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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review of Moving Object Detection Techniques for Night Time 5

A Review of Moving Object Detection Techniques for Night Tim...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Dua, Disha Bhat, Aruna Girdhar, Ashish Delhi Technological University Department Of Computer Science & Engineering Delhi India Kurukshetra University Department Of Computer Science & Applications Kurukshetra India

ISBN: (纸本)9798350330861

Moving Object Detection in low light conditions is a challenging task due to limited visibility and the presence of noise and objects in the captured images, low contrast, low brightness, low appearance information, and low Signal to Noise Ratio. The majority of night surveillance models suffer from lack of adaptability for different scenes and separation between detection and tracking. This review paper provides a comprehensive survey of recent advancements in Moving Object Detection Techniques for night-time situations. It presents an overview of various approaches proposed in the literature, including traditional and deep learning-based methods, and discusses their strengths and limitations. Moreover, the paper highlights evaluation metrics commonly used in the literature, the datasets they have been evaluated upon, along with a comparative analysis of the performance metrics & various limitations of different methods. Finally, the paper identifies the key research challenges and future directions for Moving Object Detection in Night Time viz. dim-lit environments, such as the need for more robust and efficient algorithms that can handle diverse environmental conditions and real-time applications. © 2023 IEEE.

关键词： Object detection

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Utilizing Artificial Intelligence for Advance Data science and Analysis 2

Utilizing Artificial Intelligence for Advance Data Science a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Deepak Chaudhary, Gaurav Rajput, Kapil Singhal, Amit Sharma, Tarun Kumar Srm Institute of Science and Technology Department of Computer Applications UPModinagar Delhi India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Data technology has experienced an explosion in popularity and scope over the past few years, inflicting several companies to try to capitalize on the sizeable possibilities the technology has to provide. Agencies are utilizing AI to develop records science studies, allowing them to go beyond conventional strategies and discover new insights and innovations within the discipline. With AI, statistics scientists can identify styles in massive datasets, optimize algorithms to clear up complicated issues and create predictive fashions to discover developments and correlations. In addition, AI may be used to automate specific components of fact processing, making data technological know-how simpler and quicker. To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use. With the right group and the proper equipment, AI may make data technological know-how studies extra effective and more beneficial than ever. © 2024 IEEE.

关键词： Algorithms Correlations Fundamentals Technology Understand

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Predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques 1

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International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Baby Lavanya, K. Ismail Ansari, M.D. Gundu, Venkateswarlu Krishna Mohan, G. Mouleswararao, B. Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation AP Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

Customer churn poses a significant challenge to the telecom industry as it directly impacts revenue and customer retention efforts. To tackle this concern, the present research suggests an all-encompassing machine learning oriented method for forecasting customer attrition within the telecommunications industry. The dataset is employed for training and testing the Machine Learning models, and their effectiveness is assessed through conventional metrics like accuracy, precision, recall, and the area under the curve of the receiver operating characteristic (AUC-ROC). Conducting an analysis of feature importance enables us to pinpoint the foremost factors that impact customer churn. The findings illustrate that the logistic regression model attains accuracy in its predictive capabilities for customer attrition. The approach offers a practical and accurate solution, aiding telecom companies in reducing churn, increasing customer loyalty, and ultimately improving business performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Telecommunication industry

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Fostering Learning with Facial Insights: Geometrical Approach to Real-Time Learner Engagement Detection 9

Fostering Learning with Facial Insights: Geometrical Approac...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Raji Gopinathan, N. Sherly, Elizabeth Cochin University of Science And Technology IIITM-Kerala Department of Computer Applications Trivandrum India Digital University Kerala School of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350394474

The global shift in the education paradigm demands technology-enabled learning platforms that monitor the performance and engagement of learners. In this paper, a new method of detecting learner engagement levels utilizing geometric angle features obtained from face images captured through a webcam attached to the learning device is proposed. Distinct ten angles resulting from lines joining seventeen landmark points serve as the feature set and employ machine learning algorithms for classification. The algorithms used for the classification purpose include Random forest, K-Nearest Neighbour, and Support Vector Machine. Random Forest outperforms the other classifiers in distinguishing between the two engagement levels being tested. It achieves the highest accuracy of 90% in distinguishing low and high engagement levels of students. The 3-Level classification model is also comprehensively compared with other state-of-the-art methods and found a notable accuracy than the deep learning models MobileNet and VGG16. This indicates that the proposed method can efficiently detect and output students' engagement levels which provides real-time support for teachers in classrooms and helps students while undergoing self-paced E-learning. © 2024 IEEE.

关键词： Students

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Osteoarthritis Prediction in Knee Joint Using Deep Learning Techniques 27

Osteoarthritis Prediction in Knee Joint Using Deep Learning ...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Singh, Shubham Kumar Chouhan, Kuldeep Agrawal, Arun Prakash Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Osteoarthritis is a disorder that transpires when the articular cartilage in the knee breaks down and changes to the underbelly of the cartilage. Normal joint fracture protection for the bones is provided by the slick substance known as articular cartilage. Early prediction can slow down the progression of the condition. Medical professionals' expertise is utilized to predict severity. In this research, we address the issue that arises when a model's validation accuracy is lower than its training accuracy. Specifically, we find that as dataset size increases, the validation accuracy of Osteoarthritis prediction falls. We used a Deep Convolutional Neural Network to develop an automatic osteoarthritis severity assessment system using the Kellgren and Lawrence (KL) grading system. With this method, X-ray images are used to predict the severity of osteoarthritis. This study's objective is to predict the severity stage of knee osteoarthritis in the form of prediction accuracy (in %) of osteoarthritis using a transfer learning and deep learning approach. As a result, EfficientNetB5 provides a prediction accuracy of over 97.45% as demonstrated in this work. © 2024 IEEE.

关键词： Convolutional neural networks

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Current Perspectives and Virtualisation solutions with SDN for IoT 2

Current Perspectives and Virtualisation solutions with SDN f...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Bhardwaj, Shanu Kumar, Shailender Girdhar, Ashish Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9798350305418

During the past years, IoT has acquired a lot of consideration since it incorporates intelligent gadgets which empower many applications that work in our day-to-day existence. Due to this the rising number of clients and the interest in more different and specific applications have prompted a tremendous expansion in the network traffic. Managing different traffic requests from various applications is a difficult task for the current traditional networking architecture. Therefore, the paper provides a thorough analysis of the SDN and various other technologies-based network virtualization methods as well as current perspectives for the IoT. The representation provides the working of various up-going technologies such as Machine Learning, Edge Computing, and Virtualization with SDN to betray the performance of the SDN applications in today’s world. © 2023 IEEE.

关键词： edge computing internet of things machine learning network traffic software-defined networking

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A Bio-Inspired Virtual Machine Placement Toward Sustainable Cloud Resource Management

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IEEE Systems Journal 2023年第3期17卷 3894-3905页

作者： Singh, Ashutosh Kumar Swain, Smruti Rekha Saxena, Deepika Lee, Chung-Nan National Institute of Technology Department of Computer Applications Kurukshetra136119 India Goethe University Department of Computer Science Frankfurt60323 Germany National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan

Virtual machine placement (VMP) is the process of selecting the most appropriate physical machine (PM) to place users' requested virtual machine (VM) in large cloud data centers. Several methods have been framed to deal with this problem. However, the current solutions only consider limited resource types, resulting in an unbalanced load that activates unnecessary PMs inside the data center. In this article, we suggest a flower pollination-based nondominated sorting optimization (FP-NSO) algorithm that maximizes resource usage and minimizes energy consumption and carbon emission of the data center. Multiple resource-constraint metrics are associated with our algorithm that assists in finding the most suitable PMs for deploying VMs in a cloud environment. The VMP is carried out by employing the combined concept of flower pollination optimization and nondominated sorting technique-based genetic algorithm (NSGA-II). The algorithm is evaluated using the Google cluster dataset. The performance metrics like resource utilization, power consumption, and carbon emission values are computed for static and dynamic scenarios. The obtained results are compared with existing approaches. There is a significant reduction in power consumption, carbon emission, and execution time up to 16.69%, 48.60%, and 75.87%, respectively, and an improvement in resource utilization is up to 78.18%. © 2007-2012 IEEE.

关键词： Cloud computing

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Segmentation of Water Bodies from Satellite Images Using Spatial-Channel Attention UNet

Segmentation of Water Bodies from Satellite Images Using Spa...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Anitha, M. Chitra, P. Meenaragavi, P. Thiagarajar College of Engineering Department of Computer Applications Madurai625 015 India Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350378177

Precise division of aquatic areas is essential for efficient environmental supervision and administration. It enables the precise delineation and tracking of aquatic ecosystems, facilitating the assessment of water quality and the identification of environmental hazards such as pollution and habitat degradation. By offering high-resolution data and enabling thorough coverage of large areas, advanced technologies like satellite images play a crucial role in improving water body segmentation and expanding the capacity to monitor and manage water resources effectively and sustainably. In this study, the focus is on leveraging Sentinel-2 satellite imagery for accurate water body segmentation. The dataset is meticulously curated and comprises Sentinel-2 satellite images. Masks are then created to isolate water bodies from the original images using advanced deep-learning architectures, including UNet, Deeplabs, and SegNet. Furthermore, the performance of UNet is enhanced by incorporating attention mechanisms such as channel attention, spatial attention, and combined spatial and channel attention. These algorithms are used to generate output masks, which are then compared with the existing masks from the dataset. Subsequently, the evaluation was performed using metrics such as accuracy, binary cross- entropy loss, and mean intersection over union, providing comprehensive insights into their efficacy. Notably, the findings highlight the remarkable accuracy achieved by the UNet model enhanced with spatial and channel attention mechanisms, reaching an impressive 96.7%. This notable progress emphasizes the possibility of achieving better accuracy in segmenting water, which in turn aids real-time monitoring and informed decision-making in environmental management. © 2024 IEEE.

关键词： Decision making

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Low-Cost Weather Monitoring System with Intelligent Design and XGBoost Regression

Low-Cost Weather Monitoring System with Intelligent Design a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Vishnu Vilashini, S. Kishore, S. Nair, Rekha. R. Babu, Tina Bhadani, Anant Thanushree, G.R. Dixit, Anuj Kumar Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Karnataka India Allaince University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Weather monitoring is a critical aspect of weather forecasting and analysis, providing essential data for decision-making in various sectors. However, traditional weather monitoring systems face limitations such as high costs, limited accuracy, and inadequate coverage. The proposed system addresses these challenges by utilizing the Internet of Things (IoT) technology and NodeMCU, a low-cost microcontroller. The novelty of this work lies in the implementation of a cost-effective weather monitoring system that accurately forecasts upcoming weather conditions. Notably, the XGBoost Regression model emerges as the top performer, exhibiting exceptional accuracy with a mean absolute error (MAE) of 0.0213 indicating high accuracy in weather prediction. This innovative approach not only provides an affordable solution but also demonstrates the system's advanced capability to predict future weather patterns. Utilizing a NodeMCU board, real-time sensor data is gathered and transmitted to ThingSpeak's cloud server for analysis. This methodology has the potential for future applications in industries such as agriculture, transportation, and disaster management. © 2023 IEEE.

关键词： NodeMCU Real-time monitoring Recursive feature elimination ThingSpeak Weather prediction XGBoost Regression

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