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

作者： Tanjim Taharat Aurpa Samiha Maisha Jeba Khaza Minhaz Bin Monib Maherun Nessa Ela Md Shoaib Ahmed Md Musfique Anwar Dept. of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Boise State University Boise Idaho USA Dept. of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s fast-paced world, where technology is advancing rapidly, the tragic loss of lives due to fire accidents is not only heart-wrenching but also poses a significant risk to public safety. Early prediction of such risks can play a crucial role in educating people about fire hazards, enabling them to be better prepared in case they find themselves in such unfortunate circumstances. Our study employed various machine learning (ML) techniques and evaluated their effectiveness by analyzing different factors. These factors are derived from the diverse living conditions in Bangladesh and were gathered through surveys. To refine and identify the critical features for classification, we utilized feature selection methods like ANOVA-f and the chi-square test. Our approaches have achieved a success rate of up to 97.98%.

关键词： Surveys Accuracy Feature extraction Hazards Encoding Vectors Public security Bayes methods Analysis of variance Random forests

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Crafting Connections: Empowering Kondapalli Artisans through a Mobile Application

Crafting Connections: Empowering Kondapalli Artisans through...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Chinta Vara Prasad J V D Prasad Vallakati Navyasri Dept. of Artificial Intelligence and Data Science V R Siddhartha Engineering College Vijayawada India Dept. of Computer Science and Engineering VR Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Handicrafts hold historical significance in representing the essence of our nation's native culture. However, these handcrafted goods still need to be explored globally, and the manufacturers frequently need help connecting directly with larger markets and potential customers. The scope of this project is bound to Kondapalli Toys, also known as Kondapalli Bommalu, renowned wooden handicrafts originating from Andhra Pradesh, India. The current resources for resolving this issue are not easily available on the market, and there is a lack of a user-friendly application. A dedicated Android application is needed to strengthen the handicraft business. This application enables artisans and potential customers to communicate effortlessly, guaranteeing secure transactions, offering valuable data insights, and providing resources for skill and capacity development. The user interface for this application was developed using the Flutter framework, which prioritizes data security and is scalable and region-adaptive. Through this application, manufacturers can conveniently reach markets while customers have the opportunity to explore and connect with their businesses, allowing them to display their craft items to a global audience.

关键词： Technological innovation Sentiment analysis Toy manufacturing industry Globalization User interfaces Real-time systems Mobile applications Registers Monitoring Business

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From data to Harvest through Machine Learning based Crop Yield Forecasting

From Data to Harvest through Machine Learning based Crop Yie...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Aadithya Vikram Budarapu John Hyde Gaddam Ramu Boyedi Thoshan Kumer Muthyala Nikhila Kathirisetty Dept. of Information Technology Vardhaman College of Engineering (Affiliated by JNTUH) Hyderabad India Dept. of Computer Science and Data Science Vardhaman College of Engineerin (Affiliated by JNTUH) Hyderabad India

The maintenance and enhancement of dynamic soil characteristics are the primary focus of soil management in agriculture to increase crop productivity. Higher productivity may result from efficient soil control of resources and corrective micronutrient treatments. Using CNN and ’KNN’ algorithms, the “soil land classification and crop prediction system” application was created. In this study, two datasets are used: one to obtain crop prediction and the other for soil land categorization. The kind of soils are trained using CNN (“VGG- 19”) algorithm, and the accuracy of the model is calculated. The trained model is then utilized in the Flask web app to forecast the type of soil. Another data set with nitrogen, phosphorus, potassium, pH, and temperature as features and the class type of crop as a label is used to forecast crop production. These two algorithms are used to create the flask website, which accepts inputs such as soil picture, soil type prediction, and land parameter inputs for crop prediction.

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimize...

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International Conference on computer and Applications (ICCA)

作者： Salah A. Aly Ali Bakhiet Mazen Balat Faculty of Computing & Data Science Badya University Giza Egypt Computer Science Dept. Faculty of Science Fayoum University Fayoum Egypt Computer Science Dept. Culture & Science October 6th City Giza Egypt CS & IT Dept. E-Japanese University of Science & Tech. Alexandria Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology.

关键词： Deep learning Accuracy Biological system modeling Neural networks Transfer learning Leukemia Oncology Robustness Optimization Testing

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AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT Systems

AI-Driven Energy Optimization: Household Power Consumption P...

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2024 International Conference on Microelectronics, ICM 2024

作者： Kolluru, Vinoth Kumar Challagundla, Yagnesh Chintakunta, Advaitha Naidu Roy, Bappadittya Bermak, Amine Renuka Devi, S.M. Stevens Institute of Technology Department of Data Science HobokenNJ United States University of Florida Engineering Education Dept. United States University of North Carolina Department of Computer Science and Engineering Charlotte United States School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India College of Science and Engineering Hamad Bin Khalifa Universityy Ar-Rayyan Qatar Dept. of Ece Hyderabad500104 India

ISBN: (纸本)9798350379396

Energy optimization is critical in smart home systems and IoT networks, necessitating innovative models that reduce energy use. This research presents two Long Short-Term Memory (LSTM) network models for estimating home power consumption using time-series data. The first model employs a standard LSTM technique with multivariate characteristics including global active power, voltage, and sub-metering variables. This technique strikes a balance between prediction accuracy and computing economy, making it ideal for resource-constrained applications. The second model provides an optimized version of the LSTM, which uses PyTorch and Ray Tune for hyperparameter optimization. The optimization focuses on tweaking learning rates, batch sizes, and LSTM layers to improve model accuracy and convergence speed. Using hyperparameter tuning, the Mean Squared Error (MSE) for global active power forecasts is decreased to 0.0018, proving its appropriateness for low-resource IoT systems. Both models are tested on a resampled real-world household electric power consumption dataset for effective training. The study emphasizes the advantages of multivariate time-series analysis and hyperparameter optimization, demonstrating that the optimized LSTM model can accurately predict energy consumption, enhance energy management in smart homes, and lower computing costs. Future work will look into combining more IoT data streams and real-world deployment for further improvement. The findings add to the expanding body of knowledge about energy optimization in IoT environments, addressing the crucial demand for effective, real-time energy management systems. © 2024 IEEE.

关键词： Mean square error

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Multi-Modal Deception Detection Using Deep Learning

Multi-Modal Deception Detection Using Deep Learning

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International Conference on Computing and Networking Technology (ICCNT)

作者： Om Uskaikar Aditya Sonavane Mihir Randive Kanchan Dabre Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Vile Parle (W) Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Following decades of study proving that people cannot reliably discern dishonesty, a strong solution combining gestures, auditory characteristics, and micro-expressions is put forth. This multimodal deep learning system records verbal and nonverbal indicators linked to deceitful conduct by examining speech patterns and facial features. Different categorization models combine MLP (Multi-Layer Perceptron) with CNN (Convolutional Neural Network) architectures to process auditory information and LSTM (Long Short-Term Memory) to handle visual features. Accuracy and dependability are increased by combining predictions from many modalities using an ensemble fusion approach. The approach includes ensemble modeling, feature extraction, modality separation, and video preprocessing. With an overall accuracy of $\mathbf{9 4. 2 3 \%}$, evaluations on a carefully selected dataset of trial hearing recordings show excellent performance. The integrated technique provides a non-intrusive and dependable deception detection system by successfully separating honest from misleading remarks. The solution’s potential uses in security, human resources, and law enforcement underscore its significance and promise as a new benchmark for precise lie detection.

关键词： Deep learning Visualization Accuracy Computational modeling Feature extraction Convolutional neural networks Security Speech processing Long short term memory Stress

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SMART: Submodular data Mixture Strategy for Instruction Tuning

arXiv

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arXiv 2024年

作者： Renduchintala, H.S.V.N.S. Kowndinya Bhatia, Sumit Ramakrishnan, Ganesh Media and Data Science Research Adobe Inc. India Dept. of Computer Science & Engineering Indian Institute of Technology Bombay India

Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Studies have shown the importance of balancing different task proportions during finetuning, but finding the right balance remains challenging. Unfortunately, there’s currently no systematic method beyond manual tuning or relying on practitioners’ intuition. In this paper, we introduce SMART (Submodular data Mixture strAtegy for instRuction Tuning) — a novel data mixture strategy which makes use of a submodular function to assign importance scores to tasks which are then used to determine the mixture weights. Given a fine-tuning budget, SMART redistributes the budget among tasks and selects non-redundant samples from each task. Experimental results demonstrate that SMART significantly outperforms traditional methods such as examples proportional mixing and equal mixing. Furthermore, SMART facilitates the creation of data mixtures based on a few representative subsets of tasks alone and through task pruning analysis, we reveal that in a limited budget setting, allocating budget among a subset of representative tasks yields superior performance compared to distributing the budget among all tasks. The code for reproducing our results is open-sourced at https://***/kowndinyarenduchintala/SMART. © 2024, CC BY.

关键词： Budget control

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I. Predicting future citation counts using machine learning 22

I. Predicting future citation counts using machine learning

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22nd International Arab Conference on Information Technology, ACIT 2021

作者： Mansour, Khalid Al-Daoud, Essam Al-Karaky, Baha Data Science and AI Dept. Zarqa University Zarqa Jordan Computer Science Dept. Zarqa University Zarqa Jordan

ISBN: (纸本)9781665419956

The number of citations for a research paper is an important criterion in the scientific research community and is also an important quality indicator for both the author and the affiliated institution. Since the number of researchers, universities and scientific institutes are increasing, the number of submitted papers to scientific journals increased substantially. Consequently, the time needed to process the submitted papers is increased as well. This results in delaying publishing high quality papers. This paper proposes an approach to predict the future citations of submitted research papers using machine learning algorithms to speed up the processing time of the predicted high quality submitted papers. A dataset solicited from the International Arab Journal of Information Technology (IAJIT) is used in our experiments. The dataset covers the last ten years. Sixteen machine learning algorithms are trained on the dataset. Two sets of results are produced. The first one is related to the relative importance of features used in the prediction process. The second set shows the results of the sixteen machine learning algorithms used to predict future citations of submitted papers. The experimental results show that the number of references is the most important feature while the number of authors shown on papers is the least important feature. In addition, the results produced by neural network and voting classifier 1 techniques are slightly better than other techniques in predicting future citations. Naïve base comes next. The rest of the used machine learning methods show similar performance. © 2021 IEEE.

关键词： Forecasting

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Analysis and Ranking Resume using Machine Learning Algorithms and NLP

Analysis and Ranking Resume using Machine Learning Algorithm...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratik Chandrasen Deshmukh M. R. Bendre Dept. of Data Science Engineering Pravara Rural Engineering College Loni India Dept. of Computer Engineering Pravara Rural Engineering College Loni India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited.

关键词： Machine learning algorithms Resumes Employment Natural languages Machine learning Software Portals Recruitment Testing Qualifications

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Remote Sensing Aircraft Classification Harnessing Deep Learning Advancements

Remote Sensing Aircraft Classification Harnessing Deep Learn...

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International Conference on Emerging Technologies, ICET

作者： Ahmad Saeed Haasha Bin Atif Usman Habib Mohsin Bilal Dept. of AI & Data Science National University of Computer and Emerging Sciences Islamabad Pakistan

Remote sensing imagery is challenging to analyze due to its diverse sources, object image variability and contextual backgrounds. In current era, Aviation Industry is continuously progressing from specific domain of aircraft classification to improving in its safety measures, maintenance procedures and diverse flight operations by utilizing the potential of satellite imagery and Deep Learning methods. This paper highlights and explores the potential of Transfer Learning with state-of-the-art Deep Learning architectures on publicly available Multi Type Aircraft Remote Sensing Imagery datasets. In the same context, our experiments and training mechanism on the state-of-the-art models, like Vision Transformers, ResNet50v2, EfficientNetB0 and InceptionNetV3 outperforms earlier methods on the benchmark remote sensing military aircraft dataset. Our experiments on mentioned Deep learning models yielded, 95%, 93.4%, 92% and 80% classification accuracy due to advance architecture design and fine tuning techniques by allowing them to capture more intricate features and patterns of multifaceted military platforms. This research will provide a valuable resource in the aviation industry in the perspective of aircraft recognition domain by utilizing the power of advance Deep Learning methods to handle the complexity and diversity of remote sensing imagery.

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