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International Conference on computer and Information Technology (ICCIT)

作者： Syed Alif Ul Alam Saadia Binte Alam Sourav Saha Mahmudul Haque Rashedur Rahman Syoji Kobashi Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Graduate School of Engineering University of Hyogo Shosha Himeji Hyogo Japan

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters.

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Fake Profile Identification Using Machine Learning

Fake Profile Identification Using Machine Learning

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： T. Sudhakar Bhuvana Chendrica Gogineni J. Vijaya School of Computer Science and Engineering VIT-AP University Vijayawada Andrapradesh School of Computer Science and Engineering Ira A. Fulton Schools of Engineering Arizona State University United States Department Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Chhattisgarh

Online social networks have permeated our social lives in the current generation. These sites have allowed us to see our social lives differently than they did in the past. Nowadays we can connect with new friends and maintain relationships with them via social and personal activities become quite easy. Online Social Networks (OSN) are contributed in all areas such as Research in all domains, Job-related areas, Technology oriented areas, Health care, and business-oriented areas, Information gathering and data collection, and so on. One of the biggest problems on these social media platforms is fake profiles. Impersonating to be someone else and causing harm and defamation to the real person or advertising or popularizing removed propaganda on someone’s name to get more benefit is the motto of such profile creators. There have been many studies regarding these fake accounts and how can they be mitigated. Many approaches such as graph-level activities or feature analysis have been taken into consideration to identify fake profiles. These methods are outdated when compared to arising issues of these days. In this paper, we proposed a technique using machine learning for fake profile detection which is efficient. . The benchmark data set is collected and mixed with manual data first furthermore; a data cleaning technique is used to present the data more feasibly. Then the preprocessed data is used for model building with sufficient information such as profile name, profile ID name, number of followers, and so on. We added Cross validation process where many training algorithms are implemented on the given data and are then tested on the same data. Based on the experiments the RF classifier performed better than the other classification methods. The Random Forest classifier is used to forecast the profile whether is fake or genuine in an efficient way.

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LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment

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computers in Biology and Medicine 2024年 173卷 108382-108382页

作者： Sardari, Sara Sharifzadeh, Sara Daneshkhah, Alireza Loke, Seng W. Palade, Vasile Duncan, Michael J. Nakisa, Bahareh Research Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Geelong Vic Australia Department of Computer Science Swansea University Swansea United Kingdom School of Mathematics and Data Science Emirates Aviation University Dubai United Arab Emirates Centre for Sport Exercise and Life Sciences Coventry University Coventry United Kingdom

Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial–Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity. © 2024 The Authors

关键词： Convolution

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Chaotic Particle Swarm Optimization Based Shortest Path Routing in VANET

Chaotic Particle Swarm Optimization Based Shortest Path Rout...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R. Ramya A. Yazhini T. Geetha R. Sahila Devi Kavin K. S. S. Dhanalakshmi Department of Nano Electronics Materials and Sensors ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of Computer Science & Engineering College of Engineering Anna University Chennai India Department of Artificial Intelligence and Data Science Agni College of Engineering and Technology Chennai India Department of Computer Science and Engineering Rohini College of Engineering and Technology Palkulam India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The present study examines the Vehicle Ad hoc Networks (VANETs) exhibit unexpected behavior due to their high mobility and random network structure. Including ad hoc networks, a variety of problems, including repeated connected breakdowns, flexibility, multi-hop transmission of information, and information loss, impact Transmission Control Protocols (TCP). To overcome this problem, this paper suggests employing zone-based routing in VANETs while taking mobility into account. The routing technique is implemented with a hybrid optimization strategy. The most significant swarm intelligence (SI) algorithm for solving optimization issues is the chaotic particle swarm optimization (CPSO) algorithm. Due to its simplicity, which involves just a few parameters, CPSO has grown to be considered one of the most frequently used optimization techniques. When resolving challenging optimization problems, the routing algorithms experience high overhead or incompatibilities. A CPSO based shortest path routing method is presented for VANET that offers a high-quality path for connection among nodes, improving the distribution of packets percentage and preventing end-to-end delay, in order to enhanced routing effectiveness and throughput in prevailing approaches. These achievements show that CPSO performs adequately and is experiencing significant progress.

关键词： TCP Protocols Vehicular ad hoc networks Spread spectrum communication Routing Throughput Search problems

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ISSSR 2022 Keynote Speech II: Human-Machine Cooperation in Junior science Education: Development, Opportunities, and Challenges

ISSSR 2022 Keynote Speech II: Human-Machine Cooperation in J...

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International Symposium on System and Software Reliability (ISSSR)

作者： Liang Luo Assistant Director of National & Local Joint Engineering Lab of Next Generation Internet Data Processing Technology School of Computer Science and Engineering (School of Cyberspace Security) University of Electronic Science and Technology of China

ISBN: (数字)9781665453974

ISBN: (纸本)9781665453981

Human-Machine-Environment (HME) Tri-Co (Coexisting, Cooperative, Cognitive) Education is an indispensable prerequisite and the main content of Artificial Intelligence empowering junior science education. Moreover, it is also a critical requirement for improving the quality of education and promoting the high-end development of education information. Building a unique learning environment with scene perception and an intelligent cognitive process is a tremendous challenge for diverse needs in junior scientific learning scenarios. Therefore, it is necessary to study human-machine cooperation scenarios and design advanced software and hardware. This talk will introduce some of our recent work in affective analysis, multimodal affection data processing methods, and reliability and security of affective computing systems, etc. We will also discuss some directions that are worthy of further studies.

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Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting

arXiv

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arXiv 2024年

作者： Bilal, Iman Munire Fang, Zheng Arana-Catania, Miguel van Lier, Felix-Anselm Velarde, Juliana Outes Bregazzi, Harry Carter, Eleanor Airoldi, Mara Procter, Rob Department of Computer Science University of Warwick and Alan Turing Institute for Data Science and AI Coventry United Kingdom Faculty of Engineering and Applied Sciences Cranfield University United Kingdom Government Outcomes Lab Blavatnik School of Government University of Oxford Oxford United Kingdom

As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability. © 2024, CC BY.

关键词： Adversarial machine learning

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An Adaptive Explainable AI Framework for Securing Consumer Electronics-Based IoT Applications in Fog-Cloud Infrastructure

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Tripathy, Subhranshu Sekhar Guduri, Manisha Chakraborty, Chinmay Bebortta, Sujit Pani, Subhendu Kumar Mukhopadhyay, Sabyasachi School of Computer Engineering KIIT Deemed to be University Bhubaneswar India University of Louisiana at Lafayette USA Department of Electronics and Communication Engineering Birla Institute of Technology Jharkhand India Department of Computer Science Ravenshaw University Cuttack Odisha India Kausalya Ganga Bhubaneswar Odisha India Centre For Computational And Data Sciences Indian Institute of Technology Kharagpur Kharagpur West Bengal India

A prominent use case of consumer electronics-based Internet of Things (IoT) applications, focused on smart cities, is connected devices that enable cities to optimize their operations via access to high volumes of sensitive data. Yet, these devices commonly utilize public channels for data access and sharing, requiring consistent communication protocols and an Intrusion Detection System (IDS) with the aid of AI. However, most of them involve high computation and communication costs. They are not fully reliable, either. Also, AI-based IDS solutions are viewed as black boxes because they cannot justify their decisions. To resolve these issues, we have proposed a framework based on explainable artificial intelligence (XAI) for securing consumer IoT applications in smart cities. At the beginning of the protocol execution, the participants exchange authenticated data through the blockchain-based AKA procedure. Meanwhile, we adopt the Python-based Shapley Additive Explanation (SHAP) framework to explain and interpret the core features guiding decision-making. The working model of this framework depicts its validation with recent benchmark methods. IEEE

关键词： Energy efficiency

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Semantic Level Image Retrieval in Domain Adaptive Semantic Segmentation

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Procedia computer science 2024年 242卷 1163-1170页

作者： Siyu Zhu Fenfen Zhou Kai Li Haonan Wen Minghao Liu Computer School Beijing information science and technology university 102206 No.55 Taihang Road Changping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University 100101 Beijing China. College of Robotics Beijing Union University 100027 Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University 100871 Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences No.19(A) Yuquan Road Shijingshan District Beijing 100049 P.R. China

The traditional domain adaptation task is not sufficient in mining inter domain image context. We propose a solution from the perspective of image semantic level retrieval. Image semantic level retrieval is based on the high-level semantics of images, retrieving the similarity or correspondence between images in terms of content and scene between domains. Our work focuses on the meaning, structure, and contextual information expressed by the target image, and retrieves the most relevant image from the source domain to improve the accuracy of domain adaptation. Furthermore, in response to the negative transfer brought about by image retrieval, we will introduce a category level weight adjustment module to further improve the accuracy of semantic level retrieval and weaken the negative impact caused by low precision categories. Finally, we demonstrated the effectiveness of semantic level image retrieval through experiments.

关键词： Image retrieval Domain adaptation Semantic segmentation Transfer learning

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A multidimensional time series data on wireless network to improve the detection performance of unsupervised learning algorithms

A multidimensional time series data on wireless network to i...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Paul, Chayan Ranjith, S. Vijayashanthi, R.S. Begam, R. Benazir Sabitha, R. KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science AP Vaddeswaram India Jeppiaar Engineering College Semmenchery Department of Electronics and Communication Engineering Chennai600119 India S.A. Engineering College Department of Electronics and Communication Engineering Poonamallee Avadi Road Tamil Nadu Thiruverkadu600077 India Rajalakshmi Engineering College Department of ECE Tamil Nadu Chennai602105 India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In recent years, only with developments of cellular networking technologies as well as the implementation of 4G LTE networks, the skills and options provided to end-users had expanded to a level that seems to be limitless. Smart phone users who travel by public transportation rely on high-speed internet infrastructure and expect to have a stable, greater connectivity for the length of their journey. Because of the high requirements placed on wireless networks and the regular changes in the fundamental radio channel, customers frequently encounter rapid and surprising alterations in the connectivity reliability of their connections. The old traditional process of evaluating and inspecting infrastructure cellular technology should be no longer sufficient to fulfill the demands of the manufacturing industry. Consequently, an accurate mobile network cellular irregularity diagnostic method is required to monitor for problems that lead and to improving the effectiveness of device construction and operation. It is estimated that there are thousands of mistakes and exclusions in the current single-dimensional abnormality identification technique, and also that the present multifunctional abnormality diagnostic technique has a poor detection accuracy on multifunctional time series analysis. Multidimensional statistics depending on network service device abnormality classification optimization approach described in this work is predicated on machine learning that use limited of information to support in the instruction of tremendous unmonitored techniques, thereby enhancing the detection accuracy of unsupervised classification techniques. This paper conducts experimental studies to validate the functionality of the optimization techniques, and it shows a significant progress over the four generally used unmonitored techniques, that can greatly enhance the abnormality detection performance of the existing techniques, according to the authors. © 2022 IEEE.

关键词： Wireless networks

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Category re-balancing based domain adaptive semantic segmentation

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Procedia computer science 2024年 242卷 1155-1162页

作者： Siyu Zhu Fenfen Zhou Kai Li Minghao Liu Haonan Wen Computer School Beijing information science and technology university 102206 No.55 Taihang Road Changping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University 100101 Beijing China. College of Robotics Beijing Union University 100027 Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University 100871 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences No.19(A) Yuquan Road Shijingshan District Beijing 100049 P.R. China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing 100190 Beijing China

The semantic segmentation task faces the bottleneck of high manual annotation costs. Domain adaptive learning provides an effective solution through inter domain knowledge transfer. However, existing domain adaptive semantic segmentation tasks face the situation of category asymmetry between scenes when dealing with complex scenes. Existing methods lack handling of class imbalance in the prediction process. In domain adaptive semantic segmentation tasks, class imbalance limits the performance of inter domain transfer. Our work focuses on the impact of category distribution on domain adaptive semantic segmentation tasks in image pairs. Inspired by long tail learning, we divided categories into head, middle, and tail categories, and designed effective strategies to enhance the contribution of tail categories in domain adaptation tasks. We introduce the concept of category rebalancing into domain adaptive image matching, and our method is lightweight and effective, proving its effectiveness on general datasets.

关键词： Long-tail learning Domain adaptation Semantic segmentation Transfer learning

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