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2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Rasheed, Kumeel Ammad, Syed Said, Akhunzada Younis Balbehaith, Mohammed Oad, Vipin Kumar Khan, Aimal Bahria University Dept. of Management Sciences Islamabad Pakistan Monash University Dept. of Civil Engineering Melbourne Australia Comsats University Dept. of Computer Science Islamabad Pakistan University of Portsmouth Dept. of Business Management and Law Portsmouth United Kingdom Gdañsk University of Technology Dept. of Civil and Envr. Engg Gdañsk Poland

ISBN: (纸本)9781665490580

The report presents a low-cost and flexible solution to control and monitor home and industrial appliances. The popularity of automation devices has increased greatly in recent years due to higher affordability and simplicity through IoT phone and tablet connectivity. For this purpose, we have developed the IoT Outlet: a stand-alone communication unit, used to connect outlets to the internet. The IoT Plug is a power switch which can be accessed via Wi-Fi connection. Users can plug devices into the IoT Plug to remotely switch power on/off, get information of device's power consumption and local motion status, using wireless commands from the web and Android application. To access IoT Plug, it is connected with a router in Wi-Fi access point mode and control the Plug. An Android-based IoT phone application and Website having the control panels, employs standard operations such as Get and Post requests that return responses to communicate between the remote user and the Plug using PHP MySQL Databases. The main purpose of the paper is for IoT application to control and save power by setting the time scheduling through the controlling interface. Through this IoT device we can control and get feedback from any plugged device. There is a lot of need on large scale industries, laboratories, military fields and real-life situation, where one wants to know the status of the electric operated devices. This project is the building block for the application such as wireless controlling of any appliance that can be plugged in with local voltages i.e. 220V. © 2022 IEEE.

关键词： Costs

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Role of Online Shopping Websites in Consumer Satisfaction Based Statistical Analysis 12

Role of Online Shopping Websites in Consumer Satisfaction Ba...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Mittal, Mohit Singh, Hardeep Chauhan, Shaifali Gupta, Swadha Knowtion GmbH Karlsruhe Germany Dept. of Computer Engineering & Technology Amritsar India Prestige Institute of Management Gwalior India Thapar Institute of Engineering and Technology Deptt. of Comp. Sci. and Engg. Patiala India

ISBN: (纸本)9781665462617

Nowadays, online shopping is very popular as there are many platforms are available. To dig into the consumer behavior for shopping there are many researchers, industries are working to provide the model to analyze consumer satisfaction. As people are searching a lot over the websites according to their interests, so the data is collected by the platforms and do analysis over it to find the consumer behaviour to improve their search and improve the platforms accordingly. Therefore, there is also need to analyze the selfcongurance, impulse buying and price sensitivity parameters which are directly or indirectly impact on the consumer satisfaction. Our aim is to develop a model to analyzes above mentioned parameters and find the relationship between each other. We also suggested some hypotheses and evaluate based statistical technique. The overall result is quiet satisfying. © 2023 IEEE.

关键词： Statistical methods

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Predictive Analysis for Sales Forecasting Using Machine Learning: Leveraging Data-Driven Insights for Enhanced Business Planning and Decision-Making

Predictive Analysis for Sales Forecasting Using Machine Lear...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Koner, Jonardan Deore, Shailesh Shivaji Roja, S. Sahastrabuddhe, Kalyani Nilesh Kalshetti, Prashant Rose, Anthony Nicmar University Pune India Ssvps Bapusaheb Shivajirao Deore College of Engineering Department of Computer Engineering Maharashtra Dhule India Bharathidasan University Department of Computer Science Engineering and Applications Khajamalai Campus Trichy23 India Dr. D. Y. Patil Institute of Management and Research Pimpri Pune India Dr. D Y Patil Vidyapeeth Global Business School and Research Centre Dept. of Bba Pune India Institute of Management and Entrepreneurship Development Department of Management Studies Maharashtra Pune India

ISBN: (纸本)9798331543617

Within the modern trade scene, leveraging prescient investigation for deal estimating has developed as a basic procedure for organizations pointing to optimize their decision-making forms. This consideration centres on the application of the XG Boost calculation, a well-known machine learning method, to tackle data-driven experiences for upgraded deals determining, thus encouraging prevalent commerce arranging. Through an in-dept. investigation of authentic deals information, the inquiry about illustrates the noteworthiness of consolidating machine learning strategies, especially the XG Boost calculation, for exact and solid deal forecasts. By leveraging a comprehensive dataset including different variables affecting deals, such as client socioeconomics, regular patterns, showcasing activities, and financial markets, this exhibits the algorithm's capability to capture complex connections inside the information and produce exact figures. Besides, the investigation emphasizes. interpretability of the XGBoost calculation, empowering partners to comprehend the fundamental components affecting deal expectations. This straightforwardness cultivates a more profound understanding of the driving powers behind deal patterns, encouraging educated decision-making and proactive commerce arranging. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring the Use of Artificial Intelligence for Automated Compliant Transaction Processing 2

Exploring the Use of Artificial Intelligence for Automated C...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Landge, Sunil Rajaram Gunturu, Srinivasa Rao Josyula, Hari Prasad Kiruthiga, T. Fatima, Eram New York NY United States Houston TX United States Princeton NJ United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India G.L. Bajaj Institute of Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

This paper explores the capability of the use of synthetic Intelligence (AI) for automatic compliant transaction processing. AI-primarily based structures can examine large volumes of transaction records, detect inconsistencies and compliance troubles, and offer guidelines for development. AI can also be used to create models to pick out fraudulent transactions, conduct patron segmentation and analysis, expect customer behavior, and alert establishments of capability issues. Further, AI-based algorithms can be used to automate a huge variety of responsibilities, inclusive of approval tactics and automatic record storage and retrieval. The implementation of AI-based total technology is expected to lessen guide processing efforts and time, resulting in green, streamlined compliance tactics. By leveraging AI generation, companies could be capable of improving consumer revel, lessening dangers, and improving their normal performance. This paper examines the contemporary country of the artwork in AI-based technologies and discusses their capacity packages within the computerized compliant transaction processing area. © 2024 IEEE.

关键词： Capability computerized Customer Establishments Fraudulent

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Automated Weed Species Classification in Rice Cultivation Using Deep Learning

Automated Weed Species Classification in Rice Cultivation Us...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Hamdadur Rahman Mahbub Alam Khan Shakil Khan Zishad Hossain Limon Md Ismail Hossain Siddiqui Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib Sheikh Shemanto Afridi Shafiur Rahman Dept. of Management of Information System International American University CA USA Dept. of Management Information System Pacific State University CA USA Dept. of Business Analytics International American University CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Weeds in rice fields compete for essential resources such as water, nutrients, and light, leading to reduced crop yields, increased production costs, and environmental concerns. Traditional methods of weed detection are often labor-intensive, subjective, and inefficient, highlighting the need for automated approaches in precision agriculture. This study introduces an ensemble model designed for robust and accurate classification of rice weeds, addressing the shortcomings of conventional methods and enhancing detection scalability. The proposed ensemble model combines four deep learning architectures: EfficientNet-V2, SqueezeNet, InceptionResNet-v2, and DenseNet-201. By leveraging the complementary strengths of these models, we aim to improve performance. The study utilizes the Rice Field Weed Detection Dataset, which contains 3,632 high-resolution images that have been augmented to a total of 21,792 samples. Advanced preprocessing techniques such as resizing, normalization, and augmentation are applied to ensure effective feature extraction. Our model achieved a remarkable classification accuracy of 99.51%, significantly surpassing individual models and other state-of-the-art approaches. In addition, we developed a web-based application for real-time weed detection, providing a user-friendly platform for farmers and agricultural professionals to implement timely weed management strategies. This system supports sustainable farming by reducing the need for excessive herbicide use and promoting environmentally friendly practices. This research highlights the potential of ensemble deep learning for precise agricultural weed detection. Future work will focus on integrating diverse datasets and developing mobile applications to enhance accessibility and scalability.

关键词： Deep learning Accuracy Scalability Transfer learning Feature extraction Real-time systems Mobile applications Crop yield Water resources Farming

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Simplifying Structure and Pattern in Complex Social Networks Using Graph Isomorphism

Simplifying Structure and Pattern in Complex Social Networks...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Arunakumari B N Kshama S B Swasti S Sameeksha B H Sonia Puri Rubini P A Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Dept. of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India

In recent years, networks have grown in size and complexity, traditional network testing methods have become time-consuming and inefficient. As a result, there is a need for more efficient and reliable network testing methods. One important topic in the field of network testing is graph pattern matching. Network analysis relies on graph pattern matching. The paper discusses the use of graph isomorphism for network testing. This paper also gives an overview of recent advances in isomorphism testing, which determines if two networks are structurally identical despite potential labeling discrepancies between them. The combination of isomorphism and pattern matching in network analysis is used to identify equivalent structures in different networks. This approach is used to identify and compare common patterns in complex network structures, such as those found in social networks.

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Exploring the predictive potential of artificial neural networks in enhancing mechanical properties of derivatives of graphene nanocomposites: a data-driven approach

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International Journal of Information Technology (Singapore) 2025年第2期17卷 999-1005页

作者： Rao, D. Nageswara Dahiya, Mamta Arora, Amit Kumar Jandwani, Paras Verma, Gunjan CSE ACED Alliance University Chikkahagade cross Anekal Bangalore India MAIT Maharaja Agrasen University Himachal Pradesh Solan India Computer Science and Engineering IIT Madras Chennai India Accenture LLP Philadelphia PA United States Dept. of Master of Computer Applications G L Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India

The given manuscript delves into the realm of materials science and engineering, specifically focusing on the utilization of different derivatives of graphene reinforced in matrix material (DGMM’s) to enhance mechanical properties. Graphene (Gr), with its remarkable characteristics, holds immense promise as reinforcement in composites and nanocomposites (NCs). Using linear regression and artificial neural networks (ANN) framework, this study predicts the weight% of enhanced mechanical properties in DGMMs. By analyzing a dataset comprising various graphene derivatives, matrix materials, processing methods, and resulting enhancements, the ANN model offers valuable insights into the intricate relationships within these NCs. The findings reveal the potential of ANN models in optimizing the design and fabrication processes of DGMMs, thereby accelerating materials development and enabling tailored formulations for specific applications. Also, the given manuscript motivates for future research to enhance the predictive accuracy and broaden the scope of applications for *** confirm the corresponding author is correctly identified and amend if ***, it is *** confirm the inserted city name is correct in Affiliation (3, 4) and amend if ***, it is correct and confirmed © Bharati Vidyapeeth's Institute of computer Applications and management 2024.

关键词： Artificial neural networks Graphene Linear regression Nanocomposites Nanomaterial

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On Optimal Synchronization of Diffusively Coupled Heterogeneous Van der Pol Oscillators

arXiv

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arXiv 2023年

作者： Trummel, Tabea Liu, Zonglin Stursberg, Olaf Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Germany

This paper proposes a novel method to achieve and preserve synchronization for a set of connected heterogeneous Van der Pol oscillators. Unlike the state-of-the-art synchronization methods, in which a large coupling gain is applied to couple any pair of connected oscillators, the proposed method first casts the whole synchronization process into two phases. The first one considers the period from the beginning to the first instant of synchronization, while the second phase covers the following time in which synchronization must be preserved. It is shown that a large coupling gain is adopted for the first phase, while the averaged coupling gain to preserve the synchronization in the second phase can be reduced significantly by using an offline optimized coupling law. Efficiency and performance of this method are confirmed by a set of numerical tests with different graphs and system dynamics. © 2023, CC BY-NC-ND.

关键词： Synchronization

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Construction Site Modeling Objects Using Artificial Intelligence and BIM Technology: A Multi-Stage Approach

Construction Site Modeling Objects Using Artificial Intellig...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Dolhopolov, Serhii Honcharenko, Tetyana Savenko, Volodymyr Balina, Olena Bezklubenko, Iryna Liashchenko, Tamara Kyiv National University of Construction and Architecture Dept. of Cyber Security and Computer Engineering Kyiv Ukraine Kyiv National University of Construction and Architecture Dept. of Information Technologies of Design and Applied Mathematics Kyiv Ukraine Kyiv National University of Construction and Architecture Dept. of Organization and Management of Construction Kyiv Ukraine Kyiv National University of Construction and Architecture Dept. of Information Technologies Kyiv Ukraine

ISBN: (纸本)9798350335040

This study presents a multi-stage approach for building object models (BOMs) on a construction site, aimed at creating an 'evolutionary' digital twin. The integration of building information modeling (BIM) and artificial intelligence is used to achieve this goal, with the use of photo modeling using moving cameras and the potential integration of IoT technologies also discussed. A comprehensive artificial intelligence system, combining Convolutional Neural Network (CNN) and Feed Forward Neural Network (FFNN) architectures, has been developed to detect, categorize, and evaluate BIM projects throughout their life cycle. The scalability prospects for point cloud and mesh models, as well as the use of big data technology to optimize the representation of the digital twin, are also addressed. The study determines the effectiveness of construction site conformance detection during the construction of a BIM model, providing consistency and a quantitative evaluation of the processes taking place on the construction site. The findings of this research can be used to enhance BIM modeling methods and concepts toward a multi-stage representation of the digital twin of the construction site. © 2023 IEEE.

关键词： Architectural design

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Implementation of Dynamic Spectrum Access Techniques in Cognitive Radio Networks under Generalized Fading Conditions

Implementation of Dynamic Spectrum Access Techniques in Cogn...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Shobhit Garg Amulya Sharma Divyansh Devgan Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Management Studies Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper is organized as follows: Section II provides a background on our research work based on DSA in CRNs using CR under different fading conditions. c) Performance: We also study how effective adaptive algorithms along with the machine learning can prove in achieving higher spectrum sensing accuracy and decision making on run time. By conducting simulations and extensive performance evaluation, we show that the traditional solutions fall short when compared to our AI-based methodology in terms of accuracy especially under harsh fading conditions. Finally, sensitivity analysis highlights the control parameters that need to be submitted to further optimization for better system performance. With all above analysis, our results indicate that DSA can alleviate spectrum underutilization problem and enhance communication reliability in CRNs. Thus, with proper adaptation strategies and machine learning algorithms, the DSA systems provide an appealing opportunity to meet the continuously increasing requirements of bandwidth-consuming applications in current days wireless networks. The results of this research are helpful in providing effective value input to Advances in the understanding and design of robust, efficient spectrum management approaches for dynamic and diverse wireless settings.

关键词： Fading channels Wireless sensor networks Accuracy Sensitivity analysis System performance Wireless networks Machine learning Sensors Reliability Optimization

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