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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anusha, N. Vidhya, R. Jani Anbarasi, L. Dhanya, D. Pushparani, S. Mohan, Geetha Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Vellore Campus Vellore India Agni College of Technology Department of Information Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Institute of Applied Technology Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331504403

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network's ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of 97.16%, with a precision, F1-score and a sensitivity of 97%, 97% and 99.08% respectively. © 2024 IEEE.

关键词： Diseases

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Multi-Scale Network for Thoracic Organs Segmentation

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computers, Materials & Continua 2022年第2期70卷 3251-3265页

作者： Muhammad Ibrahim Khalil Samabia Tehsin Mamoona Humayun N.Z Jhanjhi Mohammed A.AlZain Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information systems College of Computer and Information SciencesJouf UniversityKSA School of Computer Science and Engineering(SCE) Taylor’s UniversityMalaysia Center for Smart Society 5.0(CSS5) Faculty of Innovation and Technology(FIT)Taylor’s UniversityMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Medical Imaging Segmentation is an essential technique for modern medical *** is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical *** significant successes have been achieved in the segmentation of medical images,DL(deep learning)*** delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT *** now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to *** segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL *** have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of *** methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background *** results showed that our proposed framework can be segmented organs accurately.

关键词： Deep learning convolutional neural network computed tomography organs at risk computer-aided diagnostic

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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Assessing Self-Efficacy Method for Enhancing Computational Thinking in Educational Game Environment 5

Assessing Self-Efficacy Method for Enhancing Computational T...

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5th technology Innovation Management and engineering Science International Conference, TIMES-iCON 2024

作者： Nobnop, Ratchanon Chondamrongkul, Nacha Temdee, Punnarumol Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (纸本)9798350362602

Self-efficacy is critical for improving computational thinking in gaming. However, conventional assessment methods like questionnaires can produce disengaged responses or require additional work to understand the questions and have limitations in the assessment of dynamic self-efficacy. To address this challenge, this study proposes a method using in-game features to assess self-efficacy, which can change over the playtime. The employed in-game feature is the Planning Stage, in which learners strategize and prepare their approach before engaging with challenges. We collect data on the time spent in this stage, which is newly proposed in this study. The experiment was conducted with twenty-five students in the Multimedia technology and Animation Major playing Tenacity-P to accomplish tasks from a computer programming course. To validate the method, we compared self-efficacy results from the game-based approach with those from conventional questionnaires. The findings highlight the need for strategic planning in managing difficult tasks. As challenges increased, performance measures varied more, underscoring the importance of strong problem-solving skills. Efficient strategic planning directly improved self-efficacy, as participants with better planning skills maintained higher self-efficacy despite increasing task difficulty. ©2024 IEEE.

关键词： Computational Thinking Educational game Game features Self-efficacy

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Deep Reinforcement Learning-based Computing Offloading and Resource Allocation Algorithm in Internet of Vehicles 11

Deep Reinforcement Learning-based Computing Offloading and R...

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2023 IEEE 11th International Conference on information, Communication and Networks, ICICN 2023

作者： Yuan, Yiping Chen, Xin Jiao, Libo Wang, Yijie Beijing Information Science & Technology University School of Control Science and Engineering Beijing China Beijing Information Science & Technology University School of Computer Science Beijing China

ISBN: (纸本)9798350314014

With the rapid emergence of the Internet of Vehicles (IoV) and 6G networks, the exponential growth in data traffic has presented a formidable challenge to the limited computational capabilities of in-vehicle devices. To overcome this challenge, mobile edge computing (MEC) offers a viable solution by offloading computationally intensive tasks to the edge servers. In this study, we design a novel joint optimization approach that leverages deep reinforcement learning (DRL) to inform decisions on computational offloading, energy consumption, and resource allocation. Our proposed scheme considers the involvement of both the base station (BS) and road side units (RSUs) in providing computing resources to the user while simultaneously minimizing energy consumption and reducing latency of computational tasks. Our simulations unequivocally establish the efficacy of the Deep Reinforcement Learning-based computing offloading and resource allocation (DCORA) algorithm. The proposed DCORA algorithm outperforms alternative baseline schemes by approximately 15% in direct comparison. © 2023 IEEE.

关键词： Reinforcement learning

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Multi Label Classification Based on Ensemble Learning: Investigative Study 25

Multi Label Classification Based on Ensemble Learning: Inves...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Hadi, Wael Samara, Ghassan Al Daoud, Essam Owaida, Suhaila Abu Elrashidi, Ali Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan Zarqa University Faculty of Information Technolog Department of Computer Science Zarqa Jordan Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of It Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique. © 2024 IEEE.

关键词： Contrastive Learning

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Evaluation and Inference of a Proposed, Improved and Efficient W Model for Software Testing 15

Evaluation and Inference of a Proposed, Improved and Efficie...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Velan, Senthil S. Velan, Sivaranjani Senthil Rubini, P. Sudhakar, K. Information Science and Engineering CMR Institute of Technology Bengaluru India Computer Science and Engineering MVJ College of Engineering Bengaluru India School of Engineering and Technology CMR University Bengaluru India Artificial Intelligence and Data Science NITTE Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350370249

Software Testing is an important and measured/outcome-oriented field that requires an in-depth analysis for developing new methodologies. This enables the development of high-quality end product resulting in fewer maintenance schedules. Several models have been proposed by research teams of different companies leading to improved development and testing schedules. Even with the availability of these models, a significant allocation of the budget has been dedicated purely on the maintenance of software developed by the IT industry. In order to address this need for improved process models of testing, a new exclusive and efficient W model has been proposed and explained in this paper. This model can improve the quality of end product, namely the high-quality software and further reduce the cost involved for the maintenance. Based on the quantitative measurement the proposed W software testing model, it was found the small scale software developed by adopting this method produces high-quality software with 50% less errors/faults compared to development using classical life cycle model. ©2024 IEEE.

关键词： computer software selection and evaluation

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