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Selfish Users in Listen-Before-Talk Co-existence of Cellular-Wi-Fi Networks: Counteraction Methods

Selfish Users in Listen-Before-Talk Co-existence of Cellular...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Saibharath S Sudeepta Mishra Gazal Arora Anamika Sharma Chittaranjan Hota Dept. of Computer Science and Information systems BITS Pilani Hyderabad Campus India Dept. of Computer Science and Engineering IIT Ropar Punjab India

5G operates in the unlicensed spectrum to expand its capacity. This spectrum provides more uplink and downlink frequency bands in 5G to satisfy ultra-dense and scalability requirements. Wi-Fi is a prominent wireless technology that operates in unlicensed bands. Due to the proximity of cellular and Wi-Fi channels, utilization of both Wi-Fi and 5G New Radio (NR) can cause interference. The co-existence of 5G NR and Wi-Fi devices lacks the means of communication for negotiation and coordination among them. Listen-Before-Talk and Duty-cycling are standard Medium Access Control (MAC) mechanisms to enable co-existence. Due to a lack of coordination, network utilization becomes unfair with the existence of selfish devices. The selfish users could maximize throughput and affect other Wi-Fi and NR users. Here, we investigate the effects of selfish nodes on this Wi-Fi Co-existence with 5G. These nodes disobey exponential backoff to boost their channel acquisition. We study the effect of misbehavior through the Order Gain metric. Through standard CSMA/CA settings with overall channel occupancy of 99.75%, we demonstrate that selfish users profoundly impact fair co-existence. We study the side effects through throughput, channel sensing, and acquisition for various backoff window access patterns. Finally, we go through counteraction methods to overcome the selfish user impact.

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Optimizing Endotracheal Suctioning Classification: Leveraging Prompt engineering in Machine Learning for Feature Selection

Optimizing Endotracheal Suctioning Classification: Leveragin...

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Activity and Behavior Computing (ABC), International Conference on

作者： Mahera Roksana Islam Anik Mahmud Ferdous Shahera Hossain Md Atiqur Rahman Ahad Fady Alnajjar Dept. of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Dept. of Computer Science and Digital Technologies University of East London London UK Dept. of Computer Science and Software Engineering College of Information Technology UAE University Al Ain UAE

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with require in-person assistance, to accurately identify nursing activities and assess the nursing trainees to help them become proficient. This paper addresses classifying activities in one such procedure, endotracheal suctioning, using skeletal keypoint data of the subject performing the procedure. A multi-step structured prompt engineering method was established and utilized on several LLMs to select or calculate key features from the data. Then the features are passed onto several tuned machine learning models to obtain results. A tuned XGBoost prevailed across all models, achieving 90% accuracy on the validation set.

关键词： Training Accuracy Computational modeling Machine learning Medical services Predictive models Feature extraction

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Sinhala Sign Language Learning System for Hearing Impaired Community

Sinhala Sign Language Learning System for Hearing Impaired C...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： Idushan K. A. S Dilshan P. G. T Jayasundera P. S Madhushan K. L. B Jenny Krishara Dinuka Wijendra Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-to-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users.

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Analysis of Defect Associated with Powder Bed Fusion with Deep Learning and Explainable AI 15

Analysis of Defect Associated with Powder Bed Fusion with De...

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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Pratap, Ayush Sardana, Neha Utomo, Sapdo John, A. Karthikeyan, P. Hsiung, Pao-Ann Indian Institute of Technology Dept. of Metallurgical and Materials Engineering Ropar Rupnagar India National Chung Cheng University Grad. Ins. Ambient Intelligence and Smart Systems Chiayi County Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9781665477123

Research into the detection, classification, and prediction of internal defects using surface morphology data of parts created via powder bed fusion-type additive manufacturing has become a hot topic in the previous decade thanks to the development of deep learning. However, there is no other evidence to evaluate the model other than accuracy and metrics. In this paper, a novel data set is compiled from various literature and other sources to evaluate the black box model using explainable artificial intelligence (XAI). The data set contains three major powder bed fusion defects: gas porosity, lack of fusion, and balling. The anomaly was initially found using convolutional neural networks (CNN) and transfer learning. Based on test data, a model comparison was performed to determine the best accuracy and an F1 score. VGG16 has outperformed all other models in terms of accuracy, with an F1 score of 98.6 percent. Further, the model has been compared with the existing state-of-the-art model for classification in the domain of powder bed fusion defects. Finally, VGG16 was employed to interpret and explain the test data set. The LIME explanations revealed that the feature predicted by the model was present in conjunction with the fault. As a result, we are confident that the proposed model with XAI would considerably improve the fairness and trustworthiness of the output result in the powder bed fusion field. This can also aid in the automation of additive manufacturing in the realm of Industry 4.0. © 2023 IEEE.

关键词： computer vision

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture

Multispectral Images and IoT Based Tea Plantation Monitoring...

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Advanced Research in Computing (ICARC), International Conference on

作者： K. P. W. D. V. Kariyawasam P. N. Kahandagamage M. R. R. Fernando J. M. Fernandopulle Vishan Jayasinghearachchi Kapila Dissanayaka Dept. of Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Computer Science Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments.

关键词： Scalability Plantations systems architecture Crops Real-time systems Internet of Things Resource management Reliability Monitoring Stress

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A Bi-LSTM Based Model for Social Media Extremism Detection in Context of Bangladesh

A Bi-LSTM Based Model for Social Media Extremism Detection i...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Sajib Saha Zahidul Islam Tanvirul Islam Md Monzur Morshed Tithee Chakma Mama Subhenur Latif Md Walid Bin Jashim Dept. of Computer Science & Engineering Daffodil Internation University Dhaka Bangladesh Department of Accounting & Information Systems University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s modern world, digital technology has advanced social networking with revolutionary changes. People use some most popular platforms like Facebook, YouTube and some other social media platforms to engage with each other for business, information sharing and so on. Attacking someone on social media platforms through comments, in response to their speech, has become a common occurrence in Bangladesh as well as worldwide. Very little research has been done in this related field for Bengali NLP. In this study we primarily focus on classifying these comments into two categories: extremist and non-extremist. Comments were collected from two of the most popular platforms, YouTube and Facebook, used in Bangladesh to build our "BEC (Bangla Extremist Comments)" dataset. To classify the comments, a CNN + Bi-LSTM hybrid model was employed considering extremist comments detection systems using deep learning-based sentiment analysis techniques. The proposed model achieved an accuracy rate of 85% in identifying the extremist comments. This research opens opportunities for future researchers to take advantage of contributing and collaboration in this field.

关键词： Deep learning Analytical models Sentiment analysis Video on demand Social networking (online) Computational modeling information sharing Collaboration Web sites Context modeling

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Securing Digital Media in the Age of AI and Deepfakes: A Hardware-Based Solution

Securing Digital Media in the Age of AI and Deepfakes: A Har...

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IEEE Southeastcon

作者： Eric Scott Sisson Steven C. Puckett Dept. of Electrical and Computer Engineering Mississippi State University Starkville AL USA Computer Science and Information Systems University of North Alabama Florence AL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The current capabilities of deepfake and Artificial Intelligence (AI) technologies make it difficult to distinguish what is original from what is fabricated. Electronic documents can be digitally signed to verify and authenticate their information, making them admissible as evidence in court. In contrast, images and videos from Internet of Things (IoT) devices-such as police body cameras, security cameras, and smartphones are susceptible to deepfake manipulation, which can undermine their authenticity in legal proceedings. This research hypothesizes that digitally signing image and video data utilizing hardware cryptographic coprocessors could render them admissible in court. Utilizing our processes will also identify if any manipulation of the image data has occurred. Our approach addresses a critical need in the justice system to combat the challenges deepfakes pose and could pave the way for enhanced trust in digital evidence, bolstering the integrity of the judicial process.

关键词： Deepfakes Law enforcement Media Cameras Hardware Cryptography Internet of Things Artificial intelligence Digital signatures Coprocessors

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Evaluating the Solutions to Predict the Impact of Lung Cancer with an Advanced Intelligent Computing Method 5

Evaluating the Solutions to Predict the Impact of Lung Cance...

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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Sundar, R. Ramadass, Sudhir Meeha, D. Subramanian, Balambigai Shankar, S. Siva Parasa, Gayatri Madanapalle Institute of Technology and Science Dept. of Computer Science and Engineering India Sterck Systems Chennai India Dr. Rangarajan Dr. Sakunthala Engg College Vel Tech Multi Tech Dept. of Information Technology Avadi Chennai India Dept. of Ece Kongu Engineering College Perundurai India Kg Reddy College of Engineering and Technology Dept. of Cse -AIandML Telangana Chilukuru India Koneru Lakshmaiah Education Foundation Dept. of Cse Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9781665474672

Using symptoms as a basis for diagnosing lung cancer, Lung cancer detection was accomplished using several different machine-learning regression strategies. By comparing the efficacy of several regression algorithms for predicting lung cancer, considering factors including age, gender, chest discomfort, shortness of breath, alcohol intake, chronic illness, trouble swallowing, anxiety, and peer pressure. Lung cancer predictions and evaluations are made using regression methods such as the linear algorithm, polynomial regression, logistic regression, logarithmic regression, and multiple regression. With a predictive accuracy of 96%, multiple regression is superior to other regression techniques when identifying future lung cancer cases. The r-squared value, which can be calculated using several regression machine learning approaches, may also be used to evaluate the association between the various symptoms and lung cancer. Lung cancer is diagnosed using the r-squared value, which is calculated using several algorithms and takes into symptoms, including chronic illness. © 2023 IEEE.

关键词： Forecasting

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Transformer-Based Ensemble Model for Binary and Multiclass Oral Cancer Segmentation

Transformer-Based Ensemble Model for Binary and Multiclass O...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Amira Hossain Anamul Sakib Al Shahriar Uddin Khondakar Pranta Jesika Debnath Md Tanvir Rahman Tarafder Sazzadul Islam Shamim Forhad Md. Redwan Ahmed Rezaul Haque Shafiur Rahman Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Administration International American University CA USA Dept. of Computer Science Wright State University Dayton OH USA Dept. of Information Technology Westcliff University Irvine California Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Early and accurate segmentation of oral cancer is essential for timely diagnosis and treatment. Traditional methods like visual inspections and biopsies are often subjective and costly, which can hinder early detection. To improve segmentation accuracy for binary and multiclass classification, we propose a transformer-based ensemble model that combines Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), Swin Transformer, and BEiT. This ensemble utilizes self-attention mechanisms for better feature extraction and spatial representation. Our study employs two datasets: the MOD dataset (463 images of oral diseases) and a histopathological dataset (1,224 images of oral squamous cell carcinoma and normal epithelium). We applied extensive preprocessing and augmentation techniques, such as grayscale conversion, binary thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance image quality and model generalization. The performance evaluation showed that our ensemble model outperformed individual architectures, achieving an Intersection over Union (IoU) of 0.9601 and a Dice Coefficient of 0.9598 for binary segmentation, and IoU of 0.9587 and Dice Coefficient of 0.9575 for multiclass segmentation. A comparative analysis with state-of-the-art models confirmed the effectiveness of our approach. These results demonstrate the potential of transformer-based ensemble learning for oral cancer diagnosis, presenting a scalable tool for clinical applications. Future work will focus on expanding dataset diversity, optimizing computational efficiency, and integrating real-time inference for improved usability in healthcare.

关键词： Image segmentation computer vision Adaptation models Visualization Accuracy Computational modeling Transformers Cancer detection Real-time systems Cancer

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