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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Motamarri, Nikhita Satish, G. Uma, H.R. Barve, Amit Patil, Harshal Taqui, Syed Noeman Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering and Technology Department of Computer Science and Engineering Vijayawada Andhra Pradesh 520001 India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Sheriguda Ibrahimpatnam Telangana Hyderabad501510 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya Karnataka 571448 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Department of Computer Science and Engineering Pune Maharashtra 412115 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamilnadu Chennai602105 India

ISBN: (纸本)9798350327533

The Blockchain technology is leveraged to establish a secure and tamper-resistant decentralized ledger, providing a transparent and immutable record of all transactions and communications within the IoT network. By implementing smart contracts, we ensure the integrity and authenticity of device interactions, mitigating the risk of unauthorized access and data manipulation. Complementing the Blockchain, Machine Learning (ML) algorithms are employed to analyze and identify patterns indicative of malicious activities. The proposed system employs anomaly detection models, trained on historical data, to continuously monitor and adapt to emerging threats. This dynamic threat detection mechanism enhances the ability of the IoT network to recognize and respond to sophisticated intrusion attempts in real-Time. Furthermore, the proposed framework incorporates a decentralized identity management system using Blockchain, ensuring that only authenticated and authorized devices can participate in the IoT network. This not only safeguards against unauthorized access but also protects the privacy and integrity of the data generated and exchanged by IoT devices. To validate the effectiveness of our approach, we conducted experiments in a simulated IoT environment, considering various attack scenarios. The results demonstrate a significant improvement in the overall security posture of the IoT network, with reduced false positives and swift identification of intrusions. In conclusion, the integration of Blockchain and ML techniques provides a robust and adaptive defense mechanism for IoT device networks. This holistic approach addresses the challenges of security, privacy, and data integrity, paving the way for a more resilient and trustworthy IoT ecosystem in the face of evolving cyber threats. © 2024 IEEE.

关键词： Blockchain Integrity Internet of Things (IoT) Intrusion Intrusion Detection System Machine Learning Security

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Star Profile and Audience Interest-Based Movie Rating Prediction Model 8

Star Profile and Audience Interest-Based Movie Rating Predic...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Vyas, Avani Sharma, Shivani Dr B R Ambedkar National Institute of Technology Jalandhar Department of Information Technology Jalandhar India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Patiala India

ISBN: (纸本)9798331531539

In today's entertainment era, several movies are produced over a year. We all try to get a review of any movie before planning to watch it. Much work has been done in the past for early movie success prediction but most of them are from the production perspective. However, the audience perspective is still unexplored. Further, most of the predictive models work with some basic features such as genre, cast, comments, reviews, and production house. However, a few important factors such as current audience interest shift, the social influence of the star, and personal liking of a person are much more important in predicting the rating of a movie. In this work, we propose an advanced movie rating prediction cross model, which could rate movies according to general as well as some fine-tuned features. This helps in providing a fair prediction of a movie according to global and personal audience interests. We collected a wide range of data from the Web, and social media along with the IMDb dataset. A comparative analysis of the proposed model with existing benchmark schemes has been done. Significant improvement in movie rating and success prediction is observed with an accuracy of 87%. © 2024 IEEE.

关键词： Prediction models

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AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

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Digital Communications and Networks 2024年第2期10卷 292-303页

作者： Yuteng Xiao Kaijian Xia Hongsheng Yin Yu-Dong Zhang Zhenjiang Qian Zhaoyang Liu Yuehan Liang Xiaodan Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou221116China Department of Informatics University of LeicesterLeicesterLE17RHUK The Affiliated Changshu Hospital of Soochow University Changshu215500China School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

关键词： Adaptive adjacency matrix Digital twin Graph convolutional network Multivariate time series prediction Spatial-temporal graph

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READS: Resource efficient attack detection system for drones

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Multimedia Tools and Applications 2024年 1-20页

作者： Vishnu, Aditya Arora, Sumedha Kerala University of Digital Sciences Innovation and Technology Kerala Thiruvananthapuram India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahemdabad India

Unmanned Aerial Vehicles (UAVs), commonly known as aerial drones, have gained popularity due to their versatility and applications in various industries. However, their increasing use has raised concerns about cybersecurity and the vulnerability of networked UAVs to malicious attacks. A deep learning-based Attack Detection System (ADS) is essential for mitigating cyber threats to UAVs. It can provide proactive protection even against unknown attacks. Also, for resource constraints of UAVs, ADS must be resource-efficient to optimize the deep learning model for embedded hardware, making a low code footprint. Therefore, this work focuses on developing a Resource Efficient Attack Detection System (READS) for drones that uses a lite deep learning-based network activity classification algorithm for detecting cyber threats on UAVs. The entire system has been implemented in three phases, where the first phase includes dataset training. The second phase involves running multiple conventional models to choose the best conventional model for intrusion detection using accuracy, precision, and recall. This is followed by phase three, which converts the selected conventional Dense Neural Network (DNN) model into a lite weight (Tiny) model that works on UAVs. The selected Lite DNN model is evaluated on a real-time testbed. The optimized model could lower the storage space and generates 97.69% accuracy. The proposed system consumes 30% of the original power. Hence, it allows efficient threat detection while minimizing the impact on UAV resources. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Drones

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Medical knowledge graph question answering for drug-drug interaction prediction based on multi-hop machine reading comprehension

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1217-1228页

作者： Peng Gao Feng Gao Jian-Cheng Ni Yu Wang Fei Wang Qiquan Zhang School of Cyber Science and Engineering Qufu Normal UniversityQufuChina School of Computer Science and Technology East China Normal UniversityShanghaiChina Network and Information Center Qufu Normal UniversityQufuChina School of Electronics and Information Engineering Harbin Institute of TechnologyShenzhenChina School of Electrical Engineering and Telecommunications University of New South WalesSydneyNSWAustralia

Drug-drug interaction(DDI)prediction is a crucial issue in molecular *** methods of observing drug-drug interactions through medical experiments require significant resources and *** authors present a Medical Knowledge Graph Question Answering(MedKGQA)model,dubbed MedKGQA,that predicts DDI by employing machine reading comprehension(MRC)from closed-domain literature and constructing a knowledge graph of“drug-protein”triplets from open-domain *** model vectorises the drug-protein target attributes in the graph using entity embeddings and establishes directed connections between drug and protein entities based on the metabolic interaction pathways of protein targets in the human *** aligns multiple external knowledge and applies it to learn the graph neural *** bells and whistles,the proposed model achieved a 4.5%improvement in terms of DDI prediction accuracy compared to previous state-of-the-art models on the QAngaroo MedHop *** results demonstrate the efficiency and effectiveness of the model and verify the feasibility of integrating external knowledge in MRC tasks.

关键词： artificial neural network data analysis document handling natural language processing

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Improving long-tail classification via decoupling and regularisation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 62-71页

作者： Shuzheng Gao Chaozheng Wang Cuiyun Gao Wenjian Luo Peiyi Han Qing Liao Guandong Xu School of Computer Science and Technology Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Harbin Institute of TechnologyShenzhenChina Faculty of Engineering and Information Technology University of Technology SydneySydneyNew South WalesAustralia

Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based *** methods mainly tackle this problem by reweighting the loss function or rebalancing the ***,one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle *** this paper,the authors shed light on the significance of the angle distribution in achieving a balanced feature space,which is essential for improving model performance under long-tailed ***,it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature *** tackle these challenges,the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components:classifier norm(*** magnitude of the classifier vector),feature norm(*** magnitude of the feature vector),and cosine similarity between the classifier vector and feature *** this way,the authors analyse the change of each component in the training process and reveal three critical problems that should be solved,that is,the imbalanced angle distribution,the lack of feature discrimination,and the low feature *** from this analysis,the authors propose a novel loss function that incorporates hyperspherical uniformity,additive angular margin,and feature norm *** component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature *** authors conduct extensive experiments on three popular benchmark datasets including CIFAR-10/100-LT,ImageNet-LT,and iNaturalist *** experimental results demonstrate that the authors’loss function outperforms several previous state-of-the-art methods in addressing the challenges posed by imbalanced and longtailed datasets,t

关键词： computer vision image classification long-tailed data machine learning

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Prediction of Movie Categories Using Randomized Sequences with Machine Learning 4

Prediction of Movie Categories Using Randomized Sequences wi...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Namitha, Seelam Prathibha, J. Kumar, Alle Aniesh Hariharan, Shanmugasundaram Reddy, Thokala Karthik Kukreja, Vinay Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350305258

Prediction of movie categories from random sequences is tricky and challenging in several sense and has hot wide attention among research community. The study presented here suggests a novel method for categorizing movies based on the order of their frame sequences. The suggested method creates numerous sequences from the movie's frames using a randomized sequence generator, and then utilizes machine learning techniques to forecast the movie's categories based on these sequences. The method is tested against two movie datasets, and the findings reveal that it performs better in terms of efficiency and accuracy than a number of standard methods. The proposed method can be utilized as a promising tool for predicting movie categories, and it can have applications in content-based movie recommendation systems, according to the paper's conclusion. More specifically, the suggested method creates randomised sequences by subjecting the movie's frames to a variety of transformations, including random cropping, flipping, and colour jittering. Overall, using randomised sequences and machine learning algorithms, the research presents a fresh and successful method for predicting movie categories. The suggested method may be used in a variety of fields, including video summarization, content-based video retrieval, and movie recommendation systems. The proposed model will generate the result high accuracy of 92% which is more than another proposed models. © 2023 IEEE.

关键词： Machine Learning Movie Categories Prediction Randomized Sequences

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Dysphonic Voice Pattern Based Parkinson Disease Detection Using Machine Learning Models 2

Dysphonic Voice Pattern Based Parkinson Disease Detection Us...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rhythm Khandelwal, Rajat Badlani, Prerna Saxena, Bhawna Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371055

Parkinson's Disease stands as a widespread cerebral degenerative disorder impacting millions of people across the world. Timely and precise diagnosis of PD is of paramount importance, enabling early interventions and enhancing overall patient care. The presented work focuses on non-invasive detection of PD through voice analysis, targeting early intervention. Dysphonic voice patterns, indicative of PD progression, are analyzed using advanced signal processing and machine learning. Methodology involves collecting voice recordings from PD-affected individuals and healthy controls, with rigorous analysis of acoustic features. This approach minimizes patient discomfort and reduces healthcare costs, potentially revolutionizing PD care and enhancing patient quality of life. This study explored the efficacy of 4 machine learning models - Decision Trees, k-Nearest Neighbors, Support Vector Machine, and Naive Bayes- in Parkinson's Disease (PD) detection. k-Nearest Neighbors classifier demonstrates much better performance compared to the other three, achieving an accuracy of 96.61%. © 2024 IEEE.

关键词： Dysphonic Voice Pattern KNN Machine Learning Parkinson's Disease

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A Mechanism Used to Predict Diet Consumption and Stress Management in Humans Using IoMT

A Mechanism Used to Predict Diet Consumption and Stress Mana...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Rajkumar, K. Bennyhin, Bapanapalli Muddana, Dharani Sampath Hariharan, U. Department of Computer Science and Engineering Karnataka India Chandigarh University Apex Institute of Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350323887

Overeating and stress-eating are both widespread problems in today's culture. Many people in today's society consume more calories than they require, resulting in a variety of health issues such as heart disease, high cholesterol, obesity, stroke, hypertension, and other conditions. Overeating is a common way to cope with stress, boredom, anxiety, sadness, emotional traumas, and other factors. To avoid stress eating and other health concerns, attention to the quality and quantity of food we consume, as well as the length of our meals, the number of calories required for each body type, calorie expenditure, metabolism, and other factors is needed. As a result, our research focuses on users' calorie consumption and notifies them of how many calories they should consume and if they are stress-eating or eating normally. Because food intake is a major factor in a variety of health concerns, keeping a close eye on our eating habits is critical. To that end, we propose a method for an edge platform that detects, classifies, and notifies us about calories and whether we are doing normal eating or stress-eating because many health problems are largely influenced by food consumption. © 2023 IEEE.

关键词： Food supply

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Cutting-Edge Climate Analysis: Combining MLP-GRU and Remote Sensing Technologies

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 348-363页

作者： Shaik, Reddi Khasim Priya, S. Shanmuga Saranya, N. R, Kotteeswaran Ramya, S. Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India SRM Institute of Science and Technology Tiruchirappalli India Department of EIE St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Information Technology Prathyusha Engineering College Chennai India

Climate change poses significant challenges to societies worldwide, necessitating accurate and reliable climate prediction models to inform mitigation and adaptation strategies. The ability to forecast climate variables such as temperature, precipitation, and soil moisture is crucial for a wide range of applications, including agriculture, water resource management, and disaster preparedness. Traditional models have often struggled to capture the complex, nonlinear relationships and temporal dependencies inherent in climate data. In this study, we present a novel Hybrid MLP-GRU Network for climate prediction, leveraging the combined strengths of Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models. This hybrid approach aims to capture both complex, nonlinear relationships and temporal dependencies in climate data, enhancing the accuracy and reliability of predictions. We utilized comprehensive datasets from the Copernicus Climate Change Service (C3S), focusing on essential variables such as temperature, precipitation, soil moisture, and vegetation indices. The data underwent rigorous preprocessing, including normalization, temporal alignment, and feature extraction, to ensure high-quality inputs for the model. The model was trained and validated on extensive climate datasets, demonstrating superior performance metrics with an accuracy of 97.77%, RMSE of 0.140, and MAE of 0.120 on validation data. Comparative analysis with other models, including Linear Regression, Random Forest, LSTM, and CNN, highlights the efficacy of our hybrid approach. The Hybrid MLP-GRU Network consistently outperformed these models in terms of RMSE, MAE, and accuracy. Additionally, hyperparameter tuning, cross-validation, and deployment assessments were conducted to optimize the model’s performance and ensure robust predictions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate data Climate prediction Data preprocessing Feature extraction Gated Recurrent Unit Hybrid MLP-GRU Network Machine learning Multi-Layer Perceptron Nonlinear relationships Temporal dependencies

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