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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Paul, Bidyarthi Preotee, Faika Fairuj Sarker, Shuvashis Muhammad, Tashreef Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka1208 Bangladesh Southeast University Department of Computer Science and Engineering Dhaka1208 Bangladesh

ISBN: (纸本)9798331529765

This research addresses the challenge of identifying and categorizing Bangla regional dialects through the application of sophisticated natural language processing techniques. Automated translation, digital content personalization, and speech recognition systems are all improved by precise dialect detection, which is a result of the linguistic diversity in Bangladesh. Few-shot learning techniques were used to compare the performance of transformer-based models, specifically Bangla BERT, with state-of-the-art large language models such as GPT-3.5 Turbo and Gemini 1.5 Pro, and to fine-tune them. The methodology entailed the utilization of a diverse regional Bangla speech samples from the Vasantor dataset, which encompassed regions including Mymensingh, Chittagong, Barishal, Noakhali, and Sylhet. In order to enhance the interpretability of the model, implementation of Local Interpretable Model-agnostic Explanations (LIME) was done. The Bangla BERT model achieved the highest accuracy of 88.74%. GPT-3.5 Turbo's few-shot learning exhibited substantial potential, with an accuracy rate of 64%. These results underscore the significance of hyperparameter optimization and fine-tuning in the enhancement of regional dialect detection models. © 2024 IEEE.

关键词： Speech recognition

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Federated Vehicular Tailgating Detection Utilizing Distance and Speed Data Analytics for Intelligent Transport System 10

Federated Vehicular Tailgating Detection Utilizing Distance ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Velumail, Subbulakshmi Chaudhari, Shilpa Shashikant Sai Kiran, T.S. Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350353068

Connected and Automated Vehicles (CAVs) are designed with sensors, cameras, and other computing devices to collect data to support cooperative Intelligent Transportation systems (ITS). Many potential applications offered by CAVs exist for researchers and road safety stakeholders. Vehicle tailgating is one such application to analyze risky driving behavior. Reliable detection of such behavior is hard and expensive. This paper focuses on addressing the tailgating issue in CAVs using the dataset generated using Simulation of Urban Mobility (SUMO) for Indian roads. The proposed model of this study uses the Relative speed and Euclidean distance of the preceding and rear vehicle as parameters on the distributed Federated Learning (FL) framework to incorporate security for sensitive vehicular data. We verified our prediction model in the TensorFlow Federated framework, and our results show an accuracy of 96 percent. © 2024 IEEE.

关键词： Vehicles

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A Novel Methodology for Cotton Leaf Disease Detection using CNN 3

A Novel Methodology for Cotton Leaf Disease Detection using ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sitharam, M. Anusha, Vennapoosa Sri, Pallapothu Navya Sri, Gollapudi Hema Mylavaram Department of Computer Science and Engineering Lakireddy India

ISBN: (纸本)9798350375190

Precision agriculture aims to improve agricultural productivity by combining technology and farming. The productivity of the cotton is mostly effected by leaf diseases, if we predict these diseases at early stage it helps the farmers to improve productivity. We put forward a novel method for cotton leaf disease detection that works on hybrid dataset which compromises of images from the kaggle dataset and real-time images. Deep learning models VGG16 and VGG19 are applied on this hybrid dataset for disease detection of cotton leaves. This research will not only contribute to improve crop health but also be a valuable resource for farmers. A systematic comparison of the VGG16 and VGG19 models reveals their functional differences in disease detection. VGG16 and VGG19 has achieved accuracy of 94% and 95% in disease detection © 2024 IEEE.

关键词： Cotton

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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An Efficient Machine Learning Classification Model for Rainfall Prediction in Bangladesh 2nd

An Efficient Machine Learning Classification Model for Rainf...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Biplob, Md. Badiuzzaman Haque, Md. Mokammel Computer Science and Engineering Department Chittagong University of Engineering and Technology Chattogram Bangladesh Computer Science and Engineering Department Faculty of Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9789819989362

One of the most significant and difficult tasks in the modern world is rainfall forecast. Rainfall is a complicated and nonlinear phenomenon that requires sophisticated computer modeling and simulation to anticipate with any degree of accuracy. In many regions of the world, daily rainfall totals are dispersed based on various frequency distribution functions. Rainfall has become a significant factor in agricultural countries. We provide a machine learning-based framework for forecasting the total monthly precipitation. In this study, three Bangladeshi weather stations’ daily rainfall was forecasted with a 365-day lead time using backpropagation long short-term memory (LSTM), random forest, and neural network algorithms. The predicted results from the selected algorithms were compared with the observed data to determine prediction precision by mean absolute error (MAE) test results. We found that selected algorithms predicted daily rainfall with reasonable accuracy. Therefore, year-long, month-long, and day-long rainfall can be predicted using these models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Integrating Imaging and Clinical Data for Parkinson's Disease Diagnosis using Deep Learning 7

Integrating Imaging and Clinical Data for Parkinson's Diseas...

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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Divya, D. Kshirsagar, Pravin R. Tak, Tan Kuan Naveed, Quadri Noorulhasan Department of Computer Science and Engineering Jerusalem College of Engineering Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore Department of Computer Science and Engineering King Khalid University Saudi Arabia

ISBN: (纸本)9798331515683

That disorder affecting the nervous system, Parkinson's disorder can worsen with time and cause a host of health problems. Parkinson's disease diminishes cerebral function and may induce physical complications in senior individuals. Parkinson's disease can induce a range of disorders by impairing neurological in nature, behavioral, and physiological systems. Physical structures exhibit minimal damage at early diagnosis, complicating the prediction of Parkinson's illness in its initial stages based solely on physical observation. Recent studies on electroencephalogram (EEG) signs in the identification of Parkinson's illness have revealed significant aspects pertinent to early detection. Electroencephalography (EEG) is a multifaceted signal comprising several non-linear and non-stationary elements. Subsequently, an auto-encoder-based deep extraction of features method is employed to derive the spatial and temporal attributes from the four frequency partitions. Additionally, statistical and spectral attributes are obtained. The gathered attributes are utilized to analyze notable patterns associated with Parkinson's disease. Optimal weighted feature fusion is achieved through the precise adjustment of weights. The proposed DLB-LSTM (Densely Located Bidirectional Long Short Term Memory) employs weighted features so that we can identify PD. To impede the progression of Parkinson's disease, the algorithm autonomously learns from the signals and delivers targeted outcomes. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Machine Learning Techniques for Forecasting Prices and Agricultural Yields 2

Machine Learning Techniques for Forecasting Prices and Agric...

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2nd International Conference on Artificial Intelligence Trends and Pattern Recognition, ICAITPR 2024

作者： Singh, Anjali Janu, Neha Poornima College of Engineering Department of Computer Science & Engineering Rajasthan Jaipur India Manipal University Jaipur Department of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350368000

Civilization began with agriculture. India is an agrarian nation whose economy is heavily reliant on crop productivity. As a result, agriculture is the foundation of all business in India. In terms of farm production, India now ranks second in the world. Climate, topographical, chronological, spatial, biotechnological, diplomatic, organizational, and socio-demographic factors impact Indian agriculture. A recent development in yield and growth evaluation involves machine learning. In agriculture, crop productivity is a critical issue. Every producer would like to know how much yield he can expect. The prediction of yield is a major issue that has yet to be resolved according to the available information. For this purpose, machine learning techniques are the preferred idea. In agriculture, different artificial intelligence methods have been implemented and appraised, usage and assessment of predictive modeling techniques is done to anticipate the production of agriculture. for the following year. Our article suggests and needs to implement a method for estimating crop production based on historical data. The study's objective is to find the best crop forecast model and price prediction that can help farmers determine what crop to grow based on weather and soil nutrients. This paper compares well-known algorithms such as Decision Trees, Support Vector Machine, Gradient Boosting Classifier, K-Nearest Neighbor, Random Forest, and Logistic Regression. Random Forest expresses the highest precision of 99.29 percent among all classifiers. © 2024 IEEE.

关键词： Support vector machines

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Global Positioning of Underwater Nodes Using Airbome-formed Visual Light Beams and Acoustic Ranging 59

Global Positioning of Underwater Nodes Using Airbome-formed ...

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59th Annual IEEE International Conference on Communications, ICC 2024

作者： Saif, Jaeed Bin Younis, Mohamed Choa, Fow-Sen Ahmed, Akram University of Maryland Baltimore County Department of Computer Science and Electrical Engineering BaltimoreMD United States King Fahd University of Petroleum and Minerals Computer Engineering Department Dhahran Saudi Arabia

ISBN: (纸本)9781728190549

In many applications of underwater networks, surface nodes cannot be employed for security and logistical reasons. For these scenarios an airborne unit ought to reach the underwater nodes directly from the air in order to provide commands to and configure the network. In particular, the airborne unit should facilitate localizing the underwater nodes and enable the establishment of a global coordinate system. Visible Light Communication (VLC) stands out as a prime choice for communication across the air-water interface. Using VLC, an underwater node uses the light intensity to infer proximity of the beam incident point. When receiving a sufficient number of VLC transmissions, prior work could localize the underwater nodes. However, such an approach requires very fine-grained area coverage, especially in shallow water environments, which is not practical in many application scenarios. This paper tackles such a limitation by introducing a novel Hybrid Airborne-Enabled Underwater Localization (HAUL) method. HAUL leverages knowledge of the underwater network topology in the localization process. Specifically, both acoustic ranging and light intensity measurements are used to estimate the node's global coordinates. HAUL is validated through extensive MATLAB simulations, demonstrating its effectiveness in calculating accurate positions. © 2024 IEEE.

关键词： MATLAB

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Survey of Deep Learning Techniques for Malware Detection: Insights, Challenges, and Future Directions 4

Survey of Deep Learning Techniques for Malware Detection: In...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sree Bai, Kshatriya Vinaya Thirumaran, M. Department of Computer Science and Engineering Tamilnadu Puducherry India

ISBN: (纸本)9798331515720

Android and Windows become the main platforms for mobile and personal computing, the threat from advanced malware targeting these systems is increasing. Traditional machine learning methods are struggling to keep up with these sophisticated threats, so deep learning techniques, which have shown better results, are needed. This paper reviews various deep learning methods for detecting and classifying malware across different environments, like IoT and web platforms. We examined 40 research articles from major digital libraries, focusing on different deep learning models like CNNs, LSTMs, DBNs, and *** analysis highlights the strengths and weaknesses of these models, such as high false positive rates and difficulties with detecting hidden malware. In order to decrease false positives and increase detection accuracy, we provide a novel framework that integrates many deep learning methods. This survey offers a comprehensive guide for new researchers and practical insights for improving malware detection with deep learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Assessing the Viability of AC to DC Microgrid Conversion: A Comprehensive Analysis of Technical and Economic Factors 59

Assessing the Viability of AC to DC Microgrid Conversion: A ...

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59th International Universities Power engineering Conference, UPEC 2024

作者： Charalambous, Chrysanthos Polycarpou, Alexis Efthymiou, Venizelos Georghiou, George E. University of Cyprus Pv Technology Laboratory Department Of Electrical and Computer Engineering Cyprus Phaethon Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Frederick University Department of Electrical Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350379730

In the face of global warming and the acceleration of climate changes, urgent and decisive measures must be taken to mitigate these phenomena. This study addresses this urgent need by exploring the transition from traditional AC distribution systems to more efficient DC and hybrid AC/DC systems, recognizing the utmost importance of both technical developments and economic considerations. Through a thorough overview of existing literature and empirical knowledge gathered from the nanogrid of FOSS Research Centre for Sustainable Energy of University of Cyprus, this research clarifies the inherent advantages of DC and AC/DC hybrid systems. In addition, it presents a detailed case study for the comprehensive assessment of the feasibility and effectiveness of these systems. The preliminary economic analysis carried out in this study demonstrates the cost-effectiveness and performance improvements offered by hybrid AC/DC microgrids compared to conventional AC configurations. According to the findings of this work it is obvious that the ROI increases with the increase of DC loads over AC loads until the case where DC loads reach the 75% of the total load (ROI=13.9%). However, even in the case of Full DC microgrid, the ROI is higher by 1% compared to the case of Full AC microgrid thus significantly reducing the payback period. These findings not only underline the urgency of the transition to more efficient and sustainable energy distribution systems, but also provide valuable information for policymakers, industry stakeholders and researchers. By embracing these innovative technologies, a more resilient and environmentally responsive energy infrastructure can be promoted that is in line with the demands of a rapidly changing global climate. © 2024 IEEE.

关键词： Clean energy

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