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Drug–target interactions prediction based on similarity graph features extraction and deep learning

Neural Computing and Applications 2025年第6期37卷 4303-4322页

作者： Torkey, Hanaa El-Behery, Heba Attia, Abdel-Fattah El-Fishawy, Nawal Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science and Engineering Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Identifying drug–target interactions (DTIs) is a critical step in both drug repositioning. The labor-intensive, time-consuming, and costly nature of classic DTI laboratory studies makes it imperative to create efficient computer algorithms to forecast possible DTIs. However, current computational approaches that predict potential drug–target interactions (DTIs) suffer from some limitations, like finding the best similarity measures or negative samples, and thus require substantial performance improvement. This study proposes an integrated approach based on feature representation and deep learning to predict DTIs. We extract the relevant features of drugs and proteins from heterogeneous networks using graph mining techniques. The proposed approach constructs a heterogeneous graph from the known drug–protein interactions, protein–protein, and drug–drug similarities. Then applying two feature extraction techniques to extract the features, then utilizing these features in training a deep learning model to predict the potential DTIs. Also, a novel algorithm is proposed to find the negative samples based on the drug and protein similarity matrices. Four Benchmark datasets are used to evaluate the proposed approach. Our approach achieves the highest AUC (area under the ROC curve) across all datasets (0.98) with around 2% increases over the existing methods. Experimental results demonstrate that our proposed approach outperforms the baseline methods in predicting DTI, and our negative sample-identifying algorithm could be established as a competitive solution. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Online aggression detection using ensemble techniques on fine-tuned transformer-based language models

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International Journal of computers and Applications 2024年第8期46卷 567-579页

作者： Chinivar, Sneha Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Department of Computer Science and Engineering Bangalore University Karnataka Bengaluru India Department of Computer Science and Engineering Dayanand Sagar College of Engineering Karnataka Bengaluru India

The widespread adoption of Online Platforms for our day-to-day life is increasingly contributing to the rise of Online Aggression and its escalation. Consequently, there is a need for a robust mechanism that could automatically recognize online aggressive content to foster a safer and more respectful online environment. In this work, we explored the usage of four well-known Transformer-based Language Models i.e. BERT, RoBERTa, ALBERT, and XLNet, and on top employed three varied kinds of Ensemble Techniques such as Logistic Regression, Random Forest, and XGBoost on the predictions obtained from those standalone Transformer Models and aim to actively contribute to the advancement of automated Cyber Aggression Identification system. We identified the noise involved and a few outright erroneous data labels in the popular shared task Aggression dataset TRAC-1 and developed an improvised dataset called ReLbTRAC-1 by resolving the identified issues. The proposed models are evaluated on improvised TRAC-1, and TRAC-2 datasets, and they outperformed the state-of-art methods on the TRAC-1 dataset and most existing approaches on the TRAC-2 dataset, including all individual Transformer-based classifiers employed in the proposed Ensemble techniques, obtaining admirable results on all metrics considered (such as Accuracy of 0.6305, and 0.7550 and weighted F1-Score of 0.6518, and 0.7512 on TRAC-1 and TRAC-2 datasets respectively). © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational linguistics

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Plan of Block-chain Enabled Confirmed Key Management Protocol for Internet of Medical Things Development 3

Plan of Block-chain Enabled Confirmed Key Management Protoco...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Jayasri, R. Jayakumar, D. Joshila Roselin, S. Ramkumar, M.O. Ifet College of Engineering Computer Science & Engineering Villupuram India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

With the emergence and influence of the Internet on the healthcare industry, it necessitates the need to preserve, access, and update medical records from anywhere and at any time. Medical discrepancies associated with Electronic Health Records (EHR) must be addressed, as it has privacy and security issues. The lack of consistency was certainly the most critical issue, but it is far from the only issue with EHR. Decentralized cloud models based on blockchain platforms have previously been proposed and deployed to address interoperability and privacy concerns. Other issues, though, remain. Three of the most critical concerns are scalability, usability, and accessibility. This research study presents a Secure eHealth Framework (SeFra) based on a progressive temporal blockchain method to overcome the aforementioned concerns. Context-based Merkle Tree (CBMT) and Context-Based Access Control (CBAC) are two of SeFra's core building blocks that are specifically focused on establishing security, increased integrity checking, and access control approaches. Interoperability and scalability issues are also handled through the use of temporal characteristics in conjunction with HL7 protocols, as well as data management using the Interplanetary File System (IPFS). The results show that temporal shadow concepts and context-based features have an impact on the proposed framework's capacity to safely manage the Personalised Micro Ledger (PML). It also aids in the collection of medical data in text format with temporal aspects and its dissemination to all other peers in line with the restrictions set in the context-based smart contract. © 2022 IEEE.

关键词： Blockchain

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TRE-DSP:A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

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Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science and Engineering University College of EngineeringTindivanamMelpakkamTamil nadu 604001India Department of Computer Engineering National Institute of Technology KurukshetraIndia

With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every *** to securely share data between the IoV operator and various value-added service providers becomes one of the critical *** to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in ***,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key *** paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy *** addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key *** the main computation task is outsourced to the cloud,the burden of the user side is relatively *** of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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Classification of Rice Leaf using RCNN with Transfer Learning 3

Classification of Rice Leaf using RCNN with Transfer Learnin...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Iyswaryalakshmi, K. Ramkumar, M.O. Priyanka, S. Jayakumar, D. Ifet College of Engineering Computer Science of Engineering Villupuram India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Bangladesh, as one of the 10 leading rice suppliers and users in the globe, heavily relies on grain to power its economic system and meet its food demand. Rice is one of the world's most common foods. However, rice production is impeded by a variety of crop illnesses. One of most prevalent paddy diseases is leaf disease. Recognizing leaf illnesses is time-consuming as well as difficult for farmers in remote areas due to the lack of expertise. Despite the presence of experts in some areas, illness detection is achieved by the recognition through human eye, which may result in incorrect diagnosis and requires a lot of effort. The use of an automated process can help to solve these problems and hence having an Artificial Intelligence (AI) system is mandatory. In this study, an AI approach to detect four prevalent rice leaf diseases such as, Blast, Sheath Blight, Tungro, and Brownspot is provided. The input was clear photos of affected rice leaves on a white backdrop. The datasets were getting trained on using a variety of Machine Learning methods after appropriate preprocessing. When applied to the test datasets, the proposed approach attained an accuracy of over 97% after 10-fold cross validation. Moreover, to preserve the rice plants' healthy and suitable growth, it is vital to detect sickness and administer the needed therapy to the injured plants. Therefore, pesticides and/or fertilizers have been recommended based on the severity of the illness detected. © 2022 IEEE.

关键词： Image classification

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Preemptive Min Max Optimal Cost Based Scheduling for Improving the Load Balancing in Virtualized Cloud Environment

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SN computer science 2024年第6期5卷 754页

作者： Ravikumar, K. Saravanakumar, K. Viswanathan, Anand Department of Computer Science and Engineering RRASE College of Engineering Department of Computer Science and Engineering Sri Eshwar College of Engineering Department of Information Technology Ponjesly College of Engineering

Load balancing and scheduling are essential components of cloud computing that aim to optimize resource allocation and utilization. In a cloud environment, multiple virtual machines and applications compete for shared resources, and efficient load balancing and scheduling mechanisms are crucial for ensuring optimal performance and resource utilization. The prior methodologies attains higher utilization to allocate task due to makspan problem leads more deadlock occurrence and request failures with higher energy consideration. Furthermore, the paper examines the challenges and considerations in load balancing and scheduling, such as dynamic workload variations, heterogeneity of resources, and QoS requirements. To handle this issues, to propose an Efficient Load Balancing Based Predictive Priority-based Preemptive Min max priority Load balancer (PMin-Max LB) to improve the service optimality in Decentralized cloud server. Initially the user request accessibility and task progress is evaluated through Service level Workload Impact score (SLWIS). Then introducing Workload Scaling Job vector estimation is carried by Particle Swarm Optimization (PSO). By evaluating the Makespan Instance Scaling Changeover Virtualization (MISCV) task weights are assigned to virtual machine by applying Preemptive Min max priority Load balancer (PMin-Max LB) to reduce the workload to improve the performance. The proposed system improves the impact of load balancing and scheduling on cloud performance, scalability, and cost-effectiveness. Finally proposed system attains the potential advancements in load balancing and scheduling techniques improves the computing approach in virtualization process to reduce the workload burned to optimize the heterogeneity and Quality of service. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing Load balancer Particle swarm optimization Priority scheduling Task allocation Virtual machine

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Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

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computer Systems science & engineering 2023年第3期44卷 2349-2360页

作者： I.Vallirathi S.Ebenezer Juliet Department of Computer Science and Engineering PET Engineering CollegeVallioor627117India Department of Computer Science and Engineering VV College of EngineeringThisayanvilai627657India

Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN energy constraints is considered to be a key *** wireless sensor networks(WSNs),wireless mobile chargers(MCs)conquer such issues mainly,energy *** proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network(WRSN),which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the *** this algorithm,each node gets sorted using the K-means technique,in which the data gets distributed into various *** mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor *** position of the anchor points is calculated based on the energy distribution using the base *** this case,the network will act as a spare MC,so that one of the two MCs will run out of energy before reaching the *** the current tours of the two MCs terminate,regression analysis for energy prediction initiates,enabling the updating of anchor points in the upcoming *** on thefindings of the regression-based energy prediction model,the recommended algorithm could effectively refill network energy.

关键词： WSNs MCs WRSN K-means algorithm shortest hamiltonian cycle regression analysis

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ED-Ged:Nighttime Image Semantic Segmentation Based on Enhanced Detail and Bidirectional Guidance

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computers, Materials & Continua 2024年第8期80卷 2443-2462页

作者： Xiaoli Yuan Jianxun Zhang Xuejie Wang Zhuhong Chu College of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China

Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small *** address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image *** adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different *** strengthens overlooked image details,extending the IAEN module’s *** the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation *** entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network *** lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image *** experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.

关键词： Night driving semantic segmentation nighttime image processing adverse illumination differentiable filters

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Hybrid IT? Algorithm for Large-Scale Colored Traveling Salesman Problem

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Chinese Journal of Electronics 2024年第6期33卷 1337-1345页

作者： Xueshi DONG College of Computer Science and Technology Qingdao University Beijing Key Laboratory of Urban Spatial Information Engineering School of Computer Science Wuhan University

In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid IT?(HIT?) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard IT?. In the iteration process of HIT?, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HIT? is superior to the known comparison algorithms in terms of the quality solution.

关键词： Transportation Traveling salesman problems Multitasking Encoding Standards Biological cells Convergence

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