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Online Information-Based Product Evolution Course Mining and Prediction

International Journal of Information Technology and Decision Making 2024年第2期23卷 599-627页

作者： Chen, Yuh-Jen Chen, Yuh-Min Department of Accounting and Information Systems College of Business Intelligence National Kaohsiung University of Science and Technology Kaohsiung City Taiwan Institute of Manufacturing Information and Systems College of Electrical Engineering and Computer Science National Cheng Kung University Tainan City Taiwan

In today’s fiercely competitive business environment, product design and development play critical roles in an enterprise’s success;therefore, consumer demand must be understood. Enterprises used to understand their consumers’ demands through time-consuming questionnaire surveys and statistical analyses. As the Internet and the popularity of virtual communities have grown, more consumers are leaving comments about their perceptions of the appeal of products on online social media platforms, thus enabling enterprises to more objectively understand consumers’ product preferences and demands. Therefore, determining how to effectively assist enterprises in analyzing valuable information beneficial to product design and development that can be gleaned from the large amount of social media data available is critical to promoting an enterprise’s competitive advantage in the product market. However, previous studies have primarily focused on understanding consumer viewpoints of products through review articles or electronic word-of-mouth (eWOM;it is a common channel for spreading product appraisals) from online social media. For these review articles and eWOM that can imply consumer demand for product features, there were no relevant studies focused on analyzing consumer demand by using both online review articles and eWOM for product feature evolution. Therefore, for new product features, this study developed a mechanism for product evolution course mining from product-related review articles (such as product functions or specifications) and eWOM on online social media to realize the prediction of future product features or specifications, and then assist enterprises in rapidly and accurately grasping product development trends to effectively discern key reference information for product design and development. The study was achieved by (1) designing a process for online information-based product evolution course mining and prediction, (ii) developing techniques related to

关键词： Forecasting

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Construct and Dynamic Analyzing of a Novel Eight-Dimensional Hyperchaotic System

Construct and Dynamic Analyzing of a Novel Eight-Dimensional...

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2024 International Conference on Advanced Information Scientific Development, ICAISD 2024

作者： Shibeeb, Ahmed Kareem Albermany, Salah Mehdi, Sadiq A. Middle Technical University Technical Institute - Suwaira Department of Computer Systems Baghdad Iraq University of Kufa College of Computer Science and Math Department of Computer Science Najaf Iraq Mustansiriyah University College of Education Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350392036

Chaos is often present in nonlinear systems, but not universally. This has led to extensive research focusing on identifying systems that can display chaos under certain parameters or initial conditions. Certain systems are notable for their complexity, owing to their high dimensionality and straight-forward nonlinear terms or parameter values. In this paper, we present and analyze a novel eight-dimensional hyperchaotic system constructed using nonlinear equations. The dynamical analysis results demonstrate that the novel hyperchaotic system has three large positive Lyapunov exponents. μ1 = 22.072, μ2 = 18.232, and μ3 = 3.698, indicating a strong Perron effect. The Kolmogorov entropy of the system is 44.002, its Fractal Dimension is 6.584, and it has two unstable equilibrium points. Additionally, the 0-1 test results for all variables were close to 1, confirming the chaotic nature of the system. We also established the dissipation and sensitivity of the system to small changes in the initial conditions. MATHEMATICA simulations were employed to generate 2D and 3D phase and bifurcation diagrams, respectively. The analysis results show that the novel eight-dimensional hyperchaotic system outperforms many recently developed systems, including those with up to ten dimensions. © 2024 IEEE.

关键词： Lyapunov functions

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An AI-driven Approach for Tailoring Java Programming Instructions 3

An AI-driven Approach for Tailoring Java Programming Instruc...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 IEEE.

关键词： Java programming language

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NearBySell : A Novel Approach to Localized E-Commerce with Focus on Seller-Customer Interaction and Enhanced Local Retail Network 11

NearBySell : A Novel Approach to Localized E-Commerce with F...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Chavan, Vishal Subhash Hake, Ashok Shrimant Rao, Bhukya Nageswar Kotkar, Suyash Sanjay Kolhapur Institute of Technology's College of Engineering Department of Computer Science and Business Systems Kolhapur India

ISBN: (纸本)9798350379136

This paper examines a novel online retail application optimized for local commerce via a location-based matching system. The application supports the sale of new and secondhand items, home goods, and vegetables, requiring users to register and input their locations. Transactions occur only when the buyer's and seller's locations align, reducing logistical challenges. The system features a review mechanism allowing customers to rate sellers, influencing future buyer decisions and seller reputation. Security is ensured through hand-to-hand payments and direct seller delivery, mitigating risks associated with digital payments and third-party delivery. Additionally, the application supports long-term relationships by maintaining permanent connections between customers and sellers. Technical aspects include the development of location-based matching algorithms, review systems, and security measures. Notably, the application incorporates advanced security features such as fake reviews detection and duplicate listings detection to protect both buyers and sellers from fraudulent activities. This study highlights how these components enhance local market efficiency and e-commerce reliability. © 2024 IEEE.

关键词： Location

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A Cyber-Risk Management Schema for Classifying Literature on Advanced Persistent Threats 17

A Cyber-Risk Management Schema for Classifying Literature on...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Kumar, Rajesh Pandey, Swapnil Birla Institute of Technology and Science Department of Computer Science and Information Systems K K Birla Goa Campus Goa Pilani India

ISBN: (纸本)9798331531195

Advanced Persistent Threats (APTs) are complex, multi-stage cyberattacks often backed by nation-states, targeting critical infrastructure with the potential to severely impact national economies. Due to their sophisticated tactics and high-stake implications, APTs are a prominent focus in both academic research and media. Multiple disciplines have contributed to APT research, offering various attack models, defense strategies, and mitigation techniques. However, the field lacks a systematic, cohesive classification framework, making it challenging for practitioners to identify the most relevant studies and methodologies for their needs. Addressing this gap, we propose a novel cyber-risk management schema for organizing scientific literature on APTs, structured to support a comprehensive understanding and application of APT-related knowledge. To demonstrate the schema's utility, we apply it to categorize the most frequently cited works in APT research, showcasing its effectiveness in systematizing diverse perspectives. We believe this work facilitates knowledge organization, empowering both researchers and practitioners in their fight against APTs. © 2025 IEEE.

关键词： Cyber attacks

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Semantic2Graph: graph-based multi-modal feature fusion for action segmentation in videos

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Applied Intelligence 2024年第2期54卷 2084-2099页

作者： Zhang, Junbin Tsai, Pei-Hsuan Tsai, Meng-Hsun Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan701 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and raise the accuracy. We construct a graph structure of video at the frame-level. Temporal edges are utilized to model the temporal relations and action order within videos. Additionally, we have designed positive and negative semantic edges, accompanied by corresponding edge weights, to capture both long-term and short-term semantic relationships in video actions. Node attributes encompass a rich set of multi-modal features extracted from video content, graph structures, and label text, encompassing visual, structural, and semantic cues. To synthesize this multi-modal information effectively, we employ a graph neural network (GNN) model to fuse multi-modal features for node action label classification. Experimental results demonstrate that Semantic2Graph outperforms state-of-the-art methods in terms of performance, particularly on benchmark datasets such as GTEA and 50Salads. Multiple ablation experiments further validate the effectiveness of semantic features in enhancing model performance. Notably, the inclusion of semantic edges in Semantic2Graph allows for the cost-effective capture of long-term dependencies, affirming its utility in addressing the challenges posed by computational resource constraints in video-based vision models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Romanow, Dominik Grzesik, Piotr Benecki, Pawel Niyomugaba, Alexandre Habimana, Theodore Kostrzewa, Daniel Tokarz, Krzysztof Hung, Che-Lun Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Ines Ruhengeri - Institute of Applied Sciences Department of Computer Science Rwanda Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei Taiwan

ISBN: (纸本)9798350362480

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models. © 2024 IEEE.

关键词： Data streams

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PADAAV: Enhancing Perception systems using GAN-generated Adversarial Augmented Domains for Autonomous Vehicles

PADAAV: Enhancing Perception Systems using GAN-generated Adv...

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Rawlley, Oshin Gupta, Shashank Birla Institute of Technology and Science Department of Computer Science & Information Systems Rajasthan Pilani India

ISBN: (纸本)9781665453813

In the field of autonomous vehicles (AV), it is crucial for the perceptual systems of the AVs to learn inter-domain adaptations in the absence of paired examples for detecting vehicular instances in unstructured real-world scenarios. One straightforward approach is to train the models directly on labeled synthetic datasets. However, this approach usually fails to achieve generality, owing to the domain bias between the real and fake databases of images. We therefore, propose a novel architecture that produces synthetic images based on cycle consistency, in the absence of labeled pair of images. We test the object detectors Detectron and SSD on four types of curated benchmark datasets to evaluate their robustness in detecting objects such as cars, bikes, and pedestrians on road. The four benchmark datasets contain a diversified set of image corruptions and a few variations are built using the proposed framework such as weather variations. The four datasets are PASCAL VOC and SYNTHIA dataset images, weather-translated images, variation-augmented images, and stylized renderings using Adain style transfer. After conducting extensive investigations, we observe a decreased classification loss when exposed to variable image quality. We also witness that augmenting the training datasets with variations in the wild aided in boosting the generalizing capability of the object detectors. The boost in performance is testified by the testing results showing better mAP. © 2023 IEEE.

关键词： Object detection

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