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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381689

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： Predictive maintenance

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Big data Analytics for Quality Control in The Pharmaceutical Industry

Big Data Analytics for Quality Control in The Pharmaceutical...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P. Malathi S. Jansi M.V. Prabhakaran K. Senbagam M.P. Sujatha R Sadaieswaran Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer science and Engineering Acharya college of Engineering and Technology Achariyapuram Puducherry India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Applications Tagore College of arts and science Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns.

关键词： Waste reduction Technological innovation data analysis Fault detection Quality control Big data Real-time systems Manufacturing Predictive analytics Pharmaceuticals

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Text-Driven Video Style Transfer with State-Space Models: Extending StyleMamba for Temporal Coherence

arXiv

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arXiv 2025年

作者： Li, Chao Park, Minsu Rossi, Cristina Li, Zhuang Dept. of Computer Science Eastern Asia Institute of Technology Beijing China AI Research Center Seoul National Technical Labs Seoul Korea Republic of School of Computing University of South Europe Rome Italy Department of Data Science Western Frontier University CA United States

StyleMamba has recently demonstrated efficient text-driven image style transfer by leveraging state-space models (SSMs) and masked directional losses. In this paper, we extend the StyleMamba framework to handle video sequences. We propose new temporal modules, including a Video State-Space Fusion Module to model inter-frame dependencies and a novel Temporal Masked Directional Loss that ensures style consistency while addressing scene changes and partial occlusions. Additionally, we introduce a Temporal Second-Order Loss to suppress abrupt style variations across consecutive frames. Our experiments on DAVIS and UCF101 show that the proposed approach outperforms competing methods in terms of style consistency, smoothness, and computational efficiency. We believe our new framework paves the way for real-time text-driven video stylization with state-of-the-art perceptual results. Copyright © 2025, The Authors. All rights reserved.

关键词： Video recording

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Enabling Automated Machine Learning for Model-Driven AI Engineering

arXiv

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arXiv 2022年

作者： Moin, Armin Wattanavaekin, Ukrit Lungu, Alexandra Challenger, Moharram Badii, Atta Günnemann, Stephan Department of Informatics Technical University of Munich Germany Department of Computer Science University of Antwerp Flanders Make Belgium Department of Computer Science University of Reading United Kingdom Department of Informatics Munich Data Science Institute Technical University of Munich Germany

Developing smart software services requires both Software Engineering and Artificial Intelligence (AI) skills. AI practitioners, such as data scientists often focus on the AI side, for example, creating and training Machine Learning (ML) models given a specific use case and data. They are typically not concerned with the entire software development life-cycle, architectural decisions for the system and performance issues beyond the predictive ML models (e.g., regarding the security, privacy, throughput, scalability, availability, as well as ethical, legal and regulatory compliance). In this manuscript, we propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems by choosing the most appropriate ML model, algorithm and techniques with suitable hyper-parameters for the task at hand. To validate our work, we carry out a case study in the smart energy domain. © 2022, CC BY-NC-SA.

关键词： Regulatory compliance

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Natural Language Processing in Healthcare: Intelligent System for Extracting Insights from Clinical Text data

Natural Language Processing in Healthcare: Intelligent Syste...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： C. Rashmi Dava Srinivas S. Prabu P N Santosh Kumar Ayesha Siddiqua IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineeing Jyothishmathi Institute of Technology & Science Karimnagar India Department of ECE Mahendra Institute of Technology Namakkal India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The World Health Organization identified COVID-19 to be a new pandemic in March 2020. This fatal virus spread over the world, affecting many different countries. Massive volumes of useful data were provided by social media platforms like Twitter on the outbreak to help evaluate health-related decision-making. To predict disease occurrence and issue early warnings, thus implement that user’s attitudes be evaluated through the use of efficient deep learning. The gathered tweets were pre-processed and divided into three categories like neutral, positive, and negative. In the second step, various features were extracted from the articles using a number of popular techniques to capture feature dataset, including TF-IDF, FastText, and Glove. The implemented TFIDF has combination of semantic features (Glove and FAsText) to accurately describe posts and enhance the classification process utilizing the deep learning method. The implemented TF-IDF-CNN method’s performance is evaluated using Twitter dataset. Implemented method achieved better performance than the existing intrusion detection methods including NLP-BiLSTM, TF-IDF, SVM and CF and the metrics are 96% of accuracy, 0.89 of f-measure, 0.93 of precision, and 0.90 of recall.

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Significance-aware Medication Recommendation with Medication Representation Learning

Significance-aware Medication Recommendation with Medication...

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International Conference on computer Supported Cooperative Work in Design

作者： Yishuo Li Zhufeng Shao Weimin Chen Shoujin Wang Yuehan Du Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Mental Health Center Jinan China Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The goal of medication recommendation system is to recommend appropriate pharmaceutical interventions based on a patient’s diagnosis. Most of existing approaches often formulate these recommendations use data on diagnoses, procedures, and prescriptions accumulated in the electronic health records (EHR), and despite the great successes, they seem to have limitations on modelling the significance of medication to a patient’s current visit and mining fine-grained medication representation information. To address these issues, we propose a novel Significanceaware Medication Recommendation (SMRec) framework built on significance of medication to patients and fine-grained medication representation learning. Specifically, we first design a encoding mechanism to compute significance information of medications for each patient’s visit. Then, we utilize the set-level medication co-occurrence graph based on patients’ medical history which integrates temporal dependency to learn fine-grained medication representations. Experimental results on the publicly available MIMIC-III dataset demonstrate the superior effectiveness of our model compared to other approaches 1 .

关键词： Representation learning Drugs Federated learning Computational modeling MIMICs Encoding History

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A Hybrid Algorithm for Secure Image based Encryption and Steganographic Technique in combination with DET and AES Algorithms

A Hybrid Algorithm for Secure Image based Encryption and Ste...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Vinod, D. Nalini, M.K. Dhinakaran, K. Elantamilan, D. Gnanavel, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Engineering Department of Artificial Intelligence and Data Science Chennai India Vallal P.T. Lee Chengalvaraya Naicker Arts and Science College Department of Computer Science Chennai112 India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665495295

Instead of using a complex mathematical equation to key a host image, this Image Encryption algorithm uses a hybrid wavelet algorithm to increase the host image's possibilities. Here, we split different regions of the host image and the fusing image by using DWT and intermixing those regions to achieve another vision processed by DWT of the same format, which inversion results in an encrypted image. Even a brute force assault can't break the association between this original image and the fused image using a basic keying strategy. When decrypting this picture, we need to know the key and dummy image used to connect it. It would be hard to decrypt this Image matrix without knowing its layout, and the decryption time is supposedly longer than the encryption period. And Using steganography, data may be made to appear as if they never existed in another media, allowing for secure communication. Secure communication is achieved through the use of steganography. Steganography utilizing the DWT (Discrete Wavelet Transform) is proposed in this study, which uses a coefficient replacement technique to boost security while maintaining high capacity. The cover picture is encrypted using the Advanced Encryption Standard (AES) with a modification, while the payload image is encrypted using Haar and biorthogonal DWT. The payload image's high-frequency bands contain the resulting coefficients of the payload picture. A new concept is being introduced to create an intermediate stage picture: using encrypted payload to replace the HH sub-band coefficients. The suggested approach outperforms current ones in terms of both capacity and security. © 2022 IEEE.

关键词： Cryptography

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Evaluation and Design of Robotic Hand Picking Operations using Intelligent Motor Unit

Evaluation and Design of Robotic Hand Picking Operations usi...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Gnanavel, R. Vinod, D. Nalini, M.K. Dhinakaran, K. Elantamilan, D. R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Eneineering Department of Artificial Intelligence and Data Science Chennai India VallalP. T. Lee Chengalvaraya Naicker Arts and Science College Department of Computer Science Chennai India

ISBN: (纸本)9781665495295

Picking actions performed by industrial robots may be evaluated using a new architecture in this article. It has always been a goal of humankind to imbue their products with lifelike characteristics in an effort to create a substitute for themselves to carry out their commands and to work in difficult environments like the present. The most common image of a robot is that of a machine that resembles a human person in both appearance and operation. Automation is being replaced by robotics in an effort to boost output and ensure consistency in product quality. Despite the fact that every effort is being made to provide industrial robots increasingly anthropomorphic and humanlike traits and superhuman skills, today's industrial robots may not resemble humans in the slightest. A robotic manipulator, more generally known as a robotic arm, is a typical form of industrial robot. Moving joints connect stiff chains in either an open or closed kinematic structure. It is possible to think of a link's structure as a human torso with the waist, upper arm and forearm as joints [1]. The wrist joint at the end of the arm connects end effectors such as a tool and its mounting fixture, or a gripper or any other device for functioning. This workstation features a 6-axis Force/Torque pick and place robot for loading and packaging products. A six-axis Force/Torque design was used to properly evaluate and account for all of the possible loading process concerns when building the pick and place robot. © 2022 IEEE.

关键词： Robotic arms

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EDPP: Intelligent and Robust data Sharing Framework using Enhanced data Preserving Paradigm

EDPP: Intelligent and Robust Data Sharing Framework using En...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Elantamilan, D. Gnanavel, R. Vinod, D. Nalini, M.K. Dhinakaran, K. Vallal P. T. Lee Chengalvaraya Naicker Arts and Science College Department of Computer Science Chennai112 India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Engineering Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665495295

Growing amounts of information sources are required as the quantity of available information from various sources, both self governing and heterogeneous data maintenance requirement increases. A major component of the increased interest in big data is the heterogeneity of the data itself. In this work, data communications is researched, in which it is the method of constructing a specific case of direct instruction from an illustration of reference conceptual framework, so that when the eventually results entity conforms with target schema configuration. The logic of schema mappings are high-level expressions used to specify relationships across databases and are often used in this process. The experimental results of this paper provide proper proof to the efficiency of research and the results are acquired based on the real-Time working emulations. © 2022 IEEE.

关键词： Electronic data interchange

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Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

arXiv

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arXiv 2023年

作者： Wu, Peng Liu, Jing He, Xiangteng Peng, Yuxin Wang, Peng Zhang, Yanning The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Poly-technical University China The Guangzhou Institute of Technology Xidian University China The Wangxuan Institute of Computer Technology Peking University China

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., "vandalism", is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks and design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method. Captions are publicly released at https://***/Roc-Ng/VAR. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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