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Exploration of alzheimer disease using design of experiments

Multimedia Tools and Applications 2025年第3期84卷 1079-1101页

作者： Gupta, Pranav Pandey, Kavita Gupta, Sanskar Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

This study examines the use of experimental designs, specifically full and fractional factorial designs, for predicting Alzheimer’s disease with fewer variables. The full factorial design systematically investigates the effects of selected factors, providing a comprehensive analysis of their individual and interactive impacts on Alzheimer’s prediction. On the other hand, the fractional factorial design simplifies the model by selecting key factors and removing non-significant ones. For Alzheimer’s, among all the factors examined, the MMSE (Mini-mental state Examination) score emerged as the most significant, which was further cross-validated using Minitab Software Analysis. Importantly, the findings suggest that the fractional factorial design is a better alternative, particularly when time and cost constraints are present. The results highlight the effectiveness and efficiency of these experimental designs, demonstrating their potential for early detection, prevention, and treatment of Alzheimer’s disease. By contributing to the expanding knowledge base on experimental designs in medical research, this study emphasizes their crucial role in understanding and combating complex diseases like Alzheimer’s. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Design of experiments

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Imbalanced multilabel retinal disease classification using threshold moving and ensemble learning

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Multimedia Tools and Applications 2025年 1-19页

作者： Pendharkar, Gaurav Balaji, Sudharshanan Kumar, B. Muhesh Malathi, G. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

Billions of people worldwide are affected by vision impairment majorly caused due to age-related degradation and refractive errors. Diabetic Retinopathy(DR) and Macular Hole(MH) are among the most prevalent senescent retinal diseases. Machine Intelligence can assist ophthalmologists and clinicians in fast and accurate disease diagnosis by identifying patterns in disease progression for a better healthcare system. In this paper, the Retinal Fundus Multi-disease Image Dataset (RFMiD) is used to design a machine intelligence system with two chief components namely a disease risk classifier, and a multi-label classifier. The disease risk classifier predicts whether the retinal fundus image is infected or not. Based on the prediction of the disease risk classifier, a multi-label classifier can be applied to obtain probabilities for the susceptibility to DR and MH. Finally, an ensemble is employed with the best of 3 models for each classifier. The disease riskpredictor attained a peak F1-score of 88%, while the multi-label classifier achieved an Area Under the Curve(AUC) score of 86%. However, the individual binary classifiers for DR and MH reached maximum F-scores of 91% and 93%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Risk perception

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U-YOLOv3: A Model Focused on Underwater Object Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 87-102页

作者： Sarkar, Pratima De, Sourav Gurung, Sandeep Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Majhitar India Sikkim Manipal University Gangtok India Department of Computer Science and Engineering Techno International New Town Newtown Kolkata India Department of Computer Science and Engineering Cooch Behar Government Engineering College Vill-Harinchowrah West Bengal Cooch Behar736170 India

Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that underwater images are not clear due to several factors such as low light, the presence of small particles, different levels of refraction of light, etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and the modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNet is a deep learning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge-size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features that help to work with the above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation, and added downsampling and upsampling to improve the detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. Precision, recall, F1-score and mAP are used as assessment metrics to assess proposed work. The proposed work compared with SSD, Tiny-YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, KPE-YOLOv5, YOLOv7, YOLOv8 and YOLOv9 single stage object detectors. The experiment on the Brackish and Trash ICRA19 datasets shows that our proposed method enhances the mean average precision for b

关键词： Underwater imaging

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Text steganography: enhanced character-level embedding algorithm using font attribute with increased resilience to statistical attacks

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Multimedia Tools and Applications 2025年第11期84卷 9367-9392页

作者： KL, Shreya Narasimhan R, Bala Krishnan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India

Text steganography, the science of hiding secret messages in innocent-looking text documents ensures the secrecy of the embedded secret. Cryptography, on the other hand, encrypts and converts the secret message into an unintelligible form ensuring confidentiality, integrity, and authentication of the message. It is to be noted that both steganography and cryptography are expected to work together hand-in-hand in order to attain a better security. Hence, a methodology that combines techniques from both steganography and cryptography is an unavoidable one for the complete protection of sensitive data. This paper aims to achieve one such methodology. The best existing method which works in similar direction is observed to use character spacing feature of text documents to embed the secrets. The method is reported to attain an average embedding capacity of ≈2 bits/cover-character and 8-bits/distortion with high imperceptibility and considerable cryptographic security. However, during experimentation, it is noticed that the methodology has the potential vulnerability of leaving clues for attackers with a probability of 0.51% which paves way for possible statistical attacks namely frequency analysis. This vulnerability occurred because of the constant left circular shift operation that is performed after embedding each secret character. This paper aims to address this vulnerability by proposing a novel modification that facilitates the methodology to perform both left and right circular shifts in an unbiased manner, based on the embedded secret, without affecting the notable achievements of the existing methodology. The proposed modification is successfully implemented, and further analysis indicated that the performed modification has in fact embedded the secret characters uniformly thereby reducing the standard deviation by 24%. This increased the resilience of the extended method to potential attacks that rely on statistical techniques. © The Author(s), under exclusive

关键词： Cryptography

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A brief review on Quantum Key Distribution Protocols

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Multimedia Tools and Applications 2025年 1-40页

作者： Kumar, Mandeep Mondal, Bhaskar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna800005 India

Development in Quantum computing paves the path to Quantum key distribution (QKD) by using the principles of quantum physics. QKD enables two remote parties to produce and share secure keys while removing all computing constraints on an adversary. The basic physics laws are used to identify any outside parties eavesdropping on the key exchange. In recent years many revolutionary developments in the field of QKD have been developed to overcome security and networking constraints. This survey provides an overview of the QKD protocol’s evolution and quantum network architecture. The paper also demonstrates QKD deployment techniques and elements of the QKD network. It also highlights ongoing design challenges by considering security and error estimation and correction, in contrast to studies concentrating on optical channels and equipment. Finally, this paper examines the possible directions for future research and offers design principles to guide the development of QKD and its related area. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Quantum computers

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Feedback-based trust module for IoT networks using machine learning

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 78-91页

作者： Iqbal, Sania Qureshi, Shaima Department of Computer Science and Engineering National Institute of Technology Jammu & Kashmir Srinagar India

The increasing emergence of IoT in healthcare, industrial automation, manufacturing, infrastructure, business and the home undoubtedly provides more conveniences in different aspects of human life. Any IoT security and trust solution must consider the heterogeneity and mobility of IoT devices, and the features of other devices connected to the network. Traditional security measures face computation, storage and communication challenges in securing IoT, and the increasing reliance on IoT services and applications has exacerbated this issue. It is crucial to identify a security management model to handle these constraints. Trust as a security regime in the IoT has far-reaching implications. This paper proposes a novel trust-based security mechanism by combining machine learning and traditional summations to determine a node’s trustworthiness in an IoT environment in milliseconds. Based on this, a computational trust management model is designed by aggregating direct and feedback-based trust information. The model parameters like the trust breach window and trust re-initiation point are selected based on extensive experimentation to dynamically calculate each node’s trust value to reach the proposed model’s peak performance. The model’s effectiveness is verified through simulation. In 500 iterations, the proposed model achieved 97% accuracy with a run time of 0.37 seconds. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Network security

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Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

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Inteligencia Artificial 2024年第74期27卷 133-151页

作者： Ikechukwu, Agughasi Victor Department of Computer Science & Engineering Maharaja Institute of Technology Karnataka Mysore571477 India

Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for innovative and widely accessible diagnostic tools has never been more paramount. While spirometry tests serve as conventional diagnostic benchmarks, their reach remains limited, especially in regions with constrained medical resources. The presented research harnesses deep learning algorithms to facilitate early-stage COPD detection, specifically targeting Chest X-rays (CXRs). The clinically annotated VinDR-CXR dataset provides the primary foundation for model training, complemented by incorporating the ChestX-ray14 dataset for initial model pre-training. Such a dualdataset strategy augments model generalization and adaptability. Among several explored Convolutional Neural Network (CNN) architectures, the Xception model emerges as a frontrunner. Through transfer learning methodologies, this model produces a noteworthy recall rate of 98.2%, markedly surpassing the metrics of the ResNet50 model. Recognizing the imperative for transparency in AI applications in medical imaging, the research integrates essential explainability approaches viz: Gradient Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). These techniques elucidate the AI’s decision-making process, offering invaluable visual and analytical insights for fostering trust among medical professionals. In essence, this study not only underscores the potential of integrating AI with medical imaging for COPD detection but also accentuates the pivotal role of transparency in AI-driven medical interventions. © IBERAMIA and the authors.

关键词： Decision making

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Architectural framework for multiplayer cooperative cloud gaming to optimise quality of service

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International Journal of Networking and Virtual Organisations 2024年第2期30卷 174-205页

作者： Srilakshmi, Nimmagadda Kannan, Naresh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The gaming industry is getting more attraction from cloud services providing gaming applications for cooperative multiplayer gaming. Real-time services like cloud gaming are possible by performing necessary process-intensive tasks within the cloud. In this paper, an architectural model for supporting cooperative gaming towards multiplayer is proposed to improve the quality of service in terms of bandwidth and latency compared to existing architectural models. According to this study, the time it takes to change a video has grown by 95% compared to how well ad hoc mobile cloudlets and the cloud do at sharing videos. Although P-frames, I-frames, and B-frames account for 23%, 78%, and 94% of the resource consumption, density in intra-stream P-frames is also considered. Similarly, resource and CPU use based on skewness are effective compared to mobile devices' 95% faster video sharing and 77% shorter link delays. © 2024 Inderscience Enterprises Ltd.

关键词： Quality of service

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HeRo: Heuristic-Based Routing in Payment Channel Networks

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 56-60页

作者： Mishra, Shruti Aggarwal, Virat Pal, Sujata Agarwal, Vidushi Indian Institute of Technology Ropar Department of Computer Science and Engineering Rupnagar140001 India

Payment channels support off-chain transactions by enhancing transaction speed and reducing fees in the main blockchain. However, the costs and complexity of the network increase as we increase the size of the network. To address these challenges, we propose Heuristic-Based Routing with Scheduling (HeRo) combining heuristic-based routing and scheduling techniques in Payment Channel Networks (PCNs). HeRo achieves a cost reduction of 32.71% and 73.08% compared with multi-charge PCN (MPCN-RP) and Dijkstra algorithms, respectively. © 2019 IEEE.

关键词： Network routing

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Intelligent surveillance support system

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Discover Internet of Things 2023年第1期3卷 9页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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