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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： M. Sangeetha B Srinivasa Kumar K Chokkanathan Ashok Kumar S. Krishna Prabha Sivakumar Sattanathan J K Periasamy Department of Computer Science Dr.N.G.P. Arts and Science College Coimbatore Tamil Nadu India Department of Engineering Mathematics Koneru Lakshmaiah Education Foundation Guntur(D.T) Andhra Pradesh India Department of AI Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science BanasthaliVidyapith Rajasthan India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

To enhance the performance of user personalized recommendation algorithms, this study offers a user personalized recommendation calculation based on deep learning network. Efficient and accurate personalized recommendation algorithms can effectively increase user experience fulfillment. Considering user behavior, this research suggests a useful advice. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their considerations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their contemplations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. The methodology solves the sparsity problem and outperforms existing algorithms. Implementing a user behavior-based recommendation method, which gauges users' interests based on certain evaluations of item qualities, is the primary commitment. Furthermore, this strategy makes use of a sequential purchase pattern to raise the caliber of recommendations.

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BACON: Boundary-guided Polypharmacy Side Effect Prediction via Integrating Molecular Structures and Biochemical Information

BACON: Boundary-guided Polypharmacy Side Effect Prediction v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yike Wang Huifang Ma Zihao Gao Zhixin Li Liang Chang College of Computer Science and Engineering Northwest Normal University Lanzhou China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin

Polypharmacy side effect prediction is a vital task in healthcare machine learning, aiming to predict the occurrence of multiple side effects when patients take drugs together. Existing researches mainly follow the deep encoder-decoder paradigm and suffer from the following two limitations: 1) The encoder simply combines inadequate information about drugs; 2) The decoder fails to capture dependencies among different side effects, thus hindering accurate prediction. To overcome the aforementioned limitations, we propose a boundary-guided polypharmacy side effect prediction method (BACON). Our framework constructs two complementary views based on drug’s chemical substructure and biochemical features and enhances drug representations via contrastive learning. The decoder incorporates a boundary-guided strategy to capture drug interaction dependencies for optimizing polypharmacy side effect prediction. Experimental results demonstrate BACON superiority over SOTA models in accurately predicting drug side effect events.

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Organizational Culture's Significance in Technology and Innovation Management 2

Organizational Culture's Significance in Technology and Inno...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Hari Krishna, Somanchi Fajarini, Ulfah Painoli, Arun Kant Visvesvaran, C. Jahnavi, K.N. Lourens, Melanie Vignana Bharathi Institute of Technology Department of Business Management Ghatkesar Mandal Medchal Malkajigiri Medchal Malkajigiri India Syarif Hidayatullah State Islamic University Faculty of Education Social Science Study Program Jakarta Indonesia Baddi University of Emerging Sciences and Technology School of Management Studies Himachal Pradesh Baddi India Sri Krishna College of Engineering and Technology Coimbatore India SIndhi Institute of Management Bangalore India Durban University of Technology Faculty of Management Sciences South Africa

ISBN: (纸本)9798350384277

Several studies have examined the connection between innovation, technology, and organizational culture in recent years. Management of technology and innovation is crucial to an organization's expansion and success. The development of an innovative organizational culture comes before technology and innovation management. This study's primary goal was to assess how organizational culture affects innovation in the Indian service sector. The study made use of a paradigm for understanding organisational culture created by Cameron and Quinn, which looks at culture as a collection of conflicting values from four angles. A descriptive survey research design was used in the investigation. Employees of service industries made up the study's population. More than twenty IT organizations participated in the survey, and 120 out of 180 respondents returned completed questionnaires. This indicated a response rate of 66%.The collected data was analyzed using inferential data analysis techniques and descriptive statistics. Tables and figures were created after analysis with IBM SPSS software. The results demonstrated that the strongest associations with innovation and technology were found in companies with flexible, collaborative, and employee-friendly cultures. Lastly, the study suggests areas for additional research, such as employing a new organizational culture model for the study or qualitative techniques for data collecting and analysis to reveal deeper cultural features. © 2024 IEEE.

关键词： importance Organisational culture service industry technology and innovation management

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Dynamic Fault Diagnosis in Wind Turbines: A GNN-LSTM Approach

Dynamic Fault Diagnosis in Wind Turbines: A GNN-LSTM Approac...

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： G V Ramesh Babu Sagar Baburao Porlekar H. Mohammed Ali Veera Ankalu Vuyyuru Mohammed Saleh Al Ansari I Infant Raj Department of Computer Science SV University Tirupati India Department of Technology Shivaji University Maharashtra India Department Mechanical Engineering SRM Institute of Science and Technology Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Chemical Engineering University of Bahrain Bahrain Department of CSE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Wind turbines are pivotal in renewable energy production, yet their efficient operation is hindered by potential faults and failures. Traditional fault diagnosis methods often struggle to adapt to the dynamic nature of these systems, leading to delayed or inaccurate detection of faults. To overcome this challenge, research proposed aninnovative approach leveraging Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) networks for dynamic fault diagnosis in wind turbines. Previous methods typically rely on static models or simplistic time-series analysis, limiting their ability to capture the complex interactions within wind turbine systems. The approach integrates GNNs to model the spatial relationships between various components of the turbine, enabling a more holistic understanding of the system's behavior. Additionally, LSTM networks are employed to capture temporal patterns and dynamics, allowing for real-time fault detection and prediction. Through extensive trials on real-world wind turbine data, research establish the effectiveness of proposed GNN-LSTM approach. The method outperforms traditional static models and time-series analysis techniques in terms of fault detection accuracy and timeliness. By accurately identifying faults as they occur and predicting potential failures, this approach enables proactive maintenance strategies, minimizing downtime and maximizing turbine efficiency. In conclusion, the integration of GNNs and LSTM networks offers a promising avenue for dynamic fault diagnosis in wind turbines. By leveraging both spatial and temporal information, the approach significantly improves the reliability and efficiency of wind turbine operations, contributing to the advancement of renewable energy technologies. The accuracy of 95.2% achieved by this proposed GNN-LSTM approach in dynamic fault diagnosis for wind turbines underscores its efficacy in accurately detecting and predicting faults within these complex systems was implemented in p

关键词： Fault diagnosis Renewable energy sources Analytical models Accuracy Wind energy Fault detection Time series analysis

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Time-varying Nonlinear Effects in Terahertz Generation

arXiv

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arXiv 2024年

作者： Lu, Yongchang Zhang, Xueqian Qiu, Haidi Niu, Li Chen, Xieyu Xu, Quan Zhang, Weili Zhang, Shuang Han, Jiaguang Center for Terahertz waves College of Precision Instrument and Optoelectronics Engineering Tianjin University The Key Laboratory of Optoelectronics Information and Technology Ministry of Education Tianjin300072 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Department of Physics Faculty of Science University of Hong Kong Hong Kong

Time-varying effects have unveiled new possibilities for manipulating electromagnetic waves through the temporal dimension. In this study, we experimentally explore these effects in the nonlinear optical process of terahertz (THz) generation using optically pumped indium tin oxide (ITO) films. The ultrafast carrier dynamics in the ITO film endow the second-order nonlinear susceptibility (χ(2)) with sub-picosecond temporal evolution, establishing a temporal boundary for the generated THz waves. We observe significant amplitude and frequency modulations in the THz generation at various transients, attributed to the time-varying complex amplitude of the χ(2). Moreover, we also observed polarization modulations when further exploiting the tensor properties of χ(2). This work advances the exploration of time-varying effects into the nonlinear regime through frequency down-conversion, effectively transferring the strong time-varying material response from the near-infrared (NIR) band to the THz band. These findings open up new opportunities for realizing time-varying phenomena that specifically require single-cycle modulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

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IoT Attack Detection and Prevention Through Machine Learning System 2

IoT Attack Detection and Prevention Through Machine Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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Adaptive System for Human Scenario Forecasting in Intelligent Homes

Adaptive System for Human Scenario Forecasting in Intelligen...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： P. Kalyani K. V. Prashanth R. Manivannan Raksha Puthran Shruti Jalapur Dhananjaya Babu K AI&ML Department Sree Venkateswara College of Engineering Nellore Andhra Pradesh India Department of Electronics & Communication Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology(Deemed to be University) Chennai India Department of MCA NMAM Institute of Technology NITTE (Deemed to be University) Udupi India Department of Computer Science and Engineering Christ University Bangalore India Department of Electrical and Electronics Engineering Annamacharya University Rajampet Andhra Pradesh India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Due to the rise in energy usage in recent years, there has been a worldwide push to reduce energy use, particularly in the housing sector. When the concept of a "smart house" first emerged, it brought together automation and control via communication network technologies, as well as warmth from many heat source mechanisms and the terminology "process controllers" stated earlier. The Internet of Things, or IoT, is a multibillion dollar industry that shows how overlapping technologies span industries. It does this by utilizing the internet as an objective standard for sharing information that IoT services manage, supervise, or *** home automation systems, big data, and machine learning, together with the Internet of Things, to increase energy efficiency is another exciting topic. This article introduces HEMS-IoT, a comprehensive energy management system designed for the modern home. It leverages big data and machine learning to provide security and convenience in a comprehensive, eco-efficient setting. Weka API and the J48 neural network technology were used to extract user behavior and energy consumption trends. This approach was used to categorize loads depending on *** Mahout with Rule-based language (RuleML) in a smart house for user comfort and security This is because these technologies are able to predict what consumers would require for energy action and supply it during this consultation because to the aggregation of user preferences. XtraPrinting As they demonstrated how they would monitor the instance, the article offers an experimental way for monitoring intelligent buildings for comfort and safety with the least amount of energy use. Therefore, their approach, which explains the demonstration success rate, is responsible for the test’s high performance.

关键词： Industries Energy consumption Machine learning Big Data Energy efficiency Safety Behavioral sciences Internet of Things Security Monitoring

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A Deep Learning Neural Network-based System for Food Recognition and Calorie Estimation

A Deep Learning Neural Network-based System for Food Recogni...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Pallavi Yarde Dibyhash Bordoloi Rahul Mohan Chavan Vipul Vekariya Harshal Patil L Natrayan Department of Technology & Management Balaji Institute of Technology & Management Sri Balaji University Pune Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Physical Education and Sports Kholeshwar College Ambajogai Maharashtra India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The global increase in health awareness reflects a rising desire among individuals to pursue healthy and normal lives. Obesity has emerged as an important health issue in the modern world due to its rapid pace. Obesity is defined by medical authorities as having a Body Mass Index (BMI) of more than 30 kg/m2, which can lead to a variety of health difficulties like liver failure, cholesterol, respiratory disorders, diabetes, cardiac complications, and, in serious cases, cancer. Maintaining a diet rich in nutrients and low in calories is critical in the fight against obesity. Individuals, on the other hand, frequently struggle with food decisions. In response to this difficulty, this research presents a system that offers calorific calculation followed by food recognition to aid in preventing obesity. The goal of this research is to identify types of food and estimate their calorific values. For food recognition, a novel Attention-based Dilated Convolutional Neural Network Logistic Regression (A-CNNLR) model is introduced and compared to a traditional CNN model. The proposed ACNNLR model demonstrates a recognition rate of 99%, surpassing the CNN model’s 97%. The study determines the calories in the food according to their volume using data from the ECUST Food Dataset. This study provides a novel approach to the fight against obesity by using sophisticated algorithms for precise food detection and calorie estimation.

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A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

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computer Modeling in engineering & Sciences 2020年第7期124卷 303-313页

作者： Yan Li Dayou Liu Yungang Zhu Jie Liu College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China

Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but *** differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory *** a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between ***,in this way,the similarities between the pairwise GRNs are not taken into *** joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach *** this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential ***,a Bayesian inference method is used to make joint differential analysis by solving the integrated *** evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different *** performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet *** the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.

关键词： Gene regulatory networks structural equation models joint

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