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International Symposium on Advanced Computing and Communication (ISACC)

作者： Ahana Mukherjee Bipasha Chatterjee Indrakshi Das Koushiki Chakraborty Madhushree Pramanik Anjan Kumar Payra Computer Science (Artificial Intelligence & Machine Learning) Institute of Engineering and Management Newtown Kolkata India Computer Science (Artificial Intelligence & Machine Learning) Institute of Engineering and Management Newtown Institute of Engineering and Management Newtown Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research was devised to analyze how machine learning tools could assess water quality and classify waste aiming to build a better system for sustaining our environment. We unified decision trees, random forests, and convolutional neural networks to create a multi-model system that identifies and sorts waste via image recognition. To strengthen our model's performance, we expanded our dataset to include sundry labelled images. Using a multilayer perceptron with Adam optimizer, our system achieved 89% accuracy in classifying waste types and 94% accuracy in identifying waste materials. Water potability analysis, however, relied on machine learning models that were trained on a comprehensive dataset of chemical and physical water parameters. The system was able to show an accuracy of 87% in predicting water safety with a low false positive rate of 3.2% when real time monitoring was integrated. These systems when integrated gave a solution that was better than traditional methods by 47% and the processing time was reduced by 62%. Our results showed that machine learning can effectively enhance environmental management systems. This scalable, technology-driven approach proved successful for both waste and water management, making it particularly valuable for regions struggling with these challenges, regardless of their development status.

关键词： Waste management Waste materials Accuracy Image recognition Biological system modeling Water quality Real-time systems Decision trees Convolutional neural networks Random forests

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Hybrid machine learning for Personalized Ayurvedic Drug Recommendation via Prakriti Assessment

Hybrid Machine Learning for Personalized Ayurvedic Drug Reco...

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Women in Computing (InCoWoCo), International Conference for

作者： Jimsha K Mathew J Maruthu Pandi V Harish Kumar Vadagam V Sri Vishnu Charan Bindu S M Bhoomika H Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Ayurveda, an ancient system of medicine, emphasizes personalized treatment based on individual body constitutions, known as Prakriti. In this paper, we present a hybrid machine learning approach for recommending Ayurvedic drugs, integrating traditional Prakriti assessment with modern symptom-based analysis. Using clustering and classification algorithms, the system analyzes patient symptoms and body constitution to generate personalized drug recommendations. This method avoids the computational complexity of deep learning, providing an interpretable, efficient, and scalable solution. Experimental results demonstrate the system's accuracy in identifying Dosha imbalances and its effectiveness in recommending appropriate treatments.

关键词： Drugs Deep learning Accuracy Medical services Real-time systems Data models IEEE Constitution System analysis and design Engines Guidelines

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Proactive Alzheimer's Disease Identification Through EEG-Based Biomarkers 1

Proactive Alzheimer's Disease Identification Through EEG-Bas...

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1st International Conference on artificial intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Bhendare, Gaurav Dewangan, Leelkanth Datta Meghe Institute of Higher Education & Research Artificial Intelligence and Machine Learning Faculty of Engineering &technology Maharashtra Wardha India Datta Meghe Institute of Higher Education & Research Artificial Intelligence and Data Science Faculty of Engineering &technology Maharashtra Wardha India

ISBN: (纸本)9798350330915

To detection of Alzheimer's disease (AD) therapy is crucial for patients. Because it enables patients to take prevention actions before brain damage takes place. Although inherited effects limit most computer detection methods, computers have been employed in numerous research to identify AD The 'Electroencephalogram (EEG)' signal is used in this study to implement a deep learning detecting of Alzheimer's disease (AD). Initially, conventional internet databases are used to get the necessary EEG signal and there after used the The signal is divided into many current Neural Networks using the 'Lifting Wavelet Transform (LWT)' decomposition level. Extracts the temporal properties from the signal that has been divided up. © 2023 IEEE.

关键词： AD (Alzheimer's disease) LWT (Lifting Wavelet Transform) EEG (Electroencephalogram)

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Automated Text Extraction and Classification from Images using Multi-Layer Perceptron(MLP)

Automated Text Extraction and Classification from Images usi...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Madhumohan, A.J.V. Raghu, Hemanth Priyanka, R. Dsouza, Sonia Maria Teena, K.B. Shetty, Shravya B.M.S. College of Engineering Department of Machine Learning Bengaluru India Cambridge Institute of Technology Department of Ise Bengaluru India New Horizon College of Engineering Department of Ai & Ml Bengaluru India East Point College of Engineering and Technology Bengaluru India College of Engineering Artificial Intelligence and Machine Learning New Horizon Bengaluru India

ISBN: (纸本)9798350359688

This research paper presents a comprehensive approach for extracting and classifying text from images using computer vision and deep learning techniques. We demonstrate a step-by-step process of preprocessing, text extraction, and classification with a Multi-Layer Perceptron(MLP). The proposed method is evaluated on a dataset of player images having different kill counts and shows promising results using bounding box algorithm in automated text extraction and classification. © 2024 IEEE.

关键词： Image classification

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Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment 39

Asymmetric Visual Semantic Embedding Framework for Efficient...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Liu, Yang Liu, Mengyuan Huang, Shudong Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu China State Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School Shenzhen China

ISBN: (纸本)157735897X

learning visual semantic similarity is a critical challenge in bridging the gap between images and texts. However, there exist inherent variations between vision and language data, such as information density, i.e., images can contain textual information from multiple different views, which makes it difficult to compute the similarity between these two modalities accurately and efficiently. In this paper, we propose a novel framework called Asymmetric Visual Semantic Embedding (AVSE) to dynamically select features from various regions of images tailored to different textual inputs for similarity calculation. To capture information from different views in the image, we design a radial bias sampling module to sample image patches and obtain image features from various views, Furthermore, AVSE introduces a novel module for efficient computation of visual semantic similarity between asymmetric image and text embeddings. Central to this module is the presumption of foundational semantic units within the embeddings, denoted as "meta-semantic embeddings." It segments all embeddings into meta-semantic embeddings with the same dimension and calculates visual semantic similarity by finding the optimal match of meta-semantic embeddings of two modalities. Our proposed AVSE model is extensively evaluated on the large-scale MS-COCO and Flickr30K datasets, demonstrating its superiority over recent state-of-the-art methods. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

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Bird Species Classification and Prediction Using machine learning Algorithms 7

Bird Species Classification and Prediction Using Machine Lea...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Abhinav, Penchala Dhavale, Sunitha Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501286 India Defence Institute of Advanced Technology School of Computer Engg. & Mathematical Sciences Pune411025 India

ISBN: (纸本)9798331527822

The BirdClef-2024 dataset, which includes acoustic recordings of 182 Western Ghats species, was used to classify and predict bird species in this study. Three machine learning algorithms were analyzed for their accuracy, precision, recall, and F1 score - K Nearest Neighbors (KNN), Random Forest, and Support Vector machine (SVM). The Random Forest model outperformed the other models in all metrics. The paper contains sections on dataset composition, preprocessing steps, algorithm implementations as well comparisons of the model's performances. Such issues have been successfully addressed through advanced pre-processing techniques coupled with complex machine-learning algorithms despite the intricate nature of the sound signals. A Streamlit GUI that is user-friendly has been developed to facilitate real-time recognition of birds by breed;this can contribute to surveillance for environmental biodiversity conservation efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Travel Itineraries: A Multi-Objective machine learning Model Approach 3

Optimizing Travel Itineraries: A Multi-Objective Machine Lea...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Roy, X Samuel Issac, Titus Terrance, J Sebastian Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

A travel itinerary is a complex problem that involves multiple objectives and constraints, such as cost, time, transportation modes, and comfort levels. This research study focuses on creating a real-time eco-friendly itinerary generator by integrating machine learning techniques. This research work explores two machine-learning algorithms: artificial Neural Networks (ANNs) and Multi-Layer Perceptron (MLP). These algorithms are evaluated for their suitability in generating user-friendly and environment-friendly itineraries. Through experimentation, the accuracy and precision of the proposed models are assessed, leading to the identification of an optimal travel planner model. The ultimate goal of this model is to provide a detailed itinerary that considers prominent modes of transportation while addressing the pressing environmental crisis. By prioritizing eco-friendly options, this approach sets itself apart from conventional travel planning tools. © 2024 IEEE.

关键词： Multilayer neural networks

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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

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Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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Simulation and Efficiency Comparison of MOS Solar Cells Using Ag/TiO2 and Au/TiO2 as Back Reflector/Protective Sandwich Layers 9

Simulation and Efficiency Comparison of MOS Solar Cells Usin...

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9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Maram, Manodhar Perugu, Sisindri Cyril Robinson Azariah, J. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331505042

Current research aims to compare the performance of Ag/TiO2 and Au/TiO2 as back and front reflector layers in novel photovoltaic (PV) cells using simulations. The study investigated a range of panel thicknesses, from 80 nm to 920 nm. Both back and front reflector layers in the novel metal-oxide-semiconductor (MOS) solar cell (MOSSC) were fabricated with either Ag/TiO2 or Au/TiO2, respectively. A total of 42 samples were simulated across two groups using the Nanohub simulation tool. The performance of both novel solar cell devices was evaluated using the Nanohub online simulation tool. Simulated results indicated a statistically significant difference (p2-based MOSSC exhibited a higher efficiency of 19.75% compared to the 13.30% efficiency of the Au/TiO2-based MOSSC. In conclusion, the study suggests that Ag/TiO2-based MOSSCs achieve significantly better performance than Au/TiO2-based counterparts, with an efficiency of 19.75%. © 2024 IEEE.

关键词： Layered semiconductors

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Multimodal multi-objective differential evolution algorithm based on spectral clustering

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International Journal of Innovative Computing and Applications 2022年第5-6期13卷 303-313页

作者： Wang, Shenwen Chu, Xiaokai Zhang, Jiaxing Gao, Na Zhou, Yao School of Information Engineering Hebei GEO University Hebei Shijiazhuang050031 China Laboratory of Artificial Intelligence and Machine Learning Hebei GEO University Hebei Shijiazhuang050031 China

In recent years, in the face of the same problem in industrial production and life, decision-makers often hope to have a variety of different solutions to deal with. In other words, we hope to locate more different Pareto solutions under the condition of finding Pareto front. However, there are few researches in this field. For this reason, we propose a multimodal multi-objective differential evolution algorithm based on spectral clustering (SC-MMODE), which mainly uses some mechanisms to divide the solutions in the decision space into several mutually independent subpopulations. First, SC-MMODE uses a spectral clustering algorithm to control the decision space and form multiple sub-populations with good neighbourhood relations. Secondly, a special crowding distance mechanism is used to balance the distribution of solutions in the decision space and objective space. In addition, the classical differential evolution algorithm can effectively prevent premature convergence. Then, in 17 test problems, the SC-MMODE algorithm and some new multimode multi-objective algorithms are tested simultaneously. Finally, through the analysis of experimental data, the SC-MMODE algorithm can find more Pareto optimal sets in the decision space, so it can effectively solve such problems. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Multiobjective optimization

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