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Techniques for Influencer Detection, Mapping, and Popularity Prediction in Social Media Networks

Techniques for Influencer Detection, Mapping, and Popularity...

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computing for Sustainability and Intelligent Future (COMP-SIF), international conference on

作者： Chandana T M Sreekanth Rallapalli Department of Master of Computer Applications Visveswaraya Technology University Nitte Meenakshi Institute of Technology Bengaluru Karnataka Department of Master of Computer Applications Nitte Meenakshi Institute of Technology Bengaluru Karnataka

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

This paper recognizes the fact that in an age where consumption patterns are closely linked with social media platforms the measurement of key influential personalities and their effects on consumers is crucial. This paper offers an extensive analysis of the state-of-the-art methods for influencer identification and influence mapping. Approaches used in this research are based on Machine Learning and Deep Learning Algorithms which include Graph Attention Networks, Gated Recurrent Units, Graph-based Long Short-Term Memory Neural Networks, Recurrent Neural Networks, Stacked Autoencoders, Centrality Measures, and Frameworks such as Stimulus Organism Response. Comparing all of these approaches reveals their role in improving influencer marketing approaches, targeting, and the level of engagement, and profitability. The current study will benefit this area of knowledge by showing the outcomes, performance, and computational strengths of these approaches, paving the way for the enhancement of influencer marketing technology.

关键词： Deep learning Machine learning algorithms Consumer behavior Social networking (online) Profitability Current measurement Organisms pattern recognition Sustainable development Long short term memory

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Optical Observation for Body Angles of Cricket Players’ Motion to Identify the Batting Shots

Optical Observation for Body Angles of Cricket Players’ Mot...

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Advanced Research in computing (ICARC), international conference on

作者： Ishan Jayaweerage Ashan Induranga Kaveendra Maduwantha Samantha Kumara Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Faculty of Technology Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Cricket, one of the world’s most popular sports, has developed considerably during the past few decades. The competition and complexity of this sport increased largely during the past few years, which brought the attention of the researchers to develop technical solutions to improve the quality of the sport by improving both the on-field decision-making and practicing methods. There are several kinds of high-end equipment available in the market that can be used to improve these skills. Even though such equipment can be used at the national level, it is very difficult to use them at school and club levels due to their higher cost. This research focused on enhancing the batting skills of cricket batsmen using a low-cost setup that uses webcams to capture the footage and the MediaPipe library, which was developed by Google to analyze motion data. This research analyzed two cricket shots, the front foot defence, and the pull shot, using four web cameras. The received footage was analyzed using MediaPipe to identify the variations in the angles of the player’s left elbow and left shoulder while playing the shot. These pattern identifications can help successfully understand the angle variation of the forward foot defence and the pull shots by generating an average angle value graph using four web cameras. These graphs show the special patterns for each batting shot. This methodology and the results are valuable for developing a successful player analyzing system by considering the other important cricket shots at a very low cost with affordable 2D web cameras.

关键词： Integrated optics Costs Webcams Ultraviolet sources Lighting Machine learning Optical imaging pattern recognition Sports Foot

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Optimizing Human Motion Posture Detection and Humanized computing for Smart Health Applications

Optimizing Human Motion Posture Detection and Humanized Comp...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Weiwei Wang Jingshan Zhang School of Sports Medicine and Rehabilitation North Sichuan Medical College Nanchong Sichuan China Department of Sports Humanities and Social Sciences Sichuan Sports College Chengdu Sichuan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Existing human motion detection technologies have problems such as insufficient accuracy and poor real-time performance, which makes it difficult to meet the needs of health management. This paper proposes a human motion posture detection and humanized calculation method based on multi-sensor fusion and LSTM. This paper first deploys IMU sensors at key parts of the human body, and uses visual sensors to capture the three-dimensional posture information of human motion, and obtains physiological data such as heart rate and number of steps through wearable devices such as smart bracelets. Secondly, the IMU data is denoised and calibrated to eliminate sensor errors. Next, the fused data is trained using a long short-term memory network (LSTM). The rationality of the motion posture is then analyzed, and the user's exercise intensity and health status are evaluated based on the motion data and physiological data. In terms of posture recognition accuracy, the average value of this method is 92.61 %. In terms of abnormal posture detection rate, the detection rate of some data points of this method is close to 100%. At the same time, the maximum recognition time of this method is only 789 milliseconds, and the minimum recognition time is as low as 503 milliseconds, which is better than the traditional method. The optimized human motion posture detection and humanized calculation method proposed in this paper realizes high-precision detection of human motion and intelligent health management through multi-source data fusion, LSTM construction, human kinematics analysis and personalized health assessment.

关键词： Training Accuracy soft sensors Data integration Real-time systems Physiology Data models Long short term memory Intelligent sensors Wearable sensors

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Deep Learning for Real-Time Malware Detection and Classification

Deep Learning for Real-Time Malware Detection and Classifica...

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Visual Analytics and Data Visualization (ICVADV), international conference on

作者： Shantanu Sudhir Gujar Department of Cyber Security Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The increased deep learning styles have been adopted to improve the identification and detection of malware in real time. This improves the detection and detection of malware. The research focuses on the utilization of Convolutional Neural Networks (CNNs) to recognize malware patterns. These techniques are utilized to accomplish the aforementioned goals. The incorporation of these styles results in an improvement in both the delicacy and the speed of operation when it comes to transmitting viruses. Additionally, the research highlights the application of DeepLearning4J (DL4J), which is an important framework for deep learning, to successfully deploy these models, which enables large-scale malware analysis operations. This is demonstrated by the research. During the research, significant advancements were made in the detection of malware in real-time, and strong results were obtained in the fight against developing cyber threats. One way in which these developments are displayed is through the combination of different approaches and technologies that are utilized.

关键词： Deep learning Recurrent neural networks Visual analytics Heuristic algorithms Autoencoders Malware Real-time systems pattern recognition Convolutional neural networks Long short term memory

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Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. This work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. This can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. This accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

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Physical Exergames Movements and pattern recognition using Convolutional Neural Network

Physical Exergames Movements and Pattern Recognition using C...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

This paper proposes a novel framework for accurately recognizing physical exergames interaction in video sequences. The developed approach uses image processing and machine learning tools to correctly extract and classify the features. The framework comprises five main steps: Silhouette extraction, preprocessing, feature extraction, feature optimization and classification. Image contrast is improved by power law transformation, silhouette is extracted by the Multiple Object Tracking (MOT) algorithm and graph-based segmentation. We extract ORB and geometric skeleton based keypoints to capture discriminative features, and apply Fast Independent Component Analysis (FICA) to optimize feature representation. Thirdly, the Convolutional Neural Network algorithm is used to classify the optimized features, with a macro average accuracy of 0.85 and a weighted average accuracy of 0.87%. The proposed framework effectively recognizes physical exergames interaction and can be applied in many domains, such as human-human interaction and surveillance.

关键词： Image segmentation Machine learning algorithms Accuracy Surveillance Video sequences Independent component analysis Feature extraction Classification algorithms Convolutional neural networks Optimization

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Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

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Visual Analytics and Data Visualization (ICVADV), international conference on

作者： Venkateswaran Radhakrishnan Vijayakumari Rodda S. Sowmiya Abdur Rahman Sarker R. Palanikumar R. Asokan Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Dhofar Sultanate of Oman Department of Computer Science Krishna University Rudravaram Machilipatnam Sri Eshwar College of Engineering COIMBATORE Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of ECE Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security.

关键词： Deep learning Ciphers Accuracy Computational modeling Visual analytics Neural networks Transformers Encryption pattern recognition Convolutional neural networks

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SmartRecovery: A Deep Learning-Based System for Personalized Post-Cold Recovery Management in Diabetic Patients

SmartRecovery: A Deep Learning-Based System for Personalized...

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Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： G. Revathy J. Justina Princy Thilagavathy C Saranya M. Thangavel Department of CSE Srinivasa Ramunjan Centre Sastra Deemed University Kumbakonam Tamil Nadu Department of Computer App ications School of Computing VET Institute of Arts and Science (Co-education) College Thindal. Erode Tamil Nadu Department of Biomedical Engineering Nandha Engineering College Erode Tamil Nadu Department of MCA Erode Sengunthar Engineering College PerunduraiErode Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

People with diabetes frequently have a longer and more difficult time recovering from colds because of their weakened immune systems and altered metabolism. An method that is more individualized is required since traditional recovery regimens do not take into consideration lifestyle variables, pharmaceutical interactions, and individual differences in glucose levels. A deep learning-based solution called SmartRecovery is suggested in this study to help diabetics control their post-cold recovery as best they can. The system creates adaptive recovery plans by utilizing multimodal data, such as patient vitals, glucose monitoring, medication adherence, and symptom progression. Using a combination of recurrent neural networks (RNNs) for sequential health data analysis and convolutional neural networks (CNNs) for pattern recognition, SmartRecovery predicts potential complications and recommends personalized interventions. In order to ensure ongoing evaluation and feedback, the system incorporates real-time monitoring via wearable sensors and mobile health applications. Experimental assessments using actual diabetes patient datasets show how well the suggested strategy works to speed up recovery and reduce dangers to health. A revolutionary approach to post-illness care, SmartRecovery improves patient outcomes and lessens the strain on healthcare systems by utilizing AI-driven insights.

关键词： Deep learning Telemedicine Real-time systems Diabetes pattern recognition Glucose Monitoring Wearable sensors Standards Strain

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Automation Methods of Data Collection and Analysis for Managing Complex IT Infrastructures

Automation Methods of Data Collection and Analysis for Manag...

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international Russian Smart Industry conference (SmartIndustryCon)

作者： Anton Skalkin Anton Romanov Department of Information Systems Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article discusses an approach to the organization of monitoring and management of server equipment, aimed at ensuring the stable operation of the IT infrastructure. Modern methods based on threshold analysis cannot cope with the volume and variety of data, which requires automation of the collection, processing and analysis of metrics. A computing cluster of servers using the Proxmox VE virtualization platform is presented as an object of research. The features of time series storage and data processing using the InfluxDB database, which has a convenient query language called Flux, are described. Implemented software architecture for integrating monitoring and data analysis tools, including Prometheus PVE Exporter, Telegraf and Flux. The advantages of using Python for metric analysis and query development using the Builder pattern are shown, which simplifies working with the system. Methods of data preprocessing and visualization for anomaly analysis and forecasting with the aim of maintaining the stable operation of server equipment are discussed. The proposed approach ensures the efficiency and ease of monitoring, while also enhancing the accuracy of decision-making given the increasing complexity of IT infrastructure.

关键词： Measurement Automation software architecture Source coding soft sensors Time series analysis Virtual machines Servers Monitoring Python

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Deep Neural Networks for Advanced Medication Security in IoT-Enabled Smart Robotic Dispensing Cabinets

Deep Neural Networks for Advanced Medication Security in IoT...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Prabhu V S Gurumoorthi Gurulakshmanan C. Chandravathi D. P. Sangeetha Vinoth Kumar V B Meenakshi Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Mphasis Corporation Texas USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Ensuring advanced medication security is of the utmost importance in smart robotic dispensing cabinets enabled by the Internet of Things (IoT). Strong pharmaceutical security is of the utmost importance in smart robotic dispensing cabinets that the IoT enables. To strengthen security measures in these systems, this research presents a new method that uses deep neural networks (DNNs). The proposed system improves the identification of potential medication mistakes and unauthorized access by using DNNs' anomaly detection and pattern recognition capabilities. A thorough security layer is created by incorporating DNN models into the IoT design of smart cabinets to address drug administration and dispensation weaknesses. Discovering security breaches or procedural problems entails training DNNs on large datasets to detect typical operating patterns and abnormalities. It shows that the strategy significantly improves security procedures and operational dependability in real-world conditions via simulations and validation experiments, proving its usefulness. Providing a strong answer to the problems of improving the effectiveness of drug administration in healthcare settings based on the IoT helps move healthcare technology forward. The technology improves pharmaceutical security and lays the groundwork for responsive and adaptable healthcare infrastructures. This study will make improving the dependability and safety of healthcare delivery systems possible.

关键词： Drugs Medical services Artificial neural networks Robot sensing systems pattern recognition Security Internet of Things Artificial intelligence Anomaly detection Intelligent sensors

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