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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Meghana, J.H. Chethan, H.K. Santhosh Kumar, K.S. Prakash, S.P. Shiva Department of Studies in Computer Science University of Mysore Mysuru Karnataka570006 India Department of Computer Science and Engineering Maharaja Institute of Technology Thandavapura Mysuru571302 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India

Yoga contributes to mental and physical well-being by improving flexibility, strength, balance, and emotional stability when integrated into daily routines. This ancient practice can become more accessible and adaptable to a wider audience when combined with modern artificial intelligence (AI). This study introduces a comprehensive system for detecting and classifying yoga poses using computer vision and machine learning techniques. Central to this work is the application of posture estimation algorithms, such as MediaPipe, PoseNet, and OpenPose, to identify key points on the human body within a single image or video frame. These key points are analyzed in both two-dimensional (2D) and three-dimensional (3D) spaces to construct a skeletal representation of the body, enabling accurate classification of yoga poses. The study focuses on five distinct yoga poses: Downdog, Goddess, Plank, Tree, and Warrior II. To categorize these poses, machine learning classifiers including Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes utilize the key points extracted from the pose estimation models. This research is distinctive in its thorough evaluation of various conventional classifiers across multiple yoga positions. A comprehensive comparative analysis is essential for identifying the most effective classifiers for posture detection and classification. The dataset used in this study has been carefully curated to encompass a wide array of yoga poses and is divided into training and testing sets at various ratios (90:10, 80:20, 70:30, and 60:40) to ensure robust validation. Results indicate the system's effectiveness, with SVM and KNN consistently achieving high values for AccuracyPE, PrecisionPE, RecallPE, and F1-S corePE across all yoga poses. Notably, Random Forest attains up to 100% AccuracyPE in detecting and classifying certain poses, demonstrating its robustness and reliability. This research highlights the potential of integrating p

关键词： Deep learning

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Automatic Detection and Classification of Handwritten Text in Document Images 3

Automatic Detection and Classification of Handwritten Text i...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Saraswat, Pankaj Manu, S.E. Kumar, Ritesh Sanskriti University Department of Computer Science and Engineering Uttar Pradesh Mathura India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350354171

Automatic detection and type of handwritten text in report photos is a laptop imaginative and prescient hassle that entails the identification and categorization of handwritten words, characters, and emblems from scanned documents. This problem is important because handwritten text can be observed in many files, inclusive of books, papers, notes, and invoices. The intention of this problem is to as it should be hit upon and categorize the unique handwriting additives, making it viable to correctly parse documents and interpret the textual content without manual intervention. that allows you to acquire this, numerous techniques have been developed, which include function extraction, segmentation, and classification. Characteristic extraction includes the detection of edges and strokes, and segmentation includes the grouping of additives into separate categories. Then, the special elements can be classified by applying system studying strategies, such as assisting vector machines and deep getting to know. Similarly, the handwriting additives can also be localized through the usage of bounding container algorithms. With the assistance of those strategies, the challenge of appropriately figuring out written textual content in documents has been made lots easier and greater green. © 2024 IEEE.

关键词： Algorithms Automatic Books Characters Detection Documents Extraction Function Laptop Papers

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Exploring the Effects of Channel Contention in Adaptive Communications Systems 15

Exploring the Effects of Channel Contention in Adaptive Comm...

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

作者： Shankar, Amar Acharjya, Kalyan Saraswat, Nidhi School of Engineering and Technology Department of Food Technology Karnataka Bangalore India Maharishi University of Information Technology Uttar Pradesh Maharishi School of Engineering and Technology India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

This technical summary makes a specialty of the effects of channel contention in adaptive communication systems, and how this phenomenon influences communication performance. specifically, this summary examines how distinct channel rivalry techniques, including service experience more than one get admission to (CSMA), can be used to lessen interference and postpone in a wireless communication community. It's also explored how exclusive adaptive communique systems attempt to reduce the effects of channel contention. moreover, this summary examines how studies has been carried out on the way to examine the performance of various channel rivalry schemes, in addition to how the studies effects may be implemented to enhance conversation performance in adaptive communication systems. In the end, this abstract discusses the capability advantages and barriers related to exceptional types of channel rivalry and their effect on communique overall performance. © 2024 IEEE.

关键词： Communication Conversation Exclusive. Discusses Performance Systems Techniques

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Identification of Key Signaling Pathways and Novel Computational Drug Target for Depression and Coronary Artery Disease

Identification of Key Signaling Pathways and Novel Computati...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Morshed, Monir Uddin, Muhammad Shahin Daffodil International University Department of Computing and Information System Dhaka1216 Bangladesh Department of Computer Science and Engineering Dhaka1230 Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798331529765

Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions. © 2024 IEEE.

关键词： Blood vessels

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An Extensive Analysis of Green Cloud Computing: Overview, Associated Challenges and Research Directions 3

An Extensive Analysis of Green Cloud Computing: Overview, As...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Suratia, Nishi Thakkar, Priyansh Sheth, Kunal Ramoliya, Dipak Patel, Anand K. Department of Computer Science and Engineering Gujarat Changa India Dharmsinh Desai University Faculty of Technology Department of Information Technology India

ISBN: (纸本)9798350343632

Current questions and challenges for environmental shielding are introduced by Cloud Computing. Reliability, high performance, and scalability are application states that are offered by cloud computing technologies. The Cloud Computing revolution promises environmental protection, modern networking redesign, and economic and technological advantages. Huge data centers are needed for this facility and they are coupled with a system that increases energy consumption and enormous CO2 emissions. Since the prime concern is energy, the problem generates the need for Green Cloud Computing, which can boost the efficiency of energy and to lower down tracks of carbon and electronic waste. © 2023 IEEE.

关键词： Architecture Challenges Green Cloud Computing Green Initiatives Merits and Demerits

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Classification of hyperspectral images using fusion of CNN and MiniGCN with SVM

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International Journal of Digital Earth 2023年第1期16卷 3601-3617页

作者： Wenbing Wu Tariq Sadad Mejdl Safran Sultan Alfarhood Xiaojian Yuan Big Data College Fuzhou University of International Studies and TradeFuZhouPeople’s Republic of China Department of Computer Science University of Engineering&TechnologyMardanPakistan Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia

Convolutional neural networks(CNNs)have gained popularity for categorizing hyperspectral(HS)images due to their ability to capture representations of spatial-spectral ***,their ability to model relationships between data is *** convolutional networks(GCNs)have been introduced as an alternative,as they are effective in representing and analyzing irregular data beyond grid *** have *** computationally intensive,minibatch GCNs(miniGCNs)enable minibatch training of large-scale *** have improved the classification performance by using miniGCNs to infer out-of-sample data without retraining the *** addition,fuzing the capabilities of CNNs and GCNs,through concatenative fusion has been shown to improve performance compared to using CNNs or GCNs ***,support vector machine(SvM)is employed instead of softmax in the classification *** techniques were tested on two HS datasets and achieved an average accuracy of 92.80 using Indian Pines dataset,demonstrating the effectiveness of miniGCNs and fusion strategies.

关键词： CNN hyperspectral images MiniGCN SVM

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Why Should I Trust Your Explanation? An Evaluation Approach for XAI Methods Applied to Predictive Process Monitoring Results

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1458-1472页

作者： Elkhawaga, Ghada Elzeki, Omar M. Abu-Elkheir, Mervat Reichert, Manfred Ulm University Institute of Databases and Information Systems Ulm89081 Germany Mansoura University Faculty of Computers and Information Mansoura35516 Egypt New Mansoura University Faculty of Computer Science and Engineering Mansoura35516 Egypt German University in Cairo Faculty of Media Engineering and Technology New Cairo11835 Egypt

As a use case of process mining, predictive process monitoring (PPM) aims to provide information on the future course of running business process instances. A large number of available PPM approaches adopt predictive models based on machine learning (ML). With the improved efficiency and accuracy of ML models usually being coupled with increasing complexity, their understandability becomes compromised. Having the user at the center of attention, various eXplainable artificial intelligence (XAI) methods emerged to provide users with explanations of the reasoning process of an ML model. Though there is a growing interest in applying XAI methods to PPM results, various proposals have been made to evaluate explanations according to different criteria. In this article, we propose an approach to quantitatively evaluate XAI methods concerning their ability to reflect the facts learned from the underlying stores of business-related data, i.e., event logs. Our approach includes procedures to extract features that are crucial for generating predictions. Moreover, it computes ratios that have proven to be useful in differentiating XAI methods. We conduct experiments that produce useful insights into the effects of the various choices made through a PPM workflow. We can show that underlying data and model issues can be highlighted using the applied XAI methods. Furthermore, we could penalize and reward XAI methods for achieving certain levels of consistency with the facts learned about the underlying data. Our approach has been applied to different real-life event logs using different configurations of the PPM workflow. © 2020 IEEE.

关键词： Process monitoring

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Design and implementation of power socket soldering robot arm system 4

Design and implementation of power socket soldering robot ar...

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2024 4th International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2024

作者： Liu, Jingqiao Wu, Shuqin Lin, Zicheng Zhang, Huimin Fan, Yong Xie, Wu School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guangxi Guilin541004 China School of Computer Science and Information Engineering Guangxi Normal University Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

In order to solve the problem of high cost and low efficiency in manual welding of power socket welding components, a soldering robot arm system was developed. The gripper, 5-DOF manipulator, clamp, support, and control system were designed. The coordinate system of the robot arm was constructed, and the working space of the robot arm was analyzed by using the MATLAB robot toolbox. It was concluded that the x0 coordinate range of the welding working area was [175mm, 215mm], the y0 coordinate range was [55mm, -55mm], and the z0 coordinate was 90mm. At the same time, the motion trajectory of the robot arm was determined. Using Raspberry PI as a controller, the control system is developed by programming Python language. The prototype is made, and a welding test is carried out. The test shows that the system has completed the welding operation, and the welding effect is good. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Traffic Aware Routing with Round Robin Technique for Equating the Load in Software Defined WSN 2

Traffic Aware Routing with Round Robin Technique for Equatin...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Senkamalavalli, R. Nalini, M. Kalaimani, G. Iyyanar, P. East Point College of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India S.A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Shadan Women's College of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Sona College of Technology Department of Information Technology Tamil Nadu Salem India

ISBN: (纸本)9798350346640

Multiple uses of Wireless Sensor Networks (WSNs) in the real world would need the probable dispersion of a large number of sensor nodes. The adaptability and flexibility is very difficult in the traditional WSNs which uses balancing load technology. Software-defined networking (SDN) is a good choice because it lets you see what resources are used and also set up boundaries. In WSN, the highest traffic load causes an overflow of the data queue. As a result, important data should be lost. Also, the sensor node's energy is rapidly drained, and the whole network's lifespan is damaged. Traffic-aware routing with round robin technique (TARR) for equating the load in a software-defined WSN is proposed to solve this problem. The network contains two significant functions, namely, the formation of the network and the action of the network. This approach recognizes the traffic by obtaining or forwarding packets via control and data messages. In this approach, the round-robin technique is used during congestion situations. Thus, the data is forwarded constantly. As a result, the recipient node receives all data packets efficiently. The results of the simulation demonstrate that the TARR technique boosts network throughput while simultaneously reducing both packet loss and latency in a software-defined WSN. © 2023 IEEE.

关键词： Software defined networking

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A Classification Technique for Online Shoppers' Purchasing Intention 1

A Classification Technique for Online Shoppers' Purchasing I...

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1st International Conference on Logistics, ICL 2024

作者： Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Fadel, Ali Hussein Abbas, Ali Hashim Jaber, Mustafa Musa Ali, Rabei Raad Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor86400 Malaysia School of Information Technologies and Engineering ADA University BakuAZ1008 Azerbaijan University of Diyala Department of Computer Science Diyala32001 Iraq Imam Ja'afar Al-Sadiq University Department of Computer Engineering and Information Technology AI-Muthanna Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Northern Technical University Department of Computer Engineering Technology Mosul41000 Iraq

ISBN: (纸本)9798331518844

Online shopping has become a recent outburst using electronic devices that allow consumers to shop online, as there are factors that influence shopping intention. The factors include consumers' attitudes, subject norms, behavior control, perception of usefulness, considered risks, and trust. This study aims to classify the intent of online consumers' shopping. Subsequently, based on these measures, this classification can provide fundamental and prioritized suggestions for performance improvements in online shopping. The classification is performed by using four data mining algorithms, which are random forest (RF), decision tree (DT), support vector machines (SVMs), and k-nearest neighbors (k-NN). Classification of online shoppers has been implemented based on the online shopping behavior dataset and critical performance metrics, including accuracy, precision, recall, and F1-measure. Essential factors have been considered in the online shopping behavior dataset that was used. The dataset has been preprocessed to check missing values and change the data to an appropriate numerical type. Then, the classification algorithms are applied to extract the required results. The results of detecting consumer shopping behavior showed that RF has outperformed other algorithms and achieved an average accuracy of 90.24%. The average classification accuracies of the DT, SVM, and k-NN are 89.59%, 89.38%, and 87.06%, respectively. The result of this work is hoped to assist in identifying online shoppers' behavior categories in practical aspects. © 2024 IEEE.

关键词： Support vector machines

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