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An Algorithm to Reduce Compression Ratio in Multimedia Applications

computers, Materials & Continua 2023年第1期74卷 539-557页

作者： Dur-e-Jabeen Tahmina Khan Rumaisa Iftikhar Ali Akbar Siddique Samiya Asghar Department of Electronic Engineering Sir Syed University of Engineering&TechnologyKarachiPakistan Department of Information&Computer Science Sir Syed University of Engineering&TechnologyKarachiPakistan

In recent years,it has been evident that internet is the most effective means of transmitting information in the form of documents,photographs,or videos around the *** purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual *** transmission,this massive data necessitates a lot of channel *** order to overcome this problem,an effective visual compression approach is required to resize this large amount of *** work is based on lossy image compression and is offered for static color *** quantization procedure determines the compressed data quality *** images are converted from RGB to International Commission on Illumination CIE La^(∗)b^(∗);and YCbCr color spaces before being *** the transform domain,the color planes are encoded using the proposed quantization *** improve the efficiency and quality of the compressed image,the standard quantization matrix is updated with the respective image *** used seven discrete orthogonal transforms,including five variations of the Complex Hadamard Transform,Discrete Fourier Transform and Discrete Cosine Transform,as well as thresholding,quantization,de-quantization and inverse discrete orthogonal transforms with CIE La^(∗)b^(∗);and YCbCr to RGB *** to signal noise ratio,signal to noise ratio,picture similarity index and compression ratio are all used to assess the quality of compressed *** the relevant transforms,the image size and bits per pixel are also *** the(n,n)block of transform,adaptive scanning is used to acquire the best feasible compression *** of these characteristics,multimedia systems and services have a wide range of possible applications.

关键词： Color image compression color spaces discrete orthogonal transforms(DOTs) peak-to-signal noise ratio(PSNR) similarity index

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Wi-SFDAGR: WiFi-Based Cross-Domain Gesture Recognition via Source-Free Domain Adaptation

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IEEE Internet of Things Journal 2025年第13期12卷 24159-24173页

作者： Yan, Huan Zhang, Xiang Huang, Jinyang Feng, Yuanhao Li, Meng Wang, Anzhi Ou, Weihua Wang, Hongbing Liu, Zhi Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

WiFi Channel State information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, existing WiFi-based gesture recognition solutions exhibit poor generalization ability when deployed in new environment, orientation, or location. Although some methods combine labeled source domain and unlabeled target domain to learn domain-independent features, factors such as data privacy protection hinder access to source data during practical environment adaptation. Consequently, we consider realistic scenario where source data is unavailable during adaptation of unlabeled test data, and instead, a trained source domain model is used. In this paper, we propose Wi-SFDAGR, a WiFi-based Source-free Domain Adaptation Gesture Recognition framework. Specifically, we treat cross-domain as an unsupervised clustering problem, aiming to ensure that features within local neighborhoods exhibit similar prediction results while those farther apart display different prediction outcomes in the feature space. We theoretically analyze the effect of enhanced prediction consistency between neighbor points extracted from gestures on generalization error. Furthermore, we employ an attraction-dispersion network to strengthen prediction consistency among closely located features in the feature space while reducing it for distantly located features. To mitigate noise introduced during nearest neighbor sample selection in the feature space (where predictions may not align with the input sample’s prediction), we progressively improve nearby sample feature aggregation by estimating uncertainty to reweight local neighborhood predictions. Finally, extensive experiments are conducted on the Widar 3.0 and XRF55 datasets and the results show our proposed framework outperforms most cross-domain methods. © 2014 IEEE.

关键词： Differential privacy

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Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations

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International Journal of Fuzzy Systems 2024年第8期26卷 2536-2552页

作者： Haobijam, Basanta Huang, Yo-Ping Chang, Yue-Shan Chang, Tsun-Wei Department of Electrical Engineering National Taipei University New Taipei City23741 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science and Information Engineering HungKuo Delin University of Technology New Taipei City236302 Taiwan

Pisciculture encounters an array of intricate challenges that span disease management, preservation of water quality, prevention of genetic hybridization, ensuring the integrity of net systems, sourcing sustainable aquatic feed, and comprehending fish growth and reproductive dynamics. Addressing these multifaceted challenges necessitates a comprehensive research approach. This study employs an innovative synergy of fuzzy logic and deep learning techniques, resulting in a robust strategy to tackle these obstacles effectively. Fuzzy logic excels in assessing stressed fish conditions by handling inherent uncertainties. Simultaneously, YOLOv7 with fuzzy color enhancement (YOLOv7FCE) is used to detect damaged fish nets, thereby mitigating losses and upholding the integrity of the net infrastructure. This approach also leverages YOLOv7FCE for identifying Cobia fish within shoals, streamlining the identification process. Subsequently, DeepLabv3 is implemented to meticulously segment the recognized Cobia fish, facilitating precise measurements of their physical attributes. This comprehensive methodology yields profound insights into growth patterns and feeding tendencies within the confined aquatic environment. By embracing this approach, the research presents a versatile and adaptive framework that not only enhances our comprehension of piscine dynamics but also holds the potential to revolutionize the aquaculture industry. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fish

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Network Anomaly Detection using Autoencoder on Various Datasets: A Comprehensive Review

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Recent Patents on engineering 2024年第9期18卷 63-77页

作者： Singh, Richa Srivastava, Nidhi Kumar, Ashwani Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

The scientific community is currently very concerned about information and communication technology security because any assault or network anomaly can have a remarkable collision on a number of areas, including national security, the storage of private data, social welfare, economic concerns, and more. As a result, many strategies and approaches for this goal have been developed over time, making the anomaly detection domain a large research subject. The primary concern of this patent study is to review the most crucial elements relating to anomaly detection, including an overview of background analysis and a core study on the most important approaches, procedures, and systems in the field. To make the structure of this survey easier to understand, the domain of anomaly detection was examined along with five dimensions: Detection methods in network traffic, objectives of the patent paper, various datasets used, accuracy, and open issues/gaps. The gap which has been identified after the survey can be extended as a future scope might be helpful for the researcher. © 2024 Bentham science Publishers.

关键词： Anomaly detection

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Personalized E-Shopping Recommendations for Sustainable Consumption 16

Personalized E-Shopping Recommendations for Sustainable Cons...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Lo, Chin-Hsuan Lin, Lien-Fa National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350377903

To address the prevalent issue of impulsive buying behavior in the e-commerce environment, this study introduces a personalized online shopping recommendation system aimed at the short-term improvement of shopping impulses and the long-term reduction of unnecessary expenditures and waste, thereby facilitating sustainable consumption. The system builds a personalized model based on consumers' past purchase records, their preferences, and buying habits as demonstrated in interactions with chatbots. Furthermore, by integrating personality trait theories, the system conducts in-depth analyses of consumers to offer more precise product or service recommendations, thereby aiding them in making informed shopping decisions. Through continuous updates from interactions with the chatbot, the system promptly reflects the latest consumer preferences, thereby providing retailers with strategies to enhance customer experience and loyalty. © 2024 IEEE.

关键词： Marketplaces

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Efficient Data Storage: Leveraging Blockchain and IPFS Techniques for Cost Minimization 10

Efficient Data Storage: Leveraging Blockchain and IPFS Techn...

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10th IEEE Smart World Congress, SWC 2024

作者： Chou, Shih-Fan Qiu, Che-Yu National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331520861

The rapid growth of diverse information technology applications has increased the challenge of securing and efficiently managing large volumes of data. Innovative solutions like blockchain technology and distributed file systems such as InterPlanetary File System (IPFS) are emerging to address data heterogeneity, security and privacy from various applications. The fundamental differences between blockchain and IPFS, coupled with data characteristics, will influence data storage and management. Despite the blockchain's benefits of decentralization, immutability, and transparency in preserving crucial data, uploading to the chain involves significant time and financial costs, especially for large and frequently modified data. In comparison to blockchain, IPFS provides a cost-effective decentralized storage scheme. Towards this, we delve into developing a cost-effective data storage system, taking into account factors such as network bandwidth, IPFS storage usage, and blockchain busyness to design a data storage decision-making mechanism that integrates blockchain and IPFS to meet the specific data characteristics. Hence, the most appropriate data storage and management method can be tailored to various application scenarios. We then conduct a series of experiments to evaluate the performance of our proposed approach. Experimental results show the effectiveness of our approaches in terms of storage cost and scalability. © 2024 IEEE.

关键词： Storage management

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Tweeting the Market: Leveraging LSTM for Stock Price Prediction 2

Tweeting the Market: Leveraging LSTM for Stock Price Predict...

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2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Modi, Vinni Agrawal, Nandini Sardana, Neetu Jain, Ishika Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology U.P Noida India

ISBN: (纸本)9798331541217

Investors and traders constantly seek an edge, a source of insight that can help them predict market movements more accurately. Over the past several years, the emergence of social media and the increase in use of platforms such as Twitter, Facebook, etc. has given birth to a new dimension of market analysis - sentiment analysis and its potential effect on stock prices. The mood dimensions that correlate with market movements can be complex and multifaceted. Only a few researches have studied the effect of social media on stock price predictions. Prior investigations have examined discussions surrounding the COVID-19 virus, its societal repercussions, and its potential sway over financial markets. This research has specifically scrutinized the effect of negative sentiments and their potential repercussions on stock prices of major tech companies like Microsoft, Google, and Tesla. The paper aims to identify the most effective model among ARIMA and LSTM for predicting stock prices by correlating them with sentiments expressed in social media posts. The motive of this paper is to help investors better analyze the market and adjust their strategies accordingly to earn more profits. © 2024 IEEE.

关键词： Tweets

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Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

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Complex System Modeling and Simulation 2025年第1期5卷 68-85页

作者： Xuewu Wang Jianing Zhang Yi Hua Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and *** paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion *** address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is *** NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution *** dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two *** addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is *** local search operators based on ideal point are proposed to find a better local *** improve the global exploration ability of the algorithm,a dual population restart mechanism is *** tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.

关键词： limited automatic guided vehicle(AGV)scheduling problem nondominated sorting genetic algorithm II(NSGA-II) dual population co-evolution matrix manufacturing workshop

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Enhancing Text Classification Performance using Stacking Ensemble Method with TF-IDF Feature Extraction 5

Enhancing Text Classification Performance using Stacking Ens...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Parmar, Mahesh Tiwari, Akhilesh Madhav Institute of Technology & Science Computer Science and Engineering Gwalior India Madhav Institute of Technology & Science Information Technology Gwalior India

ISBN: (纸本)9798350395235

This study explores various techniques for extracting features from unstructured textual data and evaluates their effectiveness in text classification. Categorizing text into appropriate categories is a crucial task in natural language processing (NLP) known as text classification. The most commonly used feature extraction methods for this task are TF-IDF and NLP techniques. For this research, a dataset of Amazon customer reviews extracted from Amazon's website is used. The dataset contains 3071 reviews, along with star ratings, review dates, variants, and feedback for various Amazon Alexa products. To enhance classification accuracy, the study proposes a stacking ensemble method that combines the predictions of Random Forest Classifier and Gradient Boosting Classifier models with TF-IDF feature weighting. In addition, the experiment compared the proposed method's effectiveness with other methods by evaluating various feature extraction techniques, such as NGrams, Bag-of-Words (BoW), TF-IDF, Word2vec, and Glove, along with several classifiers, including Logistic Regression (LR), Decision Tree (DT), Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), k-nearest neighbors (KNN), and Random Forest (RF). According to the results of the experiment, the Stacking Ensemble (RF+GBM-Stacking) approach performed better than other methods. The proposed technique demonstrated promising results with an accuracy, precision, recall, and F1 score of 0.85, 0.82, 0.87, and 0.84, respectively. These findings suggest that the technique has the capability to improve text classification performance significantly. © 2024 IEEE.

关键词： Decision trees

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Semantic segmentation of brain tumor images using attention-based residual light u-net model

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Multimedia Tools and Applications 2025年第10期84卷 7425-7441页

作者： Chakrapani Kumar, Sunil Department of Information Technology ABV-IIITM MP Gwalior474015 India Department of Computer Science and Engineering ABV-IIITM MP Gwalior474015 India

Accurate segmentation of brain tumor regions in MRI images is essential for monitoring tumor growth. In view of this, several automated brain tumor segmentation models are proposed. U-Net is one of the most popular models widely used for image segmentation. Based on the U-Net architecture, complex models are proposed for brain tumor segmentation. However, existing models mainly focus on increasing the depth of the model for better accuracy without much bothering the transmission of spatial information. Attention-based U-Net model can help to extract salient local-level features that can be passed to the decoder part of the network. Considering the above issues, we propose a variant of the U-Net model called the Attention-based Residual Light U-Net model. The proposed model is effective in the sense that it has comparatively less model complexity, a better attention mechanism, and a combined loss function that can handle class imbalance problems. Our experiments on one of the latest BraTS-2021 datasets show that the proposed method can achieve a mean Intersection over Union of 0.8874, 0.8863, 0.8905, and dice score of 0.9415, 0.9387, 0.9115 on the whole tumor, tumor core, and enhancing tumor, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Tumors

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