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1st International Conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Verma, Bhumika Yudheksha, G.K. Sanjana Reddy, P. Maheswari, R. Parvathi, R. Vignesh, U. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Manipal Academy of Higher Education India

ISBN: (纸本)9781643683607

Floods are one of the most common and well-known natural calamities that create havoc, causing damage to one's life and economy while adversely affecting the environment. This Research focuses on receiving emergency notifications whenever the situation is critical and also being able to predict the flood in time by automatically measuring the riverbed water level periodically. In this work, the objective is to measure the level using ultrasonic sensors and process the acquired data using ESP8266, where the information will be transferred to ThingSpeak IoT cloud which uses HTTP Protocol over Local Area Network thus enabling it to graphically monitor the world. It checks whether it is not exceeding the threshold limit and when it reaches beyond the limit the system should be able to alert individuals through Telegram and web cloud application, and when the system predicts a flood in the following timeline. Here the Servo Motor acts as a dam gate i.e., it opens when the flood is alerted and closes back once it reaches below the threshold value. For prediction, various machine-learning algorithms such as Random Forest, Support Vector Machine, KNN, Decision Tree, and Logistic Regression were implemented. Due to its high accuracy of about 95%, the logistic regression model was considered suitable for predicting the changes and detecting various possibilities. © 2023 The authors and IOS Press.

关键词： HTTP

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Monarch Butterfly Optimization based Multi-Convolution Neural Network for Lung Cancer Classification 2

Monarch Butterfly Optimization based Multi-Convolution Neura...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Uma Maheswari, S. Adnan, Myasar Mundher Lathakumari, K.R. Pradeep, V. Praveena, Hirald Dwaraka Karunya Institute of Technology and Sciences Data Science and Cyber Security Coimbatore India The Islamic University Department of Computer Science Najaf Iraq Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India Alva's Institute of Engineering and Technology Department of Information Science and Engineering Mangalore India School of Engineering Mohan Babu University Erstwhile Sree Vidya Nikethan Engineering College Department of Ece Andhra Pradesh India

ISBN: (纸本)9798350382952

The lung cancer generally presented a pulmonary nodule on images of diagnostic and correct estimation in malignant pulmonary nodules is difficult for diagnosis and protecting of lung cancer. However, the existing methods has less classification accuracy because of irrelevant features on classification of lung cancer. In this research, proposed a Monarch Butterfly Optimization based Multi-Convolution Neural Network (MBO based M-CNN) method for lung cancer classification. Dataset used for lung cancer classification is LIDC-IDRI dataset and it is pre-processed by Histogram Equalization (HE). Then, the features are extracted by Gray Level Co-occurrence Matrix (GLCM) and extracted features are selected by Monarch Butterfly Optimization (MBO) algorithm. Then, the lung cancer classification is performed by Multi-Convolution Neural Network (M-CNN). The performance of proposed algorithm is tested in terms of accuracy, precision, sensitivity and specificity. Developed technique obtained accuracy 99.71%, precision 95.39%, sensitivity 99.46%, f1-score 97.45% that is better than previous method like Adaptive Boosting Self-Normalized Multiview Convolution Neural Network (AdaBoost-SNMV-CNN). © 2024 IEEE.

关键词： Adaptive boosting

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Assessment of Usability and Perceived Presence in Virtual Environments for Visual Function Testing 4th

Assessment of Usability and Perceived Presence in Virtual En...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Kini, Vishwas G. Siddalingaswamy, P.C. Shonraj, B.G. Srinivasan, Krithica Ramesh, Venkatasubbu Nayak, Srikrishna S. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Optometry Manipal College of Health Professions Manipal Academy of Higher Education Karnataka Manipal India Department of Computer Science and Engineering PES University Karnataka Bangalore India

ISBN: (纸本)9789819769919

Eye-related disorders are increasing rapidly in recent times, and it is difficult for the patients to visit the clinic frequently for regular testing and reach out to the clinician in person especially during pandemic times. Hence, there is a need in developing a home-based visual function assessment test for the patients and motivate for frequent testing to know the progression of their visual disorder. Gamification would make visual function testing enjoyable for those who lack attention and motivation. This study outcome would enable to know the usability of the mobile and Virtual Reality (VR) Visual Acuity tests and the presence felt by the users in VR environments for enhancing motivation through engagement while testing. The study results in determining an optimal home-based portable gamified eye tests in VR that is usable and engaging. Results obtained show the user perceived presence and usability increases with the increase in the interaction and display fidelity of the virtual environment and device. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual environments

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A Quantitative Study on the FaceNet System 4th

A Quantitative Study on the FaceNet System

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Gopakumar, Rajesh Kotegar, Karunakar A. Vishal Anand, M. Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819942831

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Face recognition

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Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

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computer Systems science & engineering 2022年第2期40卷 581-592页

作者： K.Rajakumari M.Vinoth Kumar Garima Verma S.Balu Dilip Kumar Sharma Sudhakar Sengan Department of Computer Science and Engineering School of EngineeringAvinashlingam Institute for Home Science and Higher Education for WomenCoimbatore641043Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India School of Computing DIT UniversityDehradun248009UttarakhandIndia Department of Computer Science and Engineering Paavai Engineering CollegePachal637018Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226M.P.India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Cloud computing is an Information technology deployment model established on *** scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing ***,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud ***,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin *** recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and ***,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task *** the end,a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the ***,the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time,system throughput,and maximizing resource utilization.

关键词： Cloud Computing scheduling ant colony optimization fuzzy logic

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A Stable Diffusion Based Image Generation Method for Few-Shot Emerging Pest Detection in the Wild 25

A Stable Diffusion Based Image Generation Method for Few-Sho...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Wang, Xiaodong Wang, Rujing Wu, Shilian Ma, Xiao Du, Jianming Gao, Junjie Institute Of Intelligent Machines Hefei Institutes Of Physical Science Chinese Academy Of Sciences Intelligent Agriculture Engineering Laboratory Of Anhui Province Hefei230031 China Graduate School Of University Of Science And Technology Of China Science Island Branch Hefei230026 China University Of Science And Technology Of China Hefei230026 China School Of Computer Science And Engineering Nanjing University Of Science And Technology Nanjing210094 China Shanghai Advanced Research Institute Chinese Academy Of Sciences Shanghai201210 China

ISBN: (纸本)9798350340266

Detecting few-shot emerging pests in the wild presents a significant challenge in the field of agricultural computer vision. However, there has been minimal research focused on generating emerging pest images with different natural scenarios that align with the authentic pest living environments. With the advent of the controllable generative stable diffusion model recent years, we proposed an innovative image generation method based on stable diffusion techniques as well as introducing text prompt and coordinate information into the vanilla model. Both qualitative and quantitative experimental results demonstrate that our proposed approach can generate anthropomorphic images which are both visually recognized by human experts and various image quality assessment performance metrics. © 2024 IEEE.

关键词： Diffusion

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Negative Itemset Tree for Discovering Rare Patterns with Periodicity from Static Databases

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engineering Letters 2023年第3期31卷 882-895页

作者： Jyothi Upadhya, K. Bolisetty, Thanishka Rao, B. Dinesh Geetha, M. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Manipal School of Information Sciences Manipal Academy of Higher Education Karnataka Manipal576104 India

Periodic patterns assist in the discovery of crucial information in the fields, including fraud detection, telecommu-nications, retail marketing, research, and medicine. Numerous methods have been developed for the extraction of periodic frequent patterns. The capacity to find uncommon or unexpected combinations that are missed by periodic frequent pattern mining techniques is the main motivation for Periodic Rare Pattern Mining. If the rare patterns are distributed across the transaction dataset, they are periodic and important. A novel PRPNegTidTreeMiner algorithm is proposed to discover rare patterns with periodicity along with support threshold. An efficient tree structure, NegTidTree, is constructed to capture the complete negative representation of the given static database. NegTidTree, serves the dual purpose of finding support count as well as periodicity information. This tree accelerates the extraction of periodic rare patterns and prevents repeated database scanning. PRPNegTidTreeMiner uses two mining techniques to generate periodic rare patterns. The first mining method, NegTidTreeMiner, employs a top-down approach to find rare patterns with periodicity thresholds which avoids traversal of frequent patterns. In the second technique, NegTidTreeMiner-FLP, the mining efficiency is improvised by avoiding the traver-sal of frequent as well as non-existing patterns. Experiments are carried out by varying support and periodicity components for a variety of datasets. The results show that NegTidTreeMiner performs better than NegTidTreeMinerFLP when the dataset is small and generates a huge number of periodic rare patterns. When the size of the database grows, NegTidTreeMinerFLP continuously outperforms NegTidTreeMiner. © 2023, International Association of Engineers. All rights reserved.

关键词： Database systems

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RAONet: CNNs with Residuals and Adaptive Occlusion for Optical Flow Estimation 5

RAONet: CNNs with Residuals and Adaptive Occlusion for Optic...

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5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Shi, Yuehong Zhao, Yanan Zhou, Wenjiang Zhang, Rufei Guan, Hongli Xu, Haitao University of Science and Technology TravelSky Mobile Technology Limited Beijing China China Academy of Information and Communications Technology Beiing China University of Science and Technology Beijing Beijing China TravelSky Mobile Technology Limited Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350355253

PWC-Net is a convolutional neural network for optical flow estimation that is superior in both inference speed and accuracy. However, the detection accuracy of this network for small moving objects is poor, and there is a problem of overlapping blur caused by occluded areas during the warping process. This paper proposes an improved PWC-Net network, this method can meet the real-time requirements, and the accuracy has been greatly improved compared with PWC-Net. Compared with PWC-Net, RAONet has a 22.1% lower error on Flying Chairs validation dataset, 12.3% lower error on MPI Sintel Clean dataset, and 12.3% lower error on MPI Sintel Final dataset and KITTI dataset by about 9%. Under the hardware environment of a 12- core CPU and RTX 2080 Ti GPU, RAONet can run at about 50fps on 1024 × 436 resolution images. And through the visual analysis of optical flow, it is concluded that RAONet can improve the detection accuracy of small moving objects, and can effectively solve the overlapping blur problem caused by the warping process. © 2024 IEEE.

关键词： Convolutional neural networks

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EDSA-Ensemble: An Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2025年第2期16卷 555-572页

作者： Petrescu, Alexandru Truică, Ciprian-Octavian Apostol, Elena-Simona Paschke, Adrian National University of Science and Technology Politehnica Bucharest Computer Science and Engineering Department Faculty of Automatic Control and Computers Bucharest060042 Romania Academy of Romanian Scientists 3 Ilfov Bucharest Romania Freie Universitaet Berlin Fraunhofer Institute for Open Communication Systems Berlin14195 Germany Leipzig04109 Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSA-Ensemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. © 2010-2012 IEEE.

关键词： Sentiment analysis

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Text Classification in Arabic Natural Language Processing: A Review 3

Text Classification in Arabic Natural Language Processing: A...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Hadheri, Hana Al-Khulaidi, Nesmah Al-Mahwiti, Sameera Yemen Academy for Graduate Studies Department of Information Technology Sana'a Yemen Yemen Academy for Graduate Studies Department of Computer Networks Sana'a Yemen College of Computer Science and Engineering Shandong University of Science and Technology Shandong Province China

ISBN: (纸本)9798350305333

Natural language processing holds immense importance across diverse fields, primarily centered around the English language. In contrast, efforts directed at processing Arabic have been comparatively limited. Therefore, our goal is to augment the existing efforts focused on advancing Arabic text processing. Generally speaking, text processing can vary according to specific demands. It encompasses tasks like text recognition, text analysis, or text classification. Among these, text classification has become a key focus in this domain and forms the core subject of this paper. Methods employed span AI to data mining algorithms, selected according to the text and the classification objective. The process consists of four primary stages: dataset selection, preprocessing and representation, text classification, and experimentation/evaluation. The objective of this paper is to compile the latest research that contributes to solving some of the challenges of Arabic text processing in a precise manner and highlight their contributions, findings, methodologies, and advancements in the Arabic Natural Language Processing ANLP field. In addition, we discuss and analyze these studies to find their limitations and make some suggestions. © 2023 IEEE.

关键词： Text processing

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