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Machine Learning and Autonomous Systems (ICmlAS), International Conference on

作者： S. Venkata Suryanarayana Lakshmi H.N Posimsetti T Chiranjeevi Swamy D. Bhanu Mahesh Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India Department of Artificial Intelligence and Machine Learning Swarnandhra College of Engineering and Technology Narsapuram Andhra Pradesh India Department of Computer Science and Engineering KMIT Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Elderly people are more vulnerable to falls, which can result in serious injuries, a lower quality of life, and higher medical expenses. Traditional fall detection methods, such as wearable sensors or vision-based systems, face limitations in accuracy, comfort, and practicality. Recent advancements in deep learning have significantly improved fall detection capabilities by leveraging powerful computational models for data analysis and feature extraction. With an emphasis on important methodologies, architectures, and datasets utilized in the area, this paper offers a thorough literature analysis of deep learning-based fall detection strategies. It investigates several deep learning techniques applied to sensor-based and video-based fall detection systems, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. The review also emphasizes the difficulties encountered in practical application, including data privacy and computing cost, and the need for robust datasets. Finally, future research directions are proposed to address current limitations and improve fall detection systems' dependability and effectiveness for elderly care.

关键词： Deep learning Recurrent neural networks Reviews Computational modeling Convolutional neural networks Fall detection Older adults Wearable sensors Long short term memory Injuries

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Predictive Analytics For Groundwater Resources Using SVM And Deep Radial Basis Approaches In Smart City Planning For Feature Extraction And Prediction 2

Predictive Analytics For Groundwater Resources Using SVM And...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Padmavathi, N. Rafee, Shaik Mohammad Ahmed, Salman Kanagavalli, N. Gangwar, Prashant Kumar Meenakshi, S. Rmd Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu Chennai India Sasi Institute of Technology & Engineering Department of Ai&ml Andhra Pradesh Tadepalligudem India Aligarh Muslim University Interdisciplinary Department of Remote Sensing & Gis Applications Uttar Pradesh Aligarh India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Woldia University Department of Construction Technology & Management Woldia Town Ethiopia Tamilnadu Kavarapettai India

ISBN: (纸本)9798350371055

The growth of smart cities is happening at the same time that there is an increasing demand for water resources. Controlling groundwater in a responsible manner is absolutely necessary in order to ensure sustainability. When it comes to groundwater forecast, traditional methods are frequently wrong, which in turn leads to inefficient use of resources and bad planning. Specifically, the research employs a two technique, which combines Support Vector Machines (SVMs) for efficient feature extraction with DRB Approaches (DRBAs) for accurate prediction. SVM is excellent at extracting significant properties from complicated datasets, whereas DRB takes use of neural network capabilities to produce variable predictions. DRB employs neural network capabilities. When compared to methods that are considered to be more conventional, the findings indicate a significant improvement in the accuracy of this prediction. It is exciting to see that the combined support vector machine and DRB model are producing positive results when it comes to estimating groundwater resources. © 2024 IEEE.

关键词： Deep Radial Basis Groundwater Resources Predictive Analytics Smart City SVM

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Ensemble Learning-based Image Captioning with NLP for Context-Aware Descriptions

Ensemble Learning-based Image Captioning with NLP for Contex...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： P. M. Naidu S. Dhanesh Sai M. Naveen Ch. Anil Kumar Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Image captioning is a challenging task that requires an appropriate combination of computer vision and natural language processing to describe an image accurately and with context relevance. This paper presents an advanced method for captioning images using an ensemble learning setup along with an NLP-based fusion layer. The proposed system analyzes images from various perspectives by using multiple pre-trained models to capture diverse features, and the generated captions are then combined into a single coherent description using the fusion layer. The system’s efficiency is demonstrated using popular evaluation metrics such as BLEU, ROUGE, and METEOR, showing high accuracy and computational efficiency. This system is designed to work efficiently for a variety of image types, ensuring scalability and real-time application compatibility. The proposed method addresses common challenges in image captioning, including ambiguity, context preservation, and ensuring relevance across different datasets. Qualitative evaluation confirms the system’s ability to produce human-like captions, making it suitable for a wide range of applications such as assistive technologies, autonomous systems, and content generation.

关键词： Measurement Deep learning Scalability Feature extraction Control systems Natural language processing Real-time systems Meteors Ensemble learning Convolutional neural networks

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A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

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SN computer science 2024年第1期5卷 135页

作者： Jatain, Divya Niranjanamurthy, M. Dayananda, P. Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of AI and ML BMS Institute of Technology and Management Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too. © 2023, The Author(s).

关键词： Bat algorithm Bio-inspired metaheuristic optimisation Data science applications in education Earthworm algorithm Fuzzy sets Modified inverse class frequency Opinion mining

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Adaptive Federated Learning for Efficient Network Traffic Management in Edge Computing 4

Adaptive Federated Learning for Efficient Network Traffic Ma...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Babu, Kuricheti Suresh Venkatasubramanian, S. Kishore, S. Sree, K. Kavya Ramya Susanna, Ch.Lavanya Rao, Goda Srinivasa Lakkshmanan, Ajanthaa Pace Institute of Technology and Sciences Department of Computer Science and Engineering AndhraPradesh Valluru India Saranathan college of engineering Department of Computer Science and Business Systems Trichy India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Aditya University Department of Ai&ml Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Kl University Department of Cse Vaddeswaram Andhra Pradesh Guntur India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603 203 India

ISBN: (纸本)9798331529635

With the increasing prevalence of edge computing environment, there is a need to establish effective network traffic management that are able to manage vast data while also protecting the privacy of users. The proposed method enhances efficiency of bandwidth, reduce the network latency and manages computational demands without managing data privacy. This is achieved by utilizing the design of edge devices. The adaptable nature of the method enables it to react in real time to different traffic situations. It optimizes the distribution of resources and improves the overall performance of the network. Additionally, it can reduce the possibility of centralized data exposure, providing robust privacy preservation through the allowance of local model training on edge devices. Finally, this method is superior. The employment of federated learning to network traffic management provide efficiency to optimize the traffic in edge computing which is both scalable and safe. This makes it suitable for a wide variety of applications that are utilized in next generation networks © 2024 IEEE.

关键词： Federated learning

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A Machine Learning Method for Detecting Behavior-Based Malware 5

A Machine Learning Method for Detecting Behavior-Based Malwa...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Venkatarao, Javvaji Deeban Chakravarthy, V. Ramya, Singaraju Siva Prasad, V. Cherukuvada, Srikanth Soujanya, Annaram Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Tirupati517 102 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Mohan Babu University Dept. of Ai&ml Tirupati India School of Computing Srm Institute of Scienceand Technology Department of Networking and Communications Chennai India Cvr College of Engineering Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350321425

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis. © 2023 IEEE.

关键词： Support vector machines

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Unmasking Deep Fakes: Detecting Low-Quality Forgeries through Unseen Artifacts

Unmasking Deep Fakes: Detecting Low-Quality Forgeries throug...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： M. Venkateshwarlu M. Yashaswi Dhanya Shah R. Akash Reddy Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The spread of deepfakee-technology is a danger to the veracity of multimedia material, since it may be used to manipulate people's identities or spread false information. Low-quality forgeries continue to be difficult to detect because of their subtle artifacts, which elude traditional detection approaches. This is true even while deep fake detection algorithms have made significant progress in identifying high-quality forgeries. This research work presents a unique method for identifying hidden elements in low-quality forgeries, which may be used to uncover deep fakes. The proposed approach, which makes use of sophisticated machine learning algorithms and computer vision techniques, is centred on locating and taking advantage of distinctive artifacts that are introduced throughout the deep fake production process. This study provides a thorough analysis of these artefacts and show how well they can distinguish between real multimedia material and poor-quality deep fakes. The proposed technique is effective in properly detecting low-quality forgeries of photos, videos, and audio recordings. These findings are based on experiments conducted on a variety of datasets. Additionally, the proposed method's resilience is assessed against the cutting-edge deep fake creation techniques and emphasize its practical benefits in preventing the distribution of false information.

关键词： Deepfakes computer vision Machine learning algorithms Communication systems Neural networks Production Streaming media Forgery Detection algorithms Resilience

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Automatic Face Recognition Using Legendre Moments and Auto-Associative Memory

Automatic Face Recognition Using Legendre Moments and Auto-A...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Jay Kant Pratap Singh Yadav Zainual Abdin Jaffery Laxman Singh Electrical Engineering Department Jamia Millia Islamia Central University New Delhi India Computer Science & Engg. (AI&ML) Department Krishna Group of Institutions Ghaziabad India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Face recognition involves identifying or verifying a person based on their facial features. This study introduces a method using Legendre moments for feature extraction combined with an auto-associative memory approach, specifically a “Multi-connection Hopfield Neural Network” to enhance accuracy and robustness. The method uses a single image per person, optimizing both the computation time and memory usage. Tested on the ORL (Olivetti Research Laboratories) dataset, the approach achieved a 99.0% recognition rate, outperforming other state-of-the-art methods. Additionally, the system demonstrated resilience to noise, maintaining a 95.75 % rate with 50 % random noise.

关键词： Training Associative memory Accuracy Face recognition Noise Hopfield neural networks Feature extraction Robustness Noise measurement Resilience

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Dynamic and Energy-Efficient Resource Allocation using Bat Optimization in 5G Cloud Radio Access Networks 2

Dynamic and Energy-Efficient Resource Allocation using Bat O...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Padmakala, S. Al-Farouni, Mohammed Rao, Deepak Dasaratha Saritha, Kambhampati Puneeth, R.P. Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science And Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Indian Institute of Technology Patna India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai & Ml Hyderabad Bachupally India Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350372892

G networks represent the fifth iteration of cellular networks, thus aimed at resolving the challenges and constraints faced by the preceding 4G network. Cloud Radio Access Networks (CRAN) provides economical and EnergyEfficient (EE) solutions customized to the requirements of 5G networks. This research proposes an energy-efficient CRAN that automatically allocates Baseband Unit (BBU) resources to Remote Radio Unit (RRHs) based on traffic, increasing network throughput as well as reducing power usage. Bat Optimization Algorithm (BOA) is a Swarm Intelligence (SI) approach used to optimize Resource Allocation (RA) in CRAN by mapping BBURRHs. Through controlling the BBU's mode based on resource utilization, this research minimizes the Power Consumption (PC) in 5G CRAN. Moreover, the findings revealed that the proposed BOA-CRAN reduced the power usage upto 95% when compared to existing methods such as 5G CRAN, Green-RAN while increasing BBU efficiency. © 2024 IEEE.

关键词： 5G mobile communication systems

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A Novel Approach to Skin Cancer Detection using CNN, ResNet, and Hybrid Feature Extraction

A Novel Approach to Skin Cancer Detection using CNN, ResNet,...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： T.Srinivasa Ravi Kiran Y.Sudha Madhuri P Venkata Bala Annapurna A. Lakshmanarao B.S.N. Murthy Department of Computer Science Parvathaneni Brahmayya Siddhartha College of Arts & Science Vijayawada AP India Department of CSE(AI&ML|DS) Anil Neerukonda Institute of Technology and Sciences Department of AI&ML Aditya University Surampalem India Department of IT Aditya University Surampalem India Bonam Venkata Chalamayya Engineering College Odalarevu Andhra Pradesh

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Skin cancer is a prevalent and potentially life-threatening condition, making early and accurate detection crucial for effective treatment. This work proposes a novel hybrid approach to skin cancer detection, integrating both deep learning and traditional ml techniques. Utilizing a dataset from Kaggle, the process began with initial classification using a Convolutional Neural Network (CNN) and achieved an accuracy of 90%. Features were then extracted using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and ResNet, adding multiple layers of feature richness. These features were combined into a comprehensive hybrid feature vector and analyzed using ml models, including Random Forest, XGBoost, and AdaBoost. The Random Forest model achieved an accuracy of 96%, while XGBoost obtained an accuracy of 95. 60%. AdaBoost reached an accuracy of 95%. The proposed method demonstrated significant improvements in detection accuracy and robustness, highlighting its effectiveness in enhancing skin cancer detection.

关键词： Accuracy Soft sensors Feature extraction Vectors Skin Robustness Real-time systems Convolutional neural networks Random forests Skin cancer

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