions.

关键词： Optimization metaheuristic constrained optimization rock hyraxes swarm optimization RHSO

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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Selvapandian D Laxmi Raja Tharageswari K Senthilkumar VM Nikitha JC Hemalatha M Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India Department of Cyber Security Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Mahendra Institute of Technology Namakkal India Department of Information Technology Nandha Arts and Science College Erode India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%.

关键词： Accuracy Normalized difference vegetation index Satellites Machine learning algorithms Normalized difference water index Predictive models Data models Probability distribution Optical sensors Wetlands

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Exploring the RFID mutual authentication domain

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International Journal of computers and Applications 2021年第2期43卷 127-141页

作者： Mohsin, Syed Muhammad Khan, Imran Ali Abrar Akber, Syed Muhammad Shamshirband, Shahaboddin Chronopoulos, Anthony T. Department of Computer Science Virtual University of Pakistan Lahore Pakistan Department of Computer Science COMSATS Institute of Information Technology Islamabad Pakistan Wuhan China Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science University of Texas at San Antonio San AntonioTX United States Department of Computer Engineering & Informatics University of Patras Rio Greece

Mutual Authentication is the initial and the most significant line of defense in any open and trusted network system. Weaknesses in mutual authentication protocol may lead to eavesdropping, unauthorized access to the tag or reader data, Denial of Service attack, counterfeiting attack, de-synchronization attack, location tracking, cloning attack, timing attack, man-in-the-middle attack, spoofing attack, and many more. As a result of continuous focus on research of mutual authentication protocols for Radio Frequency Identification (RFID) systems, numerous solutions to address the security issues of RFID system have been proposed. A Comprehensive survey of existing state-of-the-art RFID mutual authentication protocols is presented in this paper. The ultimate objective of this survey paper is to categorize the existing RFID mutual authentication protocols on the basis of their working principle along with their strengths and weaknesses. This categorization of RFID mutual authentication protocols will be beneficial for new researchers to have a quick overview and understanding of the domain and will serve as an input to devise more effective and efficient algorithms for RFID mutual authentication. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Surveys

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Automatic Identification and Reconstruction 3D Image of the Rhinosinusitis Mucus

Automatic Identification and Reconstruction 3D Image of the ...

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International Conference of information and Communication technology (ICoICT)

作者： Ondra Eka Putra Sumijan Muhammad Tajuddin Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia Department of Computer Science Bumigora University Mataram Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

Rhinosinusitis Mucus is mucus produced by mucus glands found in the nose in the paranasal sinus area which indicates inflammation. Segmentation of rhinosinusitis mucus is very difficult to do because of the many characteristics of different objects in the image of the paranasal Sinus CT-Scan in separating the mucus object with its background before the extraction process. The purpose of this study was to accurately identify and measure the mucus area of rhinosinusitis from CT-scan images of rhinosinusitis patients. The test data used in this study is secondary data from CT-Scan of paranasal sinus sagittal view in radiology RSUP M. Djamil Padang, Padang City, West Sumatra, Indonesia, using the Philips Ingenuity CT CT-Scan model type MRC880 numbered Tube series 163889. The segmentation process is carried out to obtain the optimal multi threshold value from each slice of the test image using the Improved Adaptive Multi Threshold (IAMT) segmentation method which produces a binary image to separate the mucus object with its background before the extraction process. Furthermore, the results of IAMT are applied to the identification and extraction model automatically to obtain the area of the rhinosinusitis mucus object. The extraction results were used for 2D to 3D image reconstruction in calculating the volume of rhinosinusitis mucus objects. The test results showed that the smallest area of rhinosinusitis mucus was 0.516 cm 2 and the largest was 1.807 cm 2 , and the average volume was 0.95 cm 3 with Accuracy is 96,5%. The identified areas and volumes indicate inflammation in the area of the paranasal sinuses and require treatment. Thus, this study can help doctors in making the right decision to take further medical action.

关键词： Image segmentation Visualization Adaptation models Three-dimensional displays Computed tomography Urban areas Nose Radiology Object recognition Image reconstruction

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Animation as a Creative Industry: State of the Art 5

Animation as a Creative Industry: State of the Art

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5th IEEE International Conference on information technology, information Systems and Electrical Engineering, ICITISEE 2021

作者： Saputra, Dhanar Intan Surya Manongga, Danny Hendry, Hendry Satya Wacana Christian University Faculty Of Information Technology Department Of Computer Science Jawa Tengah Salatiga Indonesia

ISBN: (纸本)9781665401968

The creative economy created by creative activities has proven to have a very large role in global economic development. The creative economy or creative industry is almost the same, but each country has a different definition and different types of sub-sectors. From this diversity, Animation is included in the sub-sector of the creative industry in each country. In its development, Animation as part of the creative industry has its uniqueness. This study presents the State of The Art of Animation as part of the creative industry. It is based on the analysis and literature study, discussing several topics, namely computer Animation, including the development of animation technology. Then discuss the Animation Industry covers the supply chain and production process, and Animation as a Creative Industry discusses the business processes, especially in several developing countries and Southeast Asia. The findings demonstrate that animation as a creative industry offers an interesting landscape for animation productions, both in terms of outsourcing services and original Intellectual Property creations. © 2021 IEEE.

关键词： Animation

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Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Mueang Phayao Thailand Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

There have been several approaches for wearable fall detection devices during the last twenty years. The majority of technologies relied on machine learning. Although the given findings appear that the issue is practically addressed, critical problems remain about feature extraction and selection. In this research, the constraint of machine learning on feature extraction is addressed by including a hybrid convolutional operation in our proposed deep residual network, called the DeepFall model. The proposed network automatically generates high-level motion signal characteristics that can be utilized to track falls and everyday activities. FallAllD dataset, a publicly available standard dataset for fall detection that gathered motion signals of falls and other events, was utilized to analyze the proposed network. We performed investigations using a 5-fold cross-validation technique to determine overall accuracy and F-measure. The experimental outcomes show that the proposed DeepFall performs better accuracy (95.19%) and F-measure (92.79%) than the state-of-the-art baseline deep learning networks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fall detection

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