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Secure Block Chain-Based Healthcare Sensitive Data Prediction Using Pragmatic Quasi-Identifiers in a Decentralized Cloud Environment

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SN computer science 2024年第1期5卷 1-14页

作者： Punithavathi, S. Jeyalaksshmi, S. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Hindustan College of Arts and Science Chennai India Department of Information Technology School of Computing Science Vels Institute of Science Technology and Advanced Studies Chennai India

Security is essential for all facts, information sharing around the internet and maintaining personalized information. In recent days, the healthcare industry needs privacy-preserving to keep personalized data from others containing sensitive information. Due to increasing security breaches, fundamental sharing problems with personalized details shared on the internet lead to security problems. To resolve this problem, we propose a Secure Blockchain-based Healthcare-sensitive data prediction using a Pragmatic quasi identifier in a decentralized cloud environment. Pragmatic quasi-sensitivity Identification (PQSI) based Privacy-preserving for securing personalized records Using Hyper Recurrent feature classification for improved cloud security. Initially, the sensitivity fitness value of the feature is estimated through Intensive feature success rate (IFSR) and clustered by marginal subset features. By marginalizing the sensitivity threshold frequency rate, the components are extracted by pragmatic quasi-sensitive identifier and classified using Hyper recurrent Neural classification (HRNC) to find the sensitive and non-sensitive records based on frequency fitness weight to split and store perception as individually in the private cloud. Further, to improve the security level Elliptic Curve Cryptography (ECC) based master node authentication technique is used in the blockchain concept. Peer end Block chain principle is applied to secure the sensitive data. To ensure the sensitive records are sanitized into data blocks to assure in a blockchain environment. This proposed system produces higher prediction accuracy than other methods and achieves higher sensitivity and specificity rate performance. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing ECC Hyper recurrent feature classification Privacy preservation Security Sensitive data analysis

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Track Damage And Obstacle Detection Using Multi Sensor Fusion Framework 15

Track Damage And Obstacle Detection Using Multi Sensor Fusio...

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

作者： Priya, S.Padma Abarna, P.S. Devi, C.Manjula Hammed, S. Shahul Karthikeyan, M.P. Saravanan, K.Muthu Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India Velammal college of Engineering and Technology Department of Information Technology Madurai India

ISBN: (纸本)9798350370249

The integration of Internet of Things (IoT) technology into railway track monitoring systems marks a pivotal advancement in ensuring the safety and efficiency of railway operations. Through strategic deployment of sensors along the tracks, this innovative system offers real-time detection of obstacles, furnishing vital data for timely intervention to avert potential accidents. This capability not only elevates passenger and crew safety but also bolsters the reliability of railway transportation systems as a whole. The system constantly scans the track ahead of the train for any anomalies using sensors positioned toward the front of the vehicle. The system immediately notifies the application faults it detects, enabling prompt action. Additionally, it updates the status of an IoT platform and displays it for personnel on board via an LCD panel. An audio alarm is generated in the event of critical sensor readings to ensure prompt attention. By lowering potential risks and responding quickly to track problems, This IoT-based solution holds the promise of revolutionizing railway operations by furnishing augmented safety, efficiency, and reliability, thereby addressing the burgeoning demands of contemporary transportation infrastructure. © 2024 IEEE.

关键词： InfraRed Sensor Railway safety reliability track monitoring Ultrasonic Sensor

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Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

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computers, Materials & Continua 2022年第9期72卷 4645-4661页

作者： Yin Liang Gaoxu Xu Sadaqat ur Rehman Faculty of Information Technology College of Computer Science and TechnologyBeijing Artificial Intelligence InstituteBeijing University of TechnologyBeijing100124China Department of Natural and Computing Science University of AberdeenAberdeenU.K

Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease ***,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease *** this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD *** model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous *** model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model *** performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out *** showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction *** also localized important FC features and brain regions associated with ASD ***,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.

关键词： Autism spectrum disorder diagnosis resting-state fMRI deep neural network functional connectivity multi-scale attention module

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Generous or Selfish? Weighing Transaction Forwarding Against Malicious Attacks in Payment Channel Networks

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Journal of computer science & technology 2022年第4期37卷 888-905页

作者： Yi Qin Qin Hu Dong-Xiao Yu Xiu-Zhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer and Information Science Indiana University-Purdue University IndianapolisIndianapolis 46202U.S.A.

Scalability has long been a major challenge of cryptocurrency systems,which is mainly caused by the delay in reaching consensus when processing transactions *** an effective mitigation approach,the payment channel networks(PCNs)enable private channels among blockchain nodes to process transactions off-chain,relieving long-time waiting for the online transaction *** state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing,scheduling,and initial deposits,as well as preventing the system from security and privacy ***,the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely *** fill this gap,we employ the game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion *** study is progressive,as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of *** analysis is complementary,since we utilize not only the classic game theory with the complete rationality assumption,but also the evolutionary game theory considering the limited rationality of players to portray the evolution of *** results of numerous simulation experiments verify the effectiveness of our analysis.

关键词： blockchain payment channel network game theory

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The Relationship Between Nomophobia, Emotional Intelligence, Interpersonal Problem-Solving, Perceived Stress, and Self-esteem Among Undergraduate Students

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Journal of technology in Behavioral science 2023年第1期8卷 27-35页

作者： Karaoglan Yilmaz, Fatma Gizem Yilmaz, Ramazan Erdogdu, Fatih Faculty of Science Department of Computer Technology & Information Systems Bartin University Bartin Turkey Department of Computer Technologies Ereğli Vocational School Bulent Ecevit University Zonguldak Turkey

Nomophobia is derived from “no mobile phobia”;it is defined as the fear of being away from the possibility of communication by mobile phone. Since mobile phones are used actively in many fields, from communication to social media, from the stock market to e-commerce, users can practically follow developments in their areas of interest with their mobile phones. This situation reveals the need for users to keep their phones with them all the time and can trigger the development of nomophobia feelings. This fear can become increasingly common among mobile phone users today and may require clinical interventions due to its consequences. Researchers state that the investigation of factors that cause nomophobia can inform subsequent interventions and guidance studies to overcome this fear. Various studies have been carried out to determine the causes of nomophobia. In these studies, the importance of individual psycho-social factors is revealed. However, it is emphasized that dimensions related to psycho-social factors need to be explored. One of them is the relationship of nomophobia with emotional intelligence, interpersonal problem-solving, perceived stress, and self-esteem. This study investigated the structural relationships between university students’ nomophobia and emotional intelligence, interpersonal problem-solving, perceived stress, and self-esteem. The research was carried out on 543 university students. Fifty-seven percent of the students participating in the study were female, and 43% were male. Path analysis was performed in the analysis of the data. The results of the research reveal that as the interpersonal problem-solving skills of university students improve, nomophobia decreases. Depending on the development of emotional intelligence, students’ interpersonal problem-solving skills increase. In addition, the development of emotional intelligence reduces the level of perceived stress. In line with the findings obtained from the research, various sugges

关键词： Emotional intelligence Interpersonal problem-solving Nomophobia Perceived stress Self-esteem Undergraduate students

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A Novel Framework for Detection of Harmful Snakes Using YOLO Algorithm

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SN computer science 2024年第1期5卷 1-10页

作者： Naresh, E. Babu, J. Ananda Darshan, S. L. Shiva Murthy, S.V.N. Srinidhi, N.N. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Information Science and Engineering Malnad College of Engineering Karnataka Malnad India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal India Department of CSE SJC Institute of Technology Chikkaballapur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The spotlight of our discussion here is on developing a model for detecting snakes present in the agricultural fields using cameras and machine learning algorithms. The idea is to construct a machine learning model to identify the snakes, which will be trained on a vast set of images of snakes. These images will be carefully selected to include the snake patterns, skin color of different species of snakes (extraction of the unique features). This is done to train the model properly to identify different types of snakes that the farmer encounters in the fields. Most famers are unaware and have a need for a way to detect these snakes so that they can either avoid them or get rid of them. Hence, this system can be implemented to prevent thousands of deaths and can help famers alleviate the repercussions of snake bites. Mistakes in recognizing potentially harmful animal species based only on visual cues are major contributors to the high death toll from venomous animal attacks. Since they spend so much time in the fields, where rice and wheat are cultivated, farmers are at a higher risk of being bitten by a snake than the general population. Because of their ignorance, illiterate farmers are more inclined to believe in superstitions, which can lead to their untimely deaths from snakebites despite medical intervention. Animals that would normally pose no threat to humans are responsible for the deaths of thousands of people every year because of environmental factors. However, because it is hard for humans to recognize these dangers, a new design paradigm has been developed to make it simpler. Researchers in the field of animal biology can use it to search for endangered species. Predators may enter gardens and green areas like tea and coffee plantations. There is not yet a plan in place to implement automated sorting for discovering distinctions. By applying the suggested framework to photographs of potentially dangerous animals, several factors useful for studying anim

关键词： Classifier training Convolutional neural network Farmer deaths Feature extraction Snake detection YOLO algorithm

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Comparisons of Deep Learning Approaches to Detect Lung Cancer through Efficient computer Based CT-based Screening

Comparisons of Deep Learning Approaches to Detect Lung Cance...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Natafji, Wael Einarson, Daniel Kristianstad University Department of Information Technology Kristianstad Sweden Kristianstad University Department of Computer Science Kristianstad Sweden

ISBN: (纸本)9798350394528

The avoidance of mortality in lung cancer is highly dependent on finding defects in the lungs early, to initiate effective treatments in time. Most often, lung disorders are diagnosed and treated using chest radiographs and CT scans. Methods based on machine learning can complement human observations and increase precisions of accuracy by mapping an CT image against a trained artificial neural network. The efficiency and accuracy of training such a network, however, depends on the availability of the performance of an underlying computer system, and the quality and size of images. The use of neural network structures with high intrinsic performance is therefore significant. This contribution focuses on comparisons between different Convolutional Neural Networks and formats on datasets to contribute to a good basis for decision-making in the context of possible lung cancer. © 2024 IEEE.

关键词： computerized tomography

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AI Simulation, IoT, and Digital Twin for Smart Campus 5

AI Simulation, IoT, and Digital Twin for Smart Campus

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5th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Pexyean, Tanapeak Saraubon, Kobkiat Nilsook, Prachyanun Bangkok Suvarnabhumi University Department of Information Technology and Digital Innovation Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer Science and Information Bangkok Thailand King Mongkut's University of Technology North Bangkok Division of Information and Communication Technology for Education Faculty of Technical Education Bangkok Thailand

ISBN: (纸本)9798331505998

The objective of this research is to Present the use of Artificial intelligence Simulation(AI Simulation), Internet of Things(IoT) and Digital Twin technology to Intelligently To develop the energy management potential of the campus intelligently by using technology to improve the environment, making the system smarter and more connected. This helps to make living in an urban community or university more comfortable. The adoption of various technologies has led to development in many fields, such as smart cities, smart homes, smart robots, smart machines Smart cars, smart agriculture, smart healthcare, and smart traffic control systems have the challenge of developing systems that allow devices to connect to the system quickly. One of the interesting ideas is Smart Campus, which is an educational institution that creates learning to develop individuals to be able to improve the world. The use of Internet of Things (IoT) technology, artificial intelligence, simulation, and the creation of digital twins. The use of IoT helps collect and exchange data from various devices in the network, enabling effective monitoring and control of the operation of various systems. Simulation allows for the creation of different scenario models to experiment and improve the system before it is actually implemented. It is a virtual model of a system or device to be used to track electricity consumption and optimize electrical energy management in real time. The application of these technologies in smart campuses will help to manage resources more efficiently, such as electrical energy management. Improvement of the transportation system within the university Security, and enhancing modern learning experiences. Developing the university into a smart campus not only improves operational efficiency but also helps to use energy efficiently. Reduce energy costs Reduce carbon dioxide emissions and create a sustainable and environmentally friendly learning environment both now and in the futur

关键词： Smart agriculture

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The Role of ERNIE Model in Analyzing Hotel Reviews using Chinese Sentiment Analysis

The Role of ERNIE Model in Analyzing Hotel Reviews using Chi...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Jie, Lee Hao Ayyasamy, Ramesh Kumar Sangodiah, Anbuselvan Jalil, Norazira Binti A Krishnan, Kesavan Chinnasamy, P. University Tunku Abdul RahmanKampar Faculty of Information and Communication Technology Malaysia Faculty of Computing and Engineering Quest International University Ipoh Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350348217

This research aims to find the best deep learning model to do Chinese sentimental analysis. BERT's model may work well in the English language but not work in the Chinese language. English is easier to encode in embedding space, and words are separated by space characters, unlike Chinese words, which do not exist separate by the area. BERT Chinese model work on character level encoding is not enough to make the BERT model adapt to Chinese NLP tasks. The BERT Model and ERNIE models are the current state of art pre-training language models. Word level embeddings and input is needed to improve the BERT model to perform in Chinese Sentiment Analysis. With the motivation to improve the Chinese Sentiment Analysis, this study will explore the role of different models to propose a better version of the ERNIE model. This study will limit the scope to improve Chinese sentiment analysis among different NLP tasks. © 2023 IEEE.

关键词： Sentiment analysis

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Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

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computers, Materials & Continua 2022年第9期72卷 5041-5058页

作者： Maqsood Ahmad Noorhaniza Wahid Rahayu A Hamid Saima Sadiq Arif Mehmood Gyu Sang Choi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia86400 Parit RajaBatu PahatJohorMalaysia Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38542Korea

Mental health signifies the emotional,social,and psychological well-being of a *** also affects the way of thinking,feeling,and situation handling of a *** mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in *** health problems are rising globally and constituting a burden on healthcare *** diagnosis can help the professionals in the treatment that may lead to complications if they remain *** machine learning models are highly prevalent for medical data analysis,disease diagnosis,and psychiatric *** research addresses the challenge of detecting six major psychological disorders,namely,Anxiety,Bipolar Disorder,Conversion Disorder,Depression,Mental Retardation and *** challenges are mined by applying decision level fusion of supervised machine learning algorithms.A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the ***,to reduce the impact of a conflicting decision,a voting scheme Shrewd Probing Prediction Model(SPPM)is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine(RF+GBM).This research provides an intuitive solution for mental disorder analysis among different target class labels or groups.A framework is proposed for determining the mental health problem of patients using observations of medical *** framework consists of an ensemble model based on RF and GBM with a novel SPPM *** proposed decision level fusion approach by combining RF+GBM with SPPM-MIN significantly improves the performance in terms of Accuracy,Precision,Recall,and F1-score with 71\%,73\%,71\%and 71\%*** framework seems suitable in the case of huge and more diverse multiclass ***,three

关键词： Mental health diagnosis machine learning depression shrewd probing diagnostic approach

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