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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Nishok, V.G. Bharathi, N. Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9789819786657

Twitter is the best source for sentiment analysis, product reviews, current issues, etc. Sentiment analysis extracts positive and negative opinions from the Twitter data set, and R studio provides the best environment for this Twitter sentiment analysis. Find if Trump’s phone Android is fake or not by comparing his iPhone and Android tweets using R with datasets. Detailed analysis is not there. The Twitter data collected might not be representative of the entire population. By using filtering of tweets as we need Android and iPhone tweets here and usage of tweets per day, no of tweets with and without quotes and pictures and links, taking common words from Android and iPhone and trying with log ratio that is Android /iPhone and with log ratio and common word find sentiment. We got good results by the usage of Twitter of each device, no of tweets quoted and non-quoted, no picture/link and picture/link, most common words, words compare with Android and iPhone and log ratio, finding sentiment of it which type of word it is. My work helps his account is a trump or not for Android as it contains negative tweets by common words, Android and iPhone words and log odd ratio with that it detects sentiment as it is a positive, negative or neutral by showing anger, sad, happy, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Potato Leaf Disease Classification using Pre Trained Deep Learning Techniques -A Comparative Analysis 5

Potato Leaf Disease Classification using Pre Trained Deep Le...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sangar, G. Rajasekar, V. Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9798350361155

Potatoes are essential for food production and consumption, but pests and illnesses can cause major economic losses. To quickly identify potato leaf diseases, image processing, computer vision, and deep learning can be used. We propose the "Potato Leaf Disease" dataset with 4072 photos of varied circumstances to address current dataset limitations. Our comparison investigation used DenseNet121, ResNet50, VGG19, EfficientNetB0, InceptionV3, and MobileNetV2 for feature extraction and SVM, KNN, and Random Forest for classification. The most accurate model was EfficientNetB0 with SVM Linear Kernel at 94.81%. Model efficiency in varied environmental situations shows dataset realism. Training took 38.0030 seconds and testing 1.3503 seconds. This study highlights the need of realistic datasets and presents a reliable potato leaf disease classification method. The dataset and model offer new opportunities for agricultural sustainability and crop security, making them useful for the potato business. © 2024 IEEE.

关键词： Deep learning

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A Way to Mark Attentance using Face Recognition using PL 3

A Way to Mark Attentance using Face Recognition using PL

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Vivek, V. Tr, Mahesh Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350399264

The computer system that is used to take attendance online is going to be upgraded as part of this project. This attendance tracking system is able to hold the technology known as facial recognition, which is a useful feature. This device helps students take attendance by utilizing both a biometric technology and artificial intelligence algorithms in one convenient package. When pupils enter the classroom, the face recognition software can identify them, making it useful for educational institutions of any kind. The existing system has a number of flaws that make it difficult to accurately record the attendance of both students and teachers. The approach makes it simpler to mark attendance and lowers the number of mistakes that might occur when doing *** students and teachers will gain a significant amount by utilizing these techniques. The ability to recognize people based on their faces is becoming increasingly significant in practically every industry. Face recognition techniques will be used to understand the faces. An attendance record is compared to the record that corresponds to it that was previously saved in the database. A student's photograph is used as a check point for this comparison. © 2023 IEEE.

关键词： Face recognition

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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A Review of Wearable Devices for Affective Computing 2

A Review of Wearable Devices for Affective Computing

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： MacHhi, Vilas S. Shah, Apurva M. The Maharaja Sayajirao University of Baroda Faculty of Technology & Engineering Computer Science & Engineering Department Vadodara India

ISBN: (纸本)9798350370188

Wearable technology is expanding rapidly in recent year. It is used in many applications in various domains, including affective computing. Affective computing is all about understanding and responding to human emotions. This paper will give an overview of a wearable device that was developed specifically for affective computing. It aims to increase the way we can understand user's emotions and allow for more personalized experiences based on their context. Multiple sensors and algorithms work together in this device to capture, analyze, and interpret our feelings through physical or behavioral signals. It can be worn as a smart-watch, wristband, or integrated into clothing or accessories. The device communicates wirelessly with a companion application installed on a smart-phone or a connected device, providing real-time feedback and insights about the user's emotional state. © 2024 IEEE.

关键词： Human computer interaction

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Energy Efficient Green Routing for UAVs Ad-Hoc Network

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Intelligent Automation & Soft Computing 2023年第7期37卷 1111-1127页

作者： M.Muthukumar Rajasekar Rangasamy Irshad Hussain Salman A.AlQahtani Department of Computer Science and Engineering Sree Sowdambika College of EngineeringTamil NaduIndia Department of Computer Science and Engineering-AI&ML School of EngineeringMalla Reddy UniversityHyderabadIndia Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar25000PeshawarPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.

关键词： Connected dominatingset hybridrouting UAVAdhocnetworks link state stability

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Enhanced Small Lesion Detection on Grape Leaves Using Optimized YOLOv7 3

Enhanced Small Lesion Detection on Grape Leaves Using Optimi...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mahalakshmi, S. Gandhi, Jay Prabakaran, V. Rastogi, Shailesh Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur Chennai India Faculty of Engineering and Technology Parul University Department of Computer science and Engineering Vadodara India Pune India Department of Finance Pune India

ISBN: (纸本)9798331508456

The detection of small lesions on grape leaves plays a pivotal role in the early identification and management of plant diseases, potentially reducing significant agricultural losses. Traditional detection systems, including advanced versions such as YOLOv7, often overlook small target diseases due to resolution constraints and inadequate feature extraction capabilities. Addressing this, our research enhances the YOLOv7 architecture by incorporating a specialized prediction branch and leveraging an improved channel attention mechanism in conjunction with an Extended-Efficient Long-range Attention Network (E-ELAN). Firstly, a new detection head specifically designed for small targets enhances the resolution capabilities of the system. Secondly, the integration of asymmetric convolution into the E-ELAN facilitates refined multi-scale feature extraction, critical for distinguishing minute pathological changes. Further, we enhance the model's sensitivity to small-scale features through a revamped channel attention mechanism. Moreover, we replace the conventional Complete Intersection over Union (CIoU) with a Structured Intersection over Union (SIoU) in the loss function, significantly improving the model's localization accuracy. Experimental results from testing the optimized YOLOv7 on a dataset of grape leaves affected by three prevalent diseases show an average accuracy improvement of 2.7%, achieving a remarkable 93.5%. This substantiates our model's efficacy in enhancing small lesion detection, thereby contributing effectively to early disease intervention strategies in viticulture. © 2025 IEEE.

关键词： asymmetric convolution channel attention mechanism plant disease diagnostics small lesion detection YOLOv7

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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A diagnosis of ovarian cyst using deep learning neural network with XGBoost algorithm

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International Journal of Information Technology (Singapore) 2023年第7期15卷 3499-3506页

作者： Suganya, Y. Ganesan, Sumathi Valarmathi, P. Suresh, T. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Chidambaram India Department of Computer Science and Engineering Mookambigai College of Engineering Tamil Nadu Pudukkottai India

Ovarian cysts are one of the most common gynecologic disorders encountered in clinical practice. The pelvic computed tomography (CT) scan is a commonly employed examination method used to detect ovarian cyst, which shows the benefits in safety, efficiency, and providing high-resolution images. Deep learning models have shown promising results in accurately detecting ovarian cysts and differentiating them from other ovarian masses. The general approach to ovarian cyst detection using deep learning involves training a convolutional neural network (CNN) on a large dataset of ovarian ultrasound or MRI images. With this motivation, this study focuses on the design of ovarian cyst detection and classification model using deep learning neural network (DLNN) model. A dataset of ovarian cysts have been gathered from hospitals, physicians, and the internet in order to accomplish these goals. Initially, the pre-processing of images take place to improve the quality of the image. The features like Hu moments, Haralick feature and color histogram are used to evaluate the performance. For feature extraction, the proposed DLNN technique uses Inception model to score different types of masses. Finally, extreme gradient boosting (XGBoost) classifier is used for ovarian cyst detection process. A detailed experimental result analysis take place to investigate the performance of the DLNN model. The experimental findings suggested that the DLNN model with XGBoost classifier accomplishes maximum detection accuracy of 98%. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Benign Deep learning Neural network Ovarian cyst XB algorithm

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