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Real-Time Motion Detection for Cargo Tracking and Management in Industrial Warehouses

Real-Time Motion Detection for Cargo Tracking and Management...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Neetha S, S. Pandey, Vijay Kumar Sharma, Amit Kumar Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Mechanical Engineering Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

this paper offers a unique method for realtime movement detection in commercial warehouses primarily based on laptop vision and machine-getting-to-know algorithms. The intention is to provide green and affordable monitoring and management of cargo in business warehouses within the contexts of protection, green stocking, and inventory control. The movement detection device proposed in this paper uses deep studying strategies to come across music-transferring gadgets so that you can become aware of capability hassle regions and optimize warehouse operations. Predominantly, the proposed system comprises two components: a convolutional neural community (CNN)-primarily based object detector and a recurrent neural community (RNN)-based movement classifier. With the CNN version, a fixed of photo frames from CCTV cameras are input to hit upon and classify objects within a commercial warehouse. The RNN classifier uses the detected gadgets as inputs to predict the motion path of the objects. The realtime motion monitoring device is carried out on a standard laptop with freely-to-be-had open-source libraries. It achieves an average body processing pace of 30 frames in line with-2nd. Experimental results on an actual-international commercial warehouse information set exhibit the system's effectiveness. In conclusion, this paper gives a low-priced and easy-to-implement actual-time movement detection system to help control the warehouse. © 2024 IEEE.

关键词： commercial convolutional information management predominantly

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A Novel Approach to Predicting the Cardiovascular Sickness 1

引用

International Conference on Advances in Data Science and Computing Technologies, ASDC 2022

作者： Swain, Debabrata Kava, Meet Jadhav, Ranjana Satapathy, Santosh Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar382421 India Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9789819936564

ISBN: (纸本)9789819936557

Cardiovascular illness is one of the prime sources for the loss of many human lives every year. The prime cause for mortality is the absence of past diagnostic information. Hence, for better prediction of the disease, many researchers have proposed this reason allows the researchers to build new and optimized machine learning models for betterment in prediction accuracy of such diseases with low false-negative rate. Here, a voting method is applied which uses Logistic Regression, Random Forest, XGBoost, and Naive Bayes to predict the nature of heart conditions. The system is trained, tested, and evaluated based on accuracy, precision, F1 score, Recall, and AUC. Testing witnessed an accuracy of 78%. The results show that ensemble modeling is more accurate when compared to individual models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Mental Health Disorder Prediction Using Recurrent Neural Network Algorithm

Mental Health Disorder Prediction Using Recurrent Neural Net...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mariappan, Umasree Balakrishnan, D. Merline, G. Sandhia, M. Reddy, Dubba Saitej Gagan Teja, Sattineni P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Sri Vidya College of Engineering & Technology Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar626005 India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798350361537

Mental health disorders pose a significant challenge to healthcare professionals due to their complex and multifactorial nature. Early identification and accurate prediction of mental health disorders play a crucial role in timely intervention and effective treatment planning. The objective of this study is to explore the potential of deep learning algorithms in predicting mental health disorders, specifically focusing on depression, anxiety, and schizophrenia. The study utilizes a dataset containing clinical and demographic information of patients diagnosed with these disorders. The dataset was pre-processed using feature scaling algorithm, and features dimensionality reduction is done using t-Distributed Stochastic Neighbor Embedding (t-SNE) to train and evaluate deep learning models. These models were trained on large-scale datasets, allowing for the identification of complex patterns and relationships within the data. The training on dataset is done by using the Recurrent Neural Networks. The performance of the models was evaluated using standard evaluation metrics, such as accuracy, precision, recall, and F1-score. The results indicate that deep learning algorithms have shown promising results in predicting mental health disorders. The models achieved high accuracy rates, with some models surpassing the performance of traditional machine learning algorithms. Performance evaluation of the model is conducted using relevant metrics such as precision, recall, and F1-score, on a separate validation or test dataset from which it is proved that the proposed methodology attains 6% performance improvement than the existing methodology. © 2024 IEEE.

关键词： Recurrent neural networks

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ADAPT: Attention-Driven Domain Adaptation for Inter-cluster Workload Forecasting in Cloud Data Centers 17th

ADAPT: Attention-Driven Domain Adaptation for Inter-cluster...

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17th International Conference on Cloud Computing, CLOUD 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Mahbub, Nosin Ibna Sinthia, Afsana Kabir Jeon, Mincheol Park, Junyoung Huh, Eui-Nam Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si Korea Republic of Korea Institute of Science and Technology Information Daejeon Korea Republic of

ISBN: (纸本)9783031771521

Cloud computing has recently gained popularity due to its cost-effective and high-quality services. Cloud-native systems are expected to host more than 95% of digital workloads. Cloud service providers face two significant challenges: real-time workload predictions and effective resource management. Furthermore, allocating resources over time may result in a suboptimal execution environment due to considerable increases and decreases in workload that follow time-dependent patterns. Recent breakthroughs in deep learning have garnered widespread favor for predicting extremely nonlinear cloud workloads;nevertheless, they have been unable to generalize inter cluster workload forecasting due to inadequate workload data at the beginning of each cluster. Furthermore, the distribution disparity across distinct cluster workloads is caused by a variety of elements, making it difficult to reuse current data or models directly. To overcome these challenges, we propose ADAPT, which relies on Attention-Driven Domain Adaptation. First, we use LSTM architecture as the backbone of our model. Moreover, we construct a strategically shared attention module to transmit relevant knowledge from the source domain to the target domain by inducing domain-invariant latent features and retraining domain-specific features. Lastly, adversarial training is used to increase the model’s resilience and predictive accuracy. Comprehensive experimental evaluations indicate that our proposed approach significantly outperforms existing baselines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud platforms

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BIVFN: Blockchain-Enabled Intelligent Vehicular Fog Networks

BIVFN: Blockchain-Enabled Intelligent Vehicular Fog Networks

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International Conference on Data Analytics, Intelligent Computing, and Cyber Security, ICDIC 2020

作者： Gaba, Priyanka Raw, Ram Shringar Department of Computer Science and Engineering School of Information Communication and Technology Guru Gobind Singh Indraprastha University Dwarka New Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology East Campus Delhi India

ISBN: (纸本)9789811941610

Vehicular ad-hoc network (VANET), a network that facilitates inter-vehicle communication regarding road safety or entertainment. The popularity of VANET is because of its increasing demand and its blend with the latest technologies like cloud, IoT, AI, and ML. The expansion in vehicles on the road and their communication leads to the need to use fog rather than cloud to provide low latency to a real-time system VANET. The fog computing together with VANET is termed as vehicular fog network although providing various advantages, but on the other side compromises the network’s security. To improve the security and privacy of the VANET system, blockchain an immutable, peer-to-peer, decentralized, and distributed ledger-based technology seems suitable for making VFN a safer and more reliable system. The combination of fog computing and blockchain technology with VANET is termed as blockchain-enabled intelligent vehicular fog network (BIVFN). This paper discusses the VFN also the architecture, and phases of BIVFN are explored in detail. BIVFN can attain the security and privacy requirements well because of blockchain and fog computing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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Solar Panel Power Prediction Using Machine Learning Technique

Solar Panel Power Prediction Using Machine Learning Techniqu...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Sekar, S. Kaviya, V. Vivekanandan, G. Nithya, P. Srm Valliammai Engineering College Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798350394580

One of the most significant renewable energy sources produced by photovoltaic panels (PV) is solar energy. Only during the day is solar energy available, and it is affected by meteorological elements such sun irradiation, cloud coverage, rainfall, and temperature. Due to these variables, the integration of solar power into the power grid is more challenging than it is for conventional energy sources. Changes in the solar power output result in increased electricity demand and higher operating costs. This leads to predict the power produced by solar PV panels accurately. There are numerous methods for estimating the power produced by PV systems, and the data sources used are either just historical data on PV power or historical data on both PV power and weather. In proposed system the data sources used are previous PV power output, weather data and weather forecasts in order to correctly get the effects of solar panel power prediction and actual operating condition data on the efficiency of power generation by using Support vector machine learning algorithm. The model has been trained and validated using previous year data from a input as year, month, date, time, temperature, humidity, direct radiation, diffuse radiation, direct normal irradiance, wind speed and outputs the power generated which gives higher prediction accuracy. © 2023 IEEE.

关键词： accuracy solar panel support vector machine

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Designing Solutions for High-Performance Communications Software Design in Network Applications

Designing Solutions for High-Performance Communications Soft...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Agarwal, Puneet Bhuvana, J. Sharma, Bhuvnesh College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Mechanical Engineering Jaipur India

ISBN: (纸本)9798350383348

Designing software program answers for high-performance communications in community packages is complex. It calls for cautious attention to the underlying community hardware, protocols, and algorithms. The goal is to create a communique machine with minimal latency and go-platform compatibility while maintaining robust safety and imparting excessive throughput. Designing high-overall performance conversation software should remember the wishes of the software. For example, an internet server wishes to acquire and respond to many requests concurrently;thus, the community should efficiently present more than one simultaneous connection. Then again, a video streaming application will require the network to handle heavy visitors without experiencing unexpected delays or packet loss. The demanding situations of designing a communications software device are further exacerbated using the complexity of today's networks. Exclusive protocols ever require unique optimizations and adjustments to maximize overall performance. Community topology and the environment, including the nature of the relationship kind, must also be considered. Similarly, selecting protocols is a crucial issue, as each has its benefits and barriers. To navigate these complexities efficaciously, software program developers must have widespread enjoyment and profound know-how of each protocol and community environment. They must additionally apprehend the interaction among the additives of the network;the development of excessive-overall performance conversation software layout for network applications is a challenging mission for software program engineers and builders. Despite challenges with variable community situations, stop-consumer requirements, and a wide range of gadgets, reliable and excessive overall performance software must be designed for these networks. Designing for excessive-performance communications software entails the attention and assessment of numerous factors, expertise, and lookin

关键词： algorithm communications designed designs minimal

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End-to-End Mobility Management in Underwater High Speed Communication Networks 3

End-to-End Mobility Management in Underwater High Speed Comm...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Geetha Pranali, S. Kaneria, Ojasvee Singla, Abhishek Prasad, Koushik V. Das, Neeraj Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Underwater high pace communique networks have emerge as increasingly critical for an expansion of packages which includes underwater exploration, oil and gas industry, protection, and environmental monitoring. Those networks require reliable and green mobility control strategies to guide the motion of underwater cars and sensors in harsh aquatic environments. The give up-to-end Mobility control in underwater excessive pace verbal exchange Networks targets to provide a comprehensive solution for managing the mobility of nodes in those networks. This answer entails the coordination of diverse layers in the community architecture which includes the bodily, records link, and network layers. at the physical layer, the solution utilizes advanced acoustic conversation techniques which includes beamforming and channel equalization to mitigate the outcomes of multipath fading, Doppler shifts, and sign attenuation. This allows the nodes to keep a dependable communication link even when moving at high speeds. at the records hyperlink layer, the answer carries strategies for seamless handover between extraordinary forms of underwater communique hyperlinks, inclusive of acoustic, optical, and hybrid links. This permits the nodes to exchange among links primarily based on their vicinity and communication requirements, with none disruption to the continuing communication session. © 2023 IEEE.

关键词： Community Equalization Layers Physical Requirements

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

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25th International Arab Conference on information technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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Analyzing the Potential Benefits of Data Mining for Customer Relationship Management 1

Analyzing the Potential Benefits of Data Mining for Customer...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Aravind, A.R. Katikar, Shilpa M. Acharjya, Kalyan Ezhilarasan, G. Yadav, Surendra Mishra, Abhinav Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350359756

Data mining for client dating control (CRM) is figuring out patterns and traits in customer records to inform selections about product imparting, pricing, sales, advertising and marketing, and different consumer techniques. Companies can use automated records mining strategies to outline client profiles, section clients, and better recognize purchasing conduct. Through knowledge of client conduct, corporations can target their customers greater correctly, grow revenue, and enhance customer satisfaction and loyalty. The advantages of fact mining for CRM are several, which include spotting purchaser needs and options, improving marketing effectiveness, increasing client acquisition, and boosting sales. Additionally, records mining can enable an employer to perceive lost sales possibilities, lessen operational costs, and optimize customer retention efforts. Essential data mining allows CRM-targeted organizations to recognize clients higher, optimize operations, and broaden strategically centered initiatives to improve consumer loyalty. © 2023 IEEE.

关键词： Data mining

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