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Applying Multi-Layer Perceptrons (mlP) for Demand Forecasting in Supply Chain Management 2

Applying Multi-Layer Perceptrons (MLP) for Demand Forecastin...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Ankam, Sreejyothsna Durgadevi, G. Kumar, Rakesh Mounika, Bathula Alsalami, Zaid Niranjanamurthy, M. GRIET Bachupally Department of CSBS Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India CMR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq BMS Institute of Technology and Management Department of AI and ML Karnataka Bangalore560064 India

ISBN: (纸本)9798350368109

Demand forecasting plays a significant role in supply chain management and business operational effectiveness. This paper focuses on the use of Multi-Layer Perceptrons (mlP) in demand forecasting which is one of artificial neural networks due to its proved ability to model non-linear patterns. This typology suggests a detailed approach which embraces data gathering, data preparation, model building and model assessment. The mlP model is compared with other models including ARIMA, Random Forest and LSTM models. Therefore, the use of mlP model indicated improved performance in terms of RMSE and MAPE in comparison to traditional approaches. Although LSTM has slightly higher performance, it is relatively more simpler and faster to implement mlP which results to better accuracy and fewer computations. It is identified that historical sales data, seasonal factors and economic factors are some of the most important features that affect the accuracy of the forecast. The use of mlP during the implementation of the project proves the efficiency of the proposed approach and the obtained results. This paper reveals the possibility of mlPs in improving the part of demand forecasting in the supply chain management process and suggested ways that could increase opportunities for better supply chain management through the management of inventory. Further research can be dedicated to the elaboration of the combined approaches utilizing the advantages of the notable methodologies for sales forecasting.. © 2024 IEEE.

关键词： Demand response

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Revolutionizing Healthcare with Wearable Devices and IoT Integration for Personalized Health Management in Next-Generation Health Informatics

Revolutionizing Healthcare with Wearable Devices and IoT Int...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Parthipan, V. Ramesh, P.S. Gururama Senthilvel, P. Dineshkumar, R. Vennila, G. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India

ISBN: (纸本)9798350388602

This work outlines a paradigm shift that is created by the integration of wearable devices with the Internet of Things for health consciousness. With the help of the proposed system, the user is given the constant readings of the key heath indicators including heart rate, blood pressure, blood glucose level, physical activity, and the quality of sleep, all the data are transferred in real-time to an IoT platform, and the data analysis supported by machine learning algorithms provide the user with individually tailored heath reports. Wearable Medical today carried out a 90 persons trial over a six-month long program and noted changes in health parameters, user interactions and results. Several client outcomes highlighted that clients' heart rate, blood pressure and glucose levels, physical activity, and sleep quality improved. The system also provided strong control in data protection and the compliance with different regulations which is needed for any company. Although there are issues with device and network integration and the supporting architecture, the study demonstrates the system's capability of decreasing hospital attendance, increasing medication compliance and quality of life, thereby opening up possibilities for exploring wearable health technologies in clinical environments in the future. © 2024 IEEE.

关键词： Electronic health record

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Deployment Of Machine Learning Model to Evaluate the Quality of Green Spacing

Deployment Of Machine Learning Model to Evaluate the Quality...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： M Vineela V Rama Krishna Bhaludra R Nadh Singh G. Divya Vani N. Sudha Laxmaiah Cherukupally Saritha Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Vinay Nagar Hyderabad India Department of CSE-AI&ML(CSM) Bhoj Reddy Engineering College for Women Vinay Nagar Hyderabad India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Green space is any vegetated infrastructure. Green space promotes mental and physical wellness, socialization, and environmental protection. The state of green space determines its quality. Based on machine learning research, trash, lack of upkeep, and dirtiness lower green space quality. These algorithms evaluate green areas without considering human perception. Domain-based approaches are laborious, tedious and difficult to implement across large areas. This study suggests applying transfer learning on pre-trained models to design, analyze, and implement a machine-learning technique for human-perception green space quality assessment. Precision, F1-score, accuracy, recollection, Cohen’s Kappa, and Regular ROC-AUC were high for the generated models. In addition, the models’ file sizes and inference times were analyzed to guarantee their use in real-world settings. Grad-CAM, which uses heat maps to assess a model’s learning performance, was also included in the study. ResNet50 was the top-performing model, with 99.99 per cent accuracy, 99.99 per cent precision, 99.99 per cent recall, 99.00 per cent F1-score,0.98 Cohen’s Kappa, and an Average ROC-AUC of 1.00. The ResNet50 model was the second-fastest predictor and had a reasonable file size. Grad-CAM imaginings reveal that ResNet50 can accurately pinpoint regions crucial to its learning. Finally, an interactive online application based on the ResNet50 model was released via the Stream lit cloud platform.

关键词： Training Analytical models Visualization Green products Transfer learning Streaming media Security

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Enhancing Human-Robot Interaction through Natural Language Processing and Speech Recognition Techniques

Enhancing Human-Robot Interaction through Natural Language P...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Thotakura Veeranna K. Pavan Kumar Mahadeva Aruna Amit Verma Thanu Kurian Department of CSE(AI&ML) Sai Spurthi Institute of Technology B. Gangaram India Department of Electronics & Communication Engineering (ECE) K.S.R.M College of Engineering(Autonomous) Kadapa India Computer Science and Engineering Sai Spurthi Institute of Technology B. Gangaram India Department of Computer Science and Engineering Dr. K N Modi University Newai Rajasthan India Department of AI &ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The rapid development of artificial intelligence (ai) has resulted in the gradual integration of robots into individuals' everyday lives and professional environments. The use of image recognition employing the robot's human-machine interface technology has become widespread. However, there are still some unresolved problems with the methods used for interaction between robots and machines, such as natural language processing (NLP) and speech recognition. Thus, building on previous research in the field, this work suggests a novel approach to robot-machine interaction by integrating the perceptron lexicon analysis model with the transfer dependence syntactic analysis model. To enhance interaction performance, researchers need to delve further into language-based human-machine interaction systems. An experimental evaluation of the structural perceptron model reveals a recall rate of 82%, an F1 value of 83%, and a testing accuracy of 96%. A maximum analysis of data speed of 751K/s is achieved using the data transfer dependence syntax analysis model. With superior robustness and reaction sensitivity, the novel technique for robot-machine interaction achieves a 93% accuracy rate in comparison to previous methods that have been tested in simulations. To conclude, research methodologies provide the foundation, both theoretically and practically, for better human-machine cooperation and enhanced robot interaction skills)

关键词： Human computer interaction Analytical models Adaptation models Accuracy Human-machine systems Speech recognition Syntactics Robot sensing systems Natural language processing Testing

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Advancements in Brain Disease Diagnosis for Comparing Traditional Methods and Deep Learning Approaches in Medical Imaging 1

Advancements in Brain Disease Diagnosis for Comparing Tradit...

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1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Kumari, Sunita Singh, Laxman Pasha, Moghal Irfan Kumaraswamy, M. Choudhary, Amar Agarwal, Monika Department Computer Science and Engineering ABES Institute of Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE-AI&ML CVR College of Engineering Hyderabad India Department of CST Sasi Institute of Technology and Engineering Tadepalligudem India Alliance University Bengaluru India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (纸本)9798350391268

This work explores improvements in the diagnosis of brain disease through traditional vs deep learning base techniques with respect to Medical Imaging. The dataset containing 3,000 images of brain tumors was preprocessed to resize (224x224), normalize and augmentation techniques like rotation, flipping were further implemented. The dataset are splitted into70% training and 30%. testing across four deep learning models, Convolutional Neural Networks (CNN), VGG16. The models were trained to classify brain tumor images into more or less complex categories. After training VGG19 gave top accuracy of 98.6 per cent, followed byVVG16 at second place with an accuracy rate of 96.75%, while the ResNet50 and CNN were rated third and fourth in terms of their accuracies;as neither performed well with just92.3%and89.3%. VGG19 showed the best precision (98.8%), recall(98.5%) and F1-score(98.65) among metrics of all models utilised in this research(which ever they might be). VGG19 had low false positives and negatives which are important for accurate diagnosis as can be seen in the confusion matrices. The type of research exemplifies how deep learning can improve the accuracy and certainty within medical imaging diagnostics, particularly in difficult tasks such as brain tumor classification. This study highlights the strong possibilities deep learning could lead to transformative improvements in clinical neuroscience by employing advanced neural network architectures and meticulous preprocessing strategies. © 2024 IEEE.

关键词： Neuroimaging

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A Hybrid Deep Learning Framework for Efficient Network Intrusion Detection Systems using Data Augmentation

A Hybrid Deep Learning Framework for Efficient Network Intru...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Priya Shirley Muller K Madhavi Naga Durga Saile K B Prasanthi B Jayaram Goski Sathish Department of Mathematics Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Mallareddy Engineering College (Autonomous) Hyderabad India Department of Computer Science and Engineering (AIML& IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Departmentt of Computer Science (AI&ML) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering RMK Engineering College Gummidipoondi India Department of Information Technology St.Martins Engineering College (Autonomous) Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Because of their critical position as the initial barrier against harmful actions, intrusion detection systems (IDSs) rely substantially on the efficacy of cybersecurity in protecting corporate communication. There are several limits to machine learning approaches that be solved by utilizing other deep learning architectures, even though they are widely used for intrusion detection. In addition, unbalanced datasets frequently make it difficult to evaluate the suggested models, which prevents a thorough evaluation of the models' effectiveness. This study seeks to tackle these difficulties by improving the performance of numerous deep-learning(DL) structures for IDS using data augmentation approaches. The experimental results shown that even a basic CNN-based architecture may produce very efficient network-attack-detection, but more complicated designs only managed to show slight performance increases. The results show that cybersecurity frameworks can easily incorporate the suggested deep learning-based intrusion detection approaches, which improves the capacity to identify and counteract complex network threats. The outcomes of this research exhibit that the accuracy of intrusion detection models is highly dependent on the dataset's quality and quantity, with augmented CIC-IDS-2017 showing an accuracy of up to 92%.

关键词： Deep learning Accuracy Communication systems Network intrusion detection Complex networks Data augmentation Integrated circuit modeling computer crime

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Federated Learning for Residential Load and PV Production Forecast 13

Federated Learning for Residential Load and PV Production Fo...

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13th IEEE Power and Energy Society: Innovative Smart Grid Technologies - Asia, ISGT Asia 2024

作者： Naladkar, Gargi Yadav, Amit Gugaliya, Jinendra Indian Institute of Technology Kanpur Computer Science & Engineering Department Kanpur India Enphase Solar Energy Pvt.Ltd Sr. Engineer Data Science Cto India Bangalore India Enphase Solar Energy Pvt.Ltd AI/ML Scientist - Cto Office Bangalore India

ISBN: (纸本)9798350373929

This paper explores the application of federated learning in predicting energy consumption and solar production for households equipped with Enphase smart home solar systems. Traditional centralized model training for energy prediction entails significant communication overhead, particularly when dealing with distributed data sources. To mitigate this challenge, federated learning is proposed as a privacy-preserving alternative, wherein each client trains a local model on its data and transmits model updates to a central server for aggregation. We propose a neural network-based energy demand predictor trained with federated learning, leveraging distributed data while maintaining prediction accuracies. Federated learning ensures data security and confidentiality by transmitting only model updates, preserving individual privacy. The datasets used are anonymized, and the actual data cannot be extracted from the shared gradients. Through experiments, we demonstrate the feasibility and effectiveness of federated learning in energy forecasting, highlighting its potential to enhance prediction capabilities without compromising privacy. © 2024 IEEE.

关键词： Federated learning

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A Comprehensive Examination of Consensus Algorithms and Their Impact on Blockchain Scalability

A Comprehensive Examination of Consensus Algorithms and Thei...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： M Veera Brahmam Y Laxmi Prasanna V T Ram Pavan Kumar M Kanneboina Ashok K V S S R Murthy S Sindhura Computer Science & Engineering (Data Science) Bapatla Engineering College Bapatla India Computer Science & Engineering (AI&ML) Anurag Engineering College Ananthagiri Suryapet India Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India School of Engineering Mallareddy University Hyderabad Telangana India Computer Science & Engineering SRKR Engineering College(A) Bhimavaram India Computer Science & Engineering NRI Institute of Technology Agiripalli Vijayawada India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

This paper goes one step ahead into a suggested testbed design with which empirical analysis can be carried out. Deep, extended consensus methods and their relative impact on blockchain scalability are analyzed. Our experimental configuration is based on two mining nodes and a master node, and to be precise, it is a blockchain network consisting of five physical nodes arranged in such a way so as to use Proof of Work (PoW) and Round Robin as separate consensus procedures. Architecture even ensures safe communication through Java-based remote clients running RPC commands with a JSON base. The performance of each consensus algorithm was tested using a series of scalability tests in order to find the transaction throughput and execution times under a different transaction load. According to the results shown, as the transactions are transmitted in bigger quantities, Round Robin outperforms PoW by way of the number of transactions processing and throughput time. In particular, it presented a comparative advantage relative to Proof of Work in terms of efficiency by processing transactions much faster and maintaining higher throughput levels. Overall, these results highlight the significance of properly selecting the consensus algorithm in the quest for blockchain performance optimization, especially under scenarios where fast transaction processing is paramount. In a nutshell, this study will conclude with the trade-off between efficiency and decentralization of blockchains within applications and recommend Round Robin as a feasible solution if speed in transaction matters.

关键词： Technological innovation Scalability Consensus algorithm computer architecture Throughput Proof of Work Blockchains Round robin Optimization

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A Transfer Learning Based Web App for Glaucoma Detection Using Low-Cost Ophthalmoscopic Camera

A Transfer Learning Based Web App for Glaucoma Detection Usi...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： B K Chethan Sanjana Vishwanath Sambargi D Roopashree K Prabhavathi Kiran Puttegowda D S Sunil Kumar Department of Computer Science Engineering (AI&ML) Vidyavardhaka College of Engineering Mysuru India Department of AI and ML Coorg Institute Of Technology Ponnampet Karnataka India Department of ECE Government Engineering College Hassan Karnataka India Department of ECE BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of ECE Vidyavardhaka College of Engineering Mysuru Karnataka India Anatta innovations BGS B-School Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Glaucoma has been the most fundamental cause of irreversible blindness in persons over a few decades. This condition is extremely prevalent in several nations, and in the worst-case situation is that many countries do not have enough ophthalmologists. According to data from global glaucoma research, roughly 12 million individuals would be symbiotically blind from early-stage glaucoma by 2020. To evaluate the disease in the eye, fundus photography is used. Fundus photos require a lengthy visual inspection. The transfer-learning based web-app for glaucoma detection utilizing low-cost ophthalmoscopic cameras is presented in this paper. Fundus evaluation is an important part of an eye exam that helps the web-app provide vital diagnostic information to both ophthalmologists and non-ophthalmologists. Vision impairment occurs over time because it is not a rapid consequence. Only in the later stages those signs are noticed. The optic nerve was studied using 2-Dimensional retinal fundus pictures, which is a difficult task that can result in a significant cost for detecting regular glaucoma. There are a variety of different tests that can be used to rule out glaucoma, including tonometry which signifies pressure on eye, gonioscopy which examines angle is closed or open, OCT - Optical Coherence Tomography, Ophthalmoscopic Imaging to visualize retina as well as optic nerve and Fundus Copying. In this paper, an effective embedded based eye testing architecture is presented.

关键词： Glaucoma Performance evaluation Biomedical optical imaging Transfer learning Medical services Cameras Retina Optical imaging Adaptive optics Servers

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Energy Harvesting in Cognitive Radio Networks using Local Search based Dingo Optimization Algorithm

Energy Harvesting in Cognitive Radio Networks using Local Se...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Sripelli Jagadish Ramy Riad Al-Fatlawy Ankam Sreejyothsna Syed Salim Sowmya M School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally India Department of Computer Science and Design School of Engineering Mysore University Manasa Gangothiri Mysuru India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cognitive Radio Networks (CRNs) have been introduced as an empowering advancement which enables the dynamically unutilized spectrum to enhance the spectral effectiveness. Energy efficiency and the throughput are the apprehensions for the energy-harvesting CRN. Nevertheless, obtaining a greatest maximum level of these metrices needs the optimization of sensing duration, transmission time and the energy harvesting. To tackle these problems, this articles purposes to propose the Local Search based Dingo Optimization Algorithm (LS-DOA) approach for energy harvesting in CRN. The LS-DOA enhances an energy harvesting procedure through balancing an energy collected and obtained. This flexibility results in effective energy management as well as make sures that energy is significantly harvested from the obtainable ambient playforms. LS-DOA is robust in dynamic environments, regulating to changeable spectrum obtainability and different energy harvesting environments. This designs it optimal for CRNs, where network conditions are highly variable. The multi-objective functions such as energy efficiency and throughput are considered to estimate the model effectiveness. The experimental results shows that the proposed LS-DOA approach attains the average throughput and energy efficiency of 0.241 and 2.938 as compared to the existing approaches like DOA and Pelican Optimization Algorithm (POA).

关键词： Wireless networks Heuristic algorithms Metaheuristics Throughput Search problems Energy efficiency Real-time systems Sensors Energy harvesting Cognitive radio

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