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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

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computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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Weed Classification Using Particle Swarm Optimization and Deep Learning Models

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computer Systems science & engineering 2023年第1期44卷 913-927页

作者： M.Manikandakumar P.Karthikeyan Department of Computer Science and Engineering Thiagarajar College of EngineeringTamil NaduIndia Department of Information Technology Thiagarajar College of EngineeringTamil NaduIndia

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and *** and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of *** address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image *** this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed *** Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed *** effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification *** proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed *** work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

关键词： Deep learning convolutional neural network weed classification transfer learning particle swarm optimization evolutionary computing Algorithm 1:Metrics Evaluation

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Performance analysis of precise energy consumption algorithm for smart home using hybrid renewable energy

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International Journal of Powertrains 2023年第1期12卷 1-14页

作者： Anguraj, Dinesh Kumar Sathya, C. Vinothini, C. Jayanthi, M. Rani, J. Vakula Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamil Nadu Coimbatore India Department of MCA CMR Institute of Technology Karnataka Bengaluru India

Energy demand is increased day by day, so there is a need for energy management, and it plays a vital part in the 21st century. At present, non-renewable energy is utilised most of the time. The people are not aware of the wastage of energy when their home appliances turned on the whole day. So, hybrid renewable energy is an alternate source for supplying continuous power for their smart home in the future. The collected renewable energy is utilised for the smart home usages. To minimise power consumption and to predict energy consumption precisely is a stimulating task in the future. This work proposes an innovative precise energy consumption algorithm (PECA) that is used to calculate the smart home power consumption accurately. PECA utilises smart plug, smart gateway, and mobile app administration platform to build and deploy a deep learning model. The smart plug and smart gateway combined into the complete distributed sensor network is to analyse, improve, and expand the energy consumption efficiently. Artificial intelligence is used to predict the energy usage data, optimise resource consumption, reduce the cost and maximise renewable energy usage. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Electric power utilization

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Deep Learning Enabled Social Media Recommendation Based on User Comments

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computer Systems science & engineering 2023年第2期44卷 1691-1702页

作者： K.Saraswathi V.Mohanraj Y.Suresh J.Senthilkumar Department of Computer Science and Engineering Government College of EngineeringSalem636011India Department of Information Technology Sona College of TechnologySalem636005India

Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classiﬁcation of this RS research is limited and needs to be *** previous method did notﬁnd any user reviews within a time,so it gets poor accuracy and doesn’tﬁlter the irre-levant comments efﬁ*** Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s *** it is efﬁcient to analyse the trust comment and remove the irrelevant sentence ***ﬁrst step is to collect the data based on the transactional reviews of social *** second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the *** the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the ***,In the classiﬁcation method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the *** simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

关键词： Recommendation systems(RS) social media recursive neural network-based trust recommender system(RNN-TRS) user reviews

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Enhancing User Experience in AI-Powered Human-computer Communication with Vocal Emotions Identification Using a Novel Deep Learning Method

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computers, Materials & Continua 2025年第2期82卷 2909-2929页

作者： Ahmed Alhussen Arshiya Sajid Ansari Mohammad Sajid Mohammadi Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.

关键词： Human-computer communication(HCC) vocal emotions live vocal artificial intelligence(AI) deep learning(DL) selfish herd optimization-tuned long/short K term memory(SHO-LSTM)

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Forensic Eye: Convolutional Neural Network-based Crime Scene Object Detection System 2

Forensic Eye: Convolutional Neural Network-based Crime Scene...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Sudha, I. Vadivel, M. Lakshmiramana, P. Kalpana, N. Kumar, Nellore Manoj Vinoth, R. Saveethe University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Tamilnadu Chennai India Excel Engineering College Department of Information Technology Komarapalayam Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh India Psna College of Engineering and Technology Department of Computer Science and Engineering India Saveetha School of Engineering Department of Mathematics Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal India

ISBN: (纸本)9798350364699

Forensic investigations rely heavily on the accurate detection and analysis of crime related objects present at crime scenes. Traditional methods of object detection often involve manual labor and are error prone process to human error. In recent years, Convolutional Neural Networks (CNNs) have shown remarkable success in various computer vision tasks, including in object detection. In this article, we introduce ForensicEye, a novel CNN-based crime scene object detection system designed to assist forensic investigators in identifying the objects related to the crime. It utilizes a state-of-the-art CNN architecture trained on a large dataset of crime scene images, enabling robust detection even in challenging lighting and environmental conditions. We evaluated the performance of the ForensicEye on benchmark datasets with various preprocessing techniques, demonstrating its superior accuracy and efficiency compared to traditional methods. Our proposed model achieved the accuracy of 96.2%, precision of 97.9%, Recall of 96.9%, F1-score of 97.4%. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimization and Representation of Non-Slicing VLSI Floorplanning 4

Optimization and Representation of Non-Slicing VLSI Floorpla...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Jeyarohini, R. Britto, K. R. Aravind Ramkumar, M.P. PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350300888

The recent advances in the semiconductor nano technologies increase the complexity of very large-scale integration circuits. With the fabrication technology entering the deep Nanoscale era more demand at greater huge complication of integrated chip design arises. The development of eccentric floor plan without over-lapping is the intention of VLSI floor planning. The major objective of VLSI is concerned with floor plan area minimization and wire length optimization. Floor planning is the important step that creates an optimal layout solution for the VLSI circuits. This article attempted to collect the necessary information in reducing the floor plan layout by means of reducing the wire length, white space area with the employment of computer aided metaheuristic algorithms. In order to ease the process of execution the best local search algorithms like a Particle Swarm Optimization and Simulated Annealing algorithms is compared. © 2023 IEEE.

关键词： Simulated annealing

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Classification Model for IDS Using Auto Cryptographic Denoising Technique

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computer Systems science & engineering 2023年第4期45卷 671-685页

作者： N.Karthikeyan P.Sivaprakash S.Karthik Department of Computer Science&Engineering PPG Institute of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization *** classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the *** are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting *** algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of *** this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam *** classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and *** neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 *** their power and limitations in the proposed methodology that could be used in future works in the IDS area.

关键词： Auto cryptographic denoising(ACD) classifier intrusion detection system(IDS) optimizer performance measures

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A Novel Reliable and Trust Objective Function for RPL-Based IoT Routing Protocol

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computers, Materials & Continua 2025年第2期82卷 3467-3497页

作者： Mariam A.Alotaibi Sami S.Alwakeel Aasem N.Alyahya Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The Internet of Things (IoT) integrates diverse devices into the Internet infrastructure, including sensors, meters, and wearable devices. Designing efficient IoT networks with these heterogeneous devices requires the selection of appropriate routing protocols, which is crucial for maintaining high Quality of Service (QoS). The Internet engineering Task Force’s Routing Over Low Power and Lossy Networks (IETF ROLL) working group developed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) to meet these needs. While the initial RPL standard focused on single-metric route selection, ongoing research explores enhancing RPL by incorporating multiple routing metrics and developing new Objective Functions (OFs). This paper introduces a novel Objective Function (OF), the Reliable and Secure Objective Function (RSOF), designed to enhance the reliability and trustworthiness of parent selection at both the node and link levels within IoT and RPL routing protocols. The RSOF employs an adaptive parent node selection mechanism that incorporates multiple metrics, including Residual Energy (RE), Expected Transmission Count (ETX), Extended RPL Node Trustworthiness (ERNT), and a novel metric that measures node failure rate (NFR). In this mechanism, nodes with a high NFR are excluded from the parent selection process to improve network reliability and stability. The proposed RSOF was evaluated using random and grid topologies in the Cooja Simulator, with tests conducted across small, medium, and large-scale networks to examine the impact of varying node densities. The simulation results indicate a significant improvement in network performance, particularly in terms of average latency, packet acknowledgment ratio (PAR), packet delivery ratio (PDR), and Control Message Overhead (CMO), compared to the standard Minimum Rank with Hysteresis Objective Function (MRHOF).

关键词： IoT LLNs RPL objective function OF MRHOF OF0 routing metrics reliability trustworthiness

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