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An effective medical image fusion method utilizing moth-flame optimization and coupled neural P systems

Neural Computing and Applications 2025年第16期37卷 10037-10077页

作者： Dinh, Phu-Hung Le, Thi-Hong-Ha Giang, Nguyen Long Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Hong Duc University 565 Quang Trung Dong Ve Thanh Hoa Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi Viet Nam

Medical image fusion is crucial for various applications, yet existing fusion models often encounter issues such as low quality, information loss, and insufficient contrast. This paper presents a novel approach to medical image fusion that directly addresses these challenges by enhancing brightness and contrast, improving overall image quality, and preserving essential structural features. Our method begins by decomposing input images into a base component and two detail components using a three-component decomposition (TCD) method with a truncated Huber filter (THF). The detail components are subsequently fused using an integrated approach combining the structure tensor saliency detection operator (STSDO), distance-weighted regional energy (DWRE), fast guided filter (FGF), and coupled neural P system (CNPS). The base components are then fused through an adaptive method guided by the moth-flame optimization (MFO) algorithm. To evaluate our approach, we used a dataset of 120 pairs of medical images and assessed performance with metrics such as QMLI, QCI, QAG, QAB/F, QP, QW, QE, and QCB. Experimental results demonstrate that the proposed model significantly enhances brightness and contrast and more effectively preserves information compared to recent methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image fusion

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Opinion Mining-Based Predictive Model Based on Social Media Responses 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Bhattacharya, Priyanka Guha, Sutirtha Kumar Department of Computer Science & Engineering Academy of Technology West Bengal Hooghly India Department of Computer Science & Engineering Meghnad Saha Institute of Technology West Bengal Kolkata India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

In the present-day scenario, it is observed that the effect of any natural or man-made disaster creates a havoc mess on society. The change in human behavior plays a crucial part in achieving sustainability. The development of a mathematical model to measure and analyze the relationship between environmental stability and human behavior can be a valuable tool in this effort. The proposed predictive model is helpful to identify the specific behaviors and the factors that can affect the harmony of the surroundings, to promote more sustainable outcomes. This paper would be helpful for the development of strategies and interventions to change behavior and promote more sustainable practices, both at the individual and societal levels. It has been explained in this paper that the stability of society toward any kind of disaster is dependent on social media as nowadays social media is the basic platform for communication in society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Hybrid dense matching features for cloud-based face recognition

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International Journal of Cloud Computing 2023年第2-4期12卷 399-423页

作者： Shreekumar, T. Karunakara, K. Faculty of Computer Science and Engineering Mangalore Institute of Technology and Engineering Mijar Moodabidre India Faculty of Information Science and Engineering Sri Siddartha Institute of Technology Sri Siddhartha Academy of Higher Education Tumakuru India

Cloud computing is a computing service done not on a local device but an internet connection to a data centre infrastructure. The cloud computing system also provides a scalability solution where cloud computing can increase the resources needed when doing larger data processing. In recent years, face bio-metric plays an important role in biometric authentication, where security is the primary concern. The two major difficulties to be addressed in face recognition are illumination and pose variation. This work proposes a cloud computing-based face recognition technique by consolidating four-patch LBP and the local landmark features to the acquired feature vector. After that, the SVM is utilised to recognise the individual. The feature set used for training and testing is also made available in the cloud. At some stage in the training phase, the kernel parameters of SVM are optimised with GWO to improve the recognition performance. This method yields a maximum recognition accuracy of 99.00% with LFW and 98.0% accuracy with YTF. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Support vector machines

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Survey of neurocognitive disorder detection methods based on speech, visual, and virtual reality technologies

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 421-472页

作者： Tian ZHENG Xinheng WANG Xiaolan PENG Ning SU Tianyi XU Xurong XIE Jin HUANG Lun XIE Feng TIAN Institute of Sofiware Chinese Academy of SciencesBeijing 100190China School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical ScienceBeijing 100005China

The global trend of population aging poses significant challenges to society and healthcare systems,particularly because of neurocognitive disorders(NCDs)such as Parkinson's disease(PD)and Alzheimer's disease(AD).In this context,artificial intelligence techniques have demonstrated promising potential for the objective assessment and detection of *** contactless screening technologies,such as speech-language processing,computer vision,and virtual reality,offer efficient and convenient methods for disease diagnosis and progression *** paper systematically reviews the specific methods and applications of these technologies in the detection of NCDs using data collection paradigms,feature extraction,and modeling ***,the potential applications and future prospects of these technologies for the detection of cognitive and motor disorders are *** providing a comprehensive summary and refinement of the extant theories,methodologies,and applications,this study aims to facilitate an in-depth understanding of these technologies for researchers,both within and outside the *** the best of our knowledge,this is the first survey to cover the use of speech-language processing,computer vision,and virtual reality technologies for the detection of NSDs.

关键词： Neurocognitive disorder Speech-language computer vision Virtual reality Artificial intelligence

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Complex Social Networks: Dynamics, Domains, and Dimensions 1

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Gera, Suruchi Sinha, Adwitiya Department of Computer Science and Engineering JSS Academy of Technical Education Noida India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (数字)9789819986613

ISBN: (纸本)9789819986606

The online social platforms witnessed enormous growth in its networked structure as users continue to connect and interact through e-social dialogues. This eventually causes the transformational emergence of online social systems into complex networks. These large-scale networks inherently allow social entities to get engaged in diverse forms of interactions, thereby resulting in dynamic and complex user behavior. This raises many distinct challenges in social networks, which requires cross-dimensional analysis of the complex information for societal benefits. Understanding these complex systems has become crucial for analyzing the behavioral characteristics for drawing essential inferences. Analysis and evaluation of social complex networks in the context of several parameters are performed with prerequisites of graph theory, computational statistics, and probabilistic models. Our research provides a review of the evolving challenges in the social domain with case studies being conducted over multiple social systems. The study covers major aspects of social network theory that reveal communities and the impact of the central behavior of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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Speak to your Database: Streamlining Human-to-Database Communication with NLP 1

Speak to your Database: Streamlining Human-to-Database Commu...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Sthuthi, B. Sivani, J.C. Sathyalakshmi, S. Kumar Yadav, Kamleshwar Global Academy of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350339727

The paper demonstrates the successful integration of recent enhancements in Natural Language Processing (NLP) with the goal of generating accurate and meaningful Structured Query Language (SQL) queries from human language. The focus is on the methods that we have implemented in the project. The methodology we propose in this paper relies entirely on NLP techniques. Our system accepts user queries in English, which are then transformed into SQL statements using our NLP-based approach, before being executed against the database to retrieve the desired results. The student database was used in our project, as it provides a real-world example of how our NLP-based approach can be applied to retrieve data from educational institutions. The paper also outlines the various challenges and limitations faced by our system when converting human language into SQL. The paper concludes by discussing the broader implications of our approach for the field of database querying, and how it could lead to the development of more intuitive and user-friendly interfaces for interacting with databases and outlines potential directions for future improvements. © 2023 IEEE.

关键词： Query languages

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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An Improved Encryption Technique for IPv6 Networks 3rd

An Improved Encryption Technique for IPv6 Networks

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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Cherukuru, Pavani Hanji, Bhagyashri R. Information Science of Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India

ISBN: (纸本)9789819795772

Encryption method for IPv6 networks provides new features and security concepts with a lot of profoundness likely Authentication Header and Encapsulating Security Payload Header (ESP). This paper shows the method of preventing certain types of network attacks over the internet and open issues of IPv6 security. It mainly deals with the Authentication and Encryption methods such as RSA and DES that are being used for years, and their drawbacks in IPv6 environment to enhance the multilevel security. There are three levels of encryption for security enhancement. The first level includes the conversion of a string of characters into corresponding ASCII values and then encrypt these ASCII values into random prime factors so that uniqueness is maintained. It has repeated patterns that is removed in the second level, where it uses two private keys which are prime for even and odd numbers and perform the addition with the first level results and convert into a binary form. In the third level, 2’s complement is applied on binary form and sent it through ESP packet of IPv6. On the other side, all the levels are repeated in reverse to get the original message. The experimental result shows the higher level of security for IPV6. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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Hybrid blockchain based medical data sharing with the optimized CP-ABE for e-Health systems

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International Journal of Information technology (Singapore) 2024年第1期16卷 121-130页

作者： Mishra, Anil Kumar Mohapatra, Yogomaya School of Computer Science and Engineering Trident Academy of Technology Odisha Bhubaneswar India Department of Computer Science and Engineering C.V. Raman Global University Odisha Bhubaneswar India

While e-Health systems must prioritise security and privacy, sharing medical data can help improve diagnostic accuracy. Because of its immutability, Blockchain (BC) is now being suggested as an exciting solution for sharing Personal Health Information (PHI). However, sharing PHI must improve patient privacy and security. As a result, this paper describes a secure medical data sharing system. Private BC, Consortium BC (CBC), and optimized cryptography are all included in the proposed system. To ensure patient privacy, PHI is encrypted using optimised ciphertext policy attribute based encryption (CP-ABE). In particular, replicated attributes amendment (RAA) is proposed to improve CP-ABE performance. For each replicated attribute, this RAA achieves a single share value. PHI ciphertexts are stored as blocks in the hospital’s private BC. Furthermore, the PHI keyword is saved to the CBC. By validating the keyword and getting access to the private BC, the doctor may retrieve all patient’s PHI. The article’s findings show that the proposed system achieves lower storage overhead, encryption time, and decryption time. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain (BC) CBC e-Health system Replicated attributes amendment

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Minimum Epsilon-Kernel Computation for Large-Scale Data Processing

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Journal of computer science & technology 2022年第6期37卷 1398-1411页

作者： Hong-Jie Guo Jian-Zhong Li Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Kernel is a kind of data summary which is elaborately extracted from a large *** a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate *** is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive *** this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data *** the open problem presented by Wang et *** whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its *** to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve *** prove that the MC problem and the MK problem can be Turing-reduced to each ***,we discuss the update of MK under insertion and deletion operations,***,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of *** efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic *** show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,*** speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic ***-SCMK runs up to three times faster than SCMK on synthetic datasets.

关键词： approximate query processing kernel large-scale dataset NP-hard

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