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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2127-2138页

作者： Singh, Vishakha Sharma, Ritesh Singh, Sanjay Kumar Indian Institute of Technology BHU Department of Computer Science and Engineering Varanasi221005 India

Artificial intelligence (AI) has emerged as a powerful tool in computational biology, where it is being used to analyze large datasets to detect difficult biological patterns. This has enabled the design of new drug molecules. In this article, a novel method called hybridized gravitational search algorithm (HyGSA) has been proposed to design novel blood-brain barrier penetrating peptides (B3P2s) with desirable characteristics that enable them to cross the blood-brain barrier (BBB) and deliver neurological drugs directly to the brain. The HyGSA has two important modules in the form of an explainable machine learning classifier (with an accuracy, f1-score, and area under the ROC curve (AUROC) of 84%, 84%, and 91%, respectively) and an explainable deep learning-based B3P2 classifier (with an accuracy, f1-score, and AUROC of 89%, 91%, and 95%, respectively). The former was used to determine the crucial hand-engineered features, and the latter was designed to determine the critical amino acids that play an important role in the BBB penetrability of a peptide. Moreover, the population of particles was sampled at the beginning of each iteration to ensure a good mix of particles with low, average, and high fitness. This was achieved using a novel method that takes inspiration from the subset-sum problem and uses the mean and variance of the distribution of particles. For the pilot study, some B3P2s were discovered and optimized from a set of cell-penetrating peptides. Lastly, a free online tool has been deployed at https://*** to help the scientific community discover and optimize B3P2s in protein sequences. © 2023 IEEE.

关键词： Peptides

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Adaptive Deep Conditional Random Field-Based Blockchain Access with Hybrid Encryption for Data Privacy Preservation

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SN computer science 2024年第8期5卷 1-23页

作者： Viswanadham, Yedida Venkata Rama Subramanaya Panaiyappan, K. Annapurani Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

Data Privacy Preservation (DPP) is a control measures to protect users sensitive information from third party. The DPP guarantees that the information of the user’s data is not being misused. User authorization is highly performed by blockchain technology that provide authentication for authorized user to utilize the encrypted data. Effective encryption techniques are emerged by employing ̣ deep-learning network and also it is difficult for illegal consumers to access sensitive information. Traditional networks for DPP mainly focus on privacy and show less consideration for data security that is susceptible to data breaches. It is also necessary to protect the data from illegal access. In order to alleviate these issues, a deep learning methods along with blockchain technology. So, this paper aims to develop a DPP framework in blockchain using deep learning. Hence, the Adaptive Deep Conditional Random Field (ADCRF) is a deep learning network utilized for accessing the blockchain in a secure manner. The parameters in the ADCRF network are optimized by Improved Running City Game Optimizer (RCGO). For securely accessing the blockchain, the data is stored after the encryption process. The data privacy is ensured by the Hybrid Elgammal-Attribute-based Encryption (HEA) method. Hybrid encryption methods enhance the security during the data preservation process. The numerical findings of the developed model are differentiated from the existing baseline works to ensure the performance of the proposed approach. The experimental findings of the developed model attain 96.35, 96.27, 96.40, 93.48 and 94.85 regarding accuracy, recall, specificity, precision, and F1-score respectively. This analysis could strengthen the developed framework to secure data with the help of user authentication model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive deep conditional random field Blockchain access Data privacy preservation Hybrid Elgammal-attribute-based encryption Improved running city game optimizer

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Heart sound classification method using gammatonegram and SVM

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Multimedia Tools and Applications 2024年 1-37页

作者： Taneja, Kriti Arora, Vinay Verma, Karun Computer Science and Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India

Today cardiovascular diseases have been posing a serious threat to human lives all over the world. Various automated decision-making systems have been proposed by the researchers to help cardiologists to diagnose heart health with more perfection, thus, reducing the frequency of fatalities. This research aims at classifying the Phonocardiogram (PCG) signals captured from the functioning of the cardiac system as normal or abnormal. The methodology proposed here has been validated using the benchmark dataset PhysioNet Computing in Cardiology (CinC) 2016. As of this writing, the studies carried out using contemporary techniques have so far been unable to generate pictorial representation from cardiac audio recordings that match the way the ear processes sound. The present research suggests a unique methodology for the detection of important events in an audio signal using a biologically-inspired depiction of the audio stream through a picture known as Gammatonegram which correlates to the processing of audio in the cochlea membrane of the inner human auditory system. In this study, texture-related features which include Linear Ternary Pattern (LTP), Local Directional Pattern (LDP), Geometric Local Textural Pattern (GLTP), and Local Phase Quantization (LPQ) have been extracted from a visual representation of PCG signal such as Spectrogram, Scalogram, Mel-spectrogram, and Gammatonegram. As compared to the case when researchers were employing other images, it has been noticed that the fusion of the features retrieved from the Gammatonegram has led to an increased overall classification performance metrics. The experiment yielded an overall accuracy of 94.00 % with precision and F1 scores of 91.77 and 93.61 respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Heart

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Forensic Video Watermarking 2

Forensic Video Watermarking

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Elavarasan, S.R. Metilda Florence, S. Department of networking and communications Srm Institute of science and technology Kattankulathur India Department of networking and communications Srm Institute of science and technology Kattankulathur India

ISBN: (纸本)9798350340235

The aim of forensic watermarking is to trace out unauthorized users who are responsible for distribution of illicit digital content. Although there are many approaches for preventing of pirated video in the internet such as video watermarking the content but the real time implementation of the watermarked video and distributing in the streaming platforms is complex and also expensive. In this paper we are going to propose a solution by implementation of a low-cost server-side blind watermarking using a/b watermarking. Although there are traditional watermarking methods for distributing the video, we discussed about the real time watermark generation and embedding and distributing to the users during live streaming. Here we will be dividing live video stream content as A and B to the nginx server the nginx server embed the watermark using a script and store it as an input. When a user requests the live video the *** script sequence a particular watermark content to the user so that for every frame sequence each user gets a different type of video. If suppose when the live video is pirated when can find the user by the sequence of the watermark which is sent to the user. © 2023 IEEE.

关键词： Live video Low cost Nginx server RTMP Server-side watermarking

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MemDefense: Defending against Membership Inference Attacks in IoT-based Federated Learning via Pruning Perturbations

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IEEE Transactions on Big Data 2024年 1-13页

作者： Shen, Meng Meng, Jin Xu, Ke Yu, Shui Zhu, Liehuang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science Tsinghua University Beijing China School of Computer Science University of Technology Sydney Sydney NSW Australia

Depending on large-scale devices, the Internet of Things (IoT) provides massive data support for resource sharing and intelligent decision, but privacy risks also increase. As a popular distributed learning framework, Federated Learning (FL) is widely used because it does not need to share raw data while only parameters to collaboratively train models. However, Federated Learning is not spared by some emerging attacks, e.g., membership inference attack. Therefore, for IoT devices with limited resources, it is challenging to design a defense scheme against the membership inference attack ensuring high model utility, strong membership privacy and acceptable time efficiency. In this paper, we propose MemDefense, a lightweight defense mechanism to prevent membership inference attack from local models and global models in IoT-based FL, while maintaining high model utility. MemDefense adds crafted pruning perturbations to local models at each round of FL by deploying two key components, i.e., parameter filter and noise generator. Specifically, the parameter filter selects the apposite model parameters which have little impact on the model test accuracy and contribute more to member inference attacks. Then, the noise generator is used to find the pruning noise that can reduce the attack accuracy while keeping high model accuracy, protecting each participant's membership privacy. We comprehensively evaluate MemDefense with different deep learning models and multiple benchmark datasets. The experimental results show that lowcost MemDefense drastically reduces the attack accuracy within limited drop of classification accuracy, meeting the requirements for model utility, membership privacy and time efficiency. IEEE

关键词： Internet of things

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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OptiFog: A Framework for Acquiring State Information and Predicting Resource Availability for Task Offloading in Cooperative Fog-Networks

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IEEE Transactions on Services Computing 2024年 1-13页

作者： Alam, Mehbub Ahmed, Nurzaman Ghosh, Shyamal Matam, Rakesh Barbhuiya, Ferdous Ahmed Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science Dartmouth College Hanover USA School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram India

The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task requirements such as latency in task execution, computation costs, etc. So, selecting such a fog node that meets task requirements is a crucial challenge. To choose an optimal fog node, access to each node's resource availability information is essential. Existing approaches often assume state availability or depend on a subset of state information to design mechanisms tailored to different task requirements. In this paper, OptiFog: a cluster-based fog computing architecture for acquiring the state information followed by optimal fog node selection and task offloading mechanism is proposed. Additionally, a continuous time Markov chain based stochastic model for predicting the resource availability on fog nodes is proposed. This model prevents the need to frequently synchronize the resource availability status of fog nodes, and allows to maintain an updated state information. Extensive simulation results show that OptiFog lowers task execution latency considerably, and schedules almost all the tasks at the fog layer compared to the existing state-of-the-art. IEEE

关键词： computer architecture

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Empowering agricultural security with IoT and deep learning driven real-time alert systems

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International Journal of Information technology (Singapore) 2024年第7期16卷 4627-4635页

作者： Singh, Priya Krishnamurthi, Rajalakshmi Department of Computer Science Jaypee Institute of Information Technology

In the realm of smart agriculture, our primary objective is to enhance security in the agriculture field by combining IoT and computer vision technologies. By leveraging these advanced tools, we strive to protect field crops from unauthorized access, aligning with the core principles of smart agriculture that prioritize the safeguarding of agricultural assets. Various governmental initiatives have been innovated to tackle the issue of ensuring food security. However, the majority of farmers face limitation due to insufficient knowledge and financial means, to effectively manage issues such as field monitoring and object detection. Hence, we aim to create an energy-efficient real time system that intelligently detects objects and sends alerts to farmers specifically designed for agricultural purposes. By integrating ultrasonic sensors, Raspberry Pi edge, and real-time trained object detectors like Embed-YOLOv3 and EmbedT-YOLOv3, we achieve superior accuracy, faster processing, and enhanced speed. This system efficiently identifies and categorizes objects, instantly notifying farmers through a real time android application. Identification of objects followed by immediate action can preserve yields, significantly reducing crop damage and strengthening the country’s economy. This proactive approach can alleviate distress among numerous farmers. Achieving an exceptional precision rate of 97%, recall rate of 96%, and accuracy of 95.86%, our system harnesses low-power IoT devices coupled with deep learning to deliver robust crop protection in the agricultural field. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep learning Internet of things (IoT) Object detection Raspberry pi Ultrasonic sensors YOLOv3

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Chi-Square Target Encoding for Categorical Data Representation: A Real-World Sensor Data Case Study

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SN computer science 2025年第3期6卷 1-22页

作者： Anitha, M. Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Applications National Institute of Technology Trichy India Department of Computer Science and Engineering National Institute of Technology Trichy India

Feature engineering is critical for improving machine learning performance (ML), especially when handling categorical data. Traditional encoding methods, such as one-hot and label encoding, often result in challenges like high dimensionality and loss of category significance. To address these limitations, this study introduces a novel Chi-Square Target Encoding (CSTE) approach, which transforms categorical variables into numerical representations by leveraging the chi-square statistic to evaluate the association between categories and the target variable, preserving information effectively. Unlike conventional techniques, CSTE uses Chi-square test values to generate target-based encoded representations, ensuring reliable transformation without information loss. Comprehensive empirical evaluations were conducted on various datasets for binary classification, showcasing CSTE’s superiority over methods like regularized target encoding, basic target encoding, and fuzzification. A case study on real-world sensor data further validated its efficiency and scalability for large-scale data-driven applications. The proposed CSTE method achieved an average increment rate of 3.9447%, surpassing one-hot and fuzzification (1.0430%) and regularized target encoding (1.8308%). Classification outcomes demonstrated F1-scores exceeding 0.90 and AUC values nearing 0.99 across diverse datasets, highlighting its robustness. Furthermore, the reduced dimensionality significantly enhanced inference time while maintaining high accuracy. The CSTE method offers a robust framework for categorical data transformation, addressing limitations of traditional encoding techniques. It improves the interpretability and efficiency of categorical data representation, boosting ML performance. This innovative approach is well-suited for applications across various domains involving categorical data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Categorical data encoding Chi- Square statistics Data distribution preservation Feature engineering Machine learning Target encoding

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