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SF-CNN: Deep Text Classification and Retrieval for Text Documents

Intelligent Automation & Soft Computing 2023年第2期35卷 1799-1813页

作者： R.Sarasu K.K.Thyagharajan N.R.Shanker Computer Science and Engineering Dhanalaksmi College of EngineeringAnna UniversityChennaiIndia R.M.D Engineering College Anna UniversityChennaiIndia Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringAnna UniversityChennaiIndia

Researchers and scientists need rapid access to text documents such as research papers,source code and *** research documents are available on the Internet and need more time to retrieve exact documents based on *** efficient classification algorithm for retrieving documents based on keyword words is *** traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in *** solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text *** proposed SF-CNN is based on deep semantic-based bag-of-word representation for document *** deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for *** experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN ***-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

关键词： Semantic classiﬁcation convolution neural networks semantic enhancement

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Multi-objective cloud load-balancing with hybrid optimization

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International Journal of computers and Applications 2023年第10期45卷 611-625页

作者： Geeta, Koppula Kamakshi Prasad, V. Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Basar India Department of Computer Science and Engineering JNTUH College of Engineering Hyderabad India

In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center;CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing;CSP, Cloud service providers;CSSA, Chaotic Squirrel Search Algorithm;DA, Dragonfly Algorithm;ED, Euclidean Distance;EDA-GA, Estimation Of Distribution Algorithm And GA;FF, FireFly algorithm;GA, Genetic Algorithm;HHO, Harris Hawk Optimization;IaaS, Infrastructure-as-a-Service;MGWO, Modified Mean Grey Wolf Optimization Algorithm;MMHHO, Mantaray modified multi-objective Harris Hawk optimization;MRFO, Manta Ray Forging Optimization;PaaS, Platform-as-a-Service;PM, Physical Machine;PSO, Particle Swarm Optimization;SaaS, Software-as-a-Service;SAW, Sample additive weighting;SLA-LB, Service Level Agreement-Based Load Balancing;TBTS, Threshold-Bas

关键词： Electric power utilization

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Auto Encoders with Cellular Automata for Anomaly Detection 5th

Auto Encoders with Cellular Automata for Anomaly Detection

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Sree, Pokkuluri Kiran Prasad, M. N, SSSN Usha Devi Rao, P. B. V. Raja Murty, P.T.S. Satyamallesh, Asapu Varma, Ch Phaneendra Babu, Gurujukota Ramesh Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada India

ISBN: (纸本)9783031770777

Combining auto encoders and hybrid cellular automata provides a novel way to identify anomalies in structured data in the field of anomaly detection. Dimensionality reduction and extracting the features is one of the know expertise of Auto encoders. Auto encoders compress and rebuild data into a lower-dimensional space, emphasizing anomalies with more reconstruction errors for non-typical data. Simultaneously, hybrid cellular automata provide a layer of spatial dynamics modelling by controlling the evolution of data through rule-based interactions among neighboring units. The hybrid methodology has the advantages of both systems: the local interaction modelling of hybrid cellular automata and the pattern recognition powers of auto encoders. This novel method is obtaining an accuracy of 99.83% which is promising, when comparing with the baseline methods. The integration of hybrid cellular automata instead of linear cellular automata has improved the overall accuracy and performance of the proposed classifier. We have used various parameters like F1 Score, precision, recall and ROC for measuring the effectiveness of the proposed system. The accuracy reported for anomaly detection in financial tractions is 99.63 and video surveillance is 99.96. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Dimensionality reduction

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Content Moderation of Generative AI Prompts

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SN computer science 2025年第4期6卷 1-28页

作者： Pardhi, Praful Computer Science and Engineering Department Shri Ramdeobaba College of Engineering and Management Nagpur India

Recent advances in Generative AI, have encouraged many industries to use generative models in their products for generating components such as images, text, audio, or video. Generative foundation models like Large Language Models and Diffusion Models are trained on large amounts of data scraped from the internet. Analysis with respect to the quality and content of this extensive data is not possible before training, which leads to generating inappropriate content that may contain profanity, violence, NSFW content, hate speech, and substance use. We cannot rely on the content the generative AI models may generate when the user prompts to generate the content. To generate fair and safe content the input prompt should be analyzed before generating the content to avoid generating unsafe content. If the prompt is unsafe the filters should avoid such a prompt or replace the improper and unsuitable words with the proper and suitable words. Here, we provide a framework for analyzing the text prompts based on the content moderation taxonomy. We define the taxonomy for content moderation and explore different language models to classify the prompt as per the moderation taxonomy. The proposed moderation taxonomy can be used to analyze user’s behavior and misuse of any generative AI application. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Content moderation Generative model Language model Large language models Prompt

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Enhancing VANET communication using squid game optimization based energy aware clustering approach

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5389-5394页

作者： Rajakumar, R. Suresh, T. Sekar, K. Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Chadalawada Ramanamma Engineering College Tirupati India

Vehicular Ad-Hoc Networks (VANETs) are studied wireless networks that enable communication among vehicles and roadside infrastructure. The role a vital play in improving on-road safety, efficacy, and convenience by enabling real-time data interchange for controlling traffic, infotainment services, and collision avoidance. Energy efficiency in VANETs is vital because of the restricted power resources of vehicles. Methods like clustering, vehicles are categorized into groups to decrease communication overhead, and meta-heuristic approaches that optimize network performance by intelligent problem-solving approaches are deployed to exploit energy efficiency while preserving network reliability and responsiveness. These methodologies contribute to the effective implementation of VANETs, ensuring sustainable and dependable communication in dynamic vehicular environments. In this study, a new Squid Game Optimization based Energy Aware Clustering Approach (SGO-EACA) technique for VANET is introduced. The goal of the SGO-EACA technique is to optimally choose the cluster heads (CHs) and produce clusters in the VANET in such a way as to realize energy efficiency. In the SGO-EACA technique, the concept of typical Korean sport is used where the attackers try to achieve their goal, but players try to eliminate each other. Moreover, the SGO-EACA approach derives a fitness function (FF) containing multiple metrics such as Residual Energy (RE), Trust Level, Degree Difference, Total Energy consumption, Distance to Base Station (DBS), and Mobility. The simulation values exposed that the SGO-EACA approach surpassed earlier state-of-the-art approaches with respect to various aspects. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cluster head Energy efficiency Fitness function Squid game optimization VANET Vehicle to vehicle

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Nonlinear Teager-Kaiser Infomax Boost Clustering Algorithm for Brain Tumor Detection Technique

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computer Systems science & engineering 2023年第8期46卷 2589-2599页

作者： P.M.Siva Raja S.Brinthakumari K.Ramanan Computer Science and Engineering Amrita College of Engineering and TechnologyNagercoil629901India Information Technology K.C College of Engineering and Management Studies and ResearchThaneIndia Computer Science and Engineering NPR College of Engineering and TechnologyDindigul629902India

Brain tumor detection and division is a difficult tedious undertaking in clinical image *** it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great *** is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain *** have a difficult time sorting and classifying tumors from multiple *** tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are *** clinical characteristics are then retrieved and analyzed statistically to identify brain *** use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative *** dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture *** segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.

关键词： Brain tumor detection image segmentation nonlinear teager kaiser filtering

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Distribution,enrichment mechanism and risk assessment for fluoride in groundwater:a case study of Mihe-Weihe River Basin,China

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Frontiers of Environmental science & engineering 2023年第6期17卷 63-83页

作者： Xingyue Qu Peihe Zhai Longqing Shi Xingwei Qu Ahmer Bilal Jin Han Xiaoge Yu College of Earth Science and Engineering Shandong University of Science and TechnologyQingdao 266590China College of Safety and Environmental Engineering Shandong University of Science and TechnologyQingdao 266590China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China Department of Resource and Civil Engineering Shandong University of Science and TechnologyTai'an 271019China

Due to the unclear distribution characteristics and causes of fluoride in groundwater of Mihe-Weihe River Basin(China),there is a higher risk for the future development and utilization of ***,based on the systematic sampling and analysis,the distribution features and enrichment mechanism for fluoride in groundwater were studied by the graphic method,hydrogeochemical modeling,the proportionality factor between conventional ions and factor *** results show that the fluorine content in groundwater is generally on the high side,with a large area of medium-fluorine water(0.5–1.0 mg/L),and high-fluorine water is chiefly in the interfluvial lowlands and alluvial-marine plain,which mainly contains HCO_(3)·Cl-Na-and HCO_(3)^(-)Na-type *** vertical zonation characteristics of the fluorine content decrease with increasing depth to the water *** high flouride groundwater during the wet season is chiefly controlled by the weathering and dissolution of fluorine-containing minerals,as well as the influence of rock weathering,evaporation and *** weak alkaline environment that is rich in sodium and poor in calcium during the dry season is the main reason for the enrichment of ***,an integrated assessment model is established using rough set theory and an improved matter element extension model,and the level of groundwater pollution caused by fluoride in the Mihe-Weihe River Basin during the wet and dry seasons in the Shandong Peninsula is defined to show the necessity for local management measures to reduce the potential risks caused by groundwater quality.

关键词： Groundwater in the Mihe-Weihe River Basin Distribution characteristics of fluorine Factors influencing fluoride Enrichment mechanism of fluorine Hydrogeochemical modeling Pollution and risk assessment

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Generative Adversarial Networks (GANs) for Drug Discovery 5th

Generative Adversarial Networks (GANs) for Drug Discovery

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Pokkuluri, Kiran Sree Gurujukota, Ramesh Babu Chintalapati, Phaneendra Varma Rao, P. B. V. Raja Raju, P. J. R. Shalem Prasad, Maddula Devi, N. SSSN Usha Murthy, Poppoppula T. Satyanarayana Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada India

ISBN: (纸本)9783031770807

Generative Adversarial Networks (GANs) are used in drug discovery by offering a computational framework to generate novel molecular structures with preferred properties. This paper highlights GANs’ role in accelerating de novo drug design, enhancing virtual screening, and addressing data scarcity challenges. Evaluating chemical validity, diversity, and bioactivity prediction, GANs provide a versatile platform for generating molecules that align with pharmaceutical requirements. This paper emphasizes the ethical considerations, stability, and interpretability of GAN-generated structures, positioning GANs as a transformative force in reshaping and expediting drug discovery processes. The use of GANs in drug discovery holds promise for accelerating the identification of lead compounds, optimizing existing molecules, and exploring new avenues in pharmaceutical research. We have effectively tested our proposed model with various parameters to obtain the usability and efficiency. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Drug discovery

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An Improved Gannet Optimization Algorithm Based on Opposition-Based Schemes for Feature Selection Problems in High-Dimensional Datasets

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SN computer science 2024年第1期5卷 183页

作者： Avinash, N. Sinha, Sitesh Kumar Shivamurthaiah, M. Department of computer science and engineering Rabindranath tagore university Bhopal India Department of Computer Science and Engineering APS College of Engineering Bangalore India

In the ever-evolving landscape of optimization algorithms for healthcare datasets, this study introduces an innovative fusion of the gannet optimization algorithm (GOA) with advanced opposition-based learning (OBL) techniques, including quasi-OBL, generalized OBL, and partial OBL. The primary novelty of our approach lies in the hybridization of GOA with OBL schemes. While GOA itself draws inspiration from the hunting behavior of gannets, OBL techniques introduce a layer of opposition-based learning that mimics the dialectical nature of opposing forces. By integrating these two distinct paradigms, we create a symbiotic relationship between exploration and exploitation. This synergy empowers our optimization framework to dynamically adapt to the complex landscape of high-dimensional healthcare datasets, underlining its prowess in classification tasks, particularly when employed with support vector machine (SVM) and k-nearest neighbor (KNN) classifiers. Statistical validation further underscores the significance of our results. The p value analysis reveals that our GOBL–GOA approach stands as a statistically superior alternative, making substantial strides in feature selection and classification accuracy. Beyond statistics, our approach excels in practicality, effectively identifying and selecting relevant features to provide accurate insights from complex healthcare data, as demonstrated by precision, recall, and F1-score metrics. In conclusion, our study not only introduces a paradigm shift in feature selection and optimization but also advances the cause of precise disease detection and prognosis, holding immense potential to enhance healthcare outcomes globally. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature selection Gannet optimization algorithm High-dimensional dataset Opposition-based learning

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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