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Unlocking the Potential of Generative AI inLarge Language models 6

Unlocking the Potential of Generative AI inLarge Language Mo...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Warudkar, Sahil Jalit, Rugved Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (纸本)9798350369748

In the ever-evolving realm of artificial intelligence, the fusion of Generative AI with Large Language models (LLMs) represents a ground- breaking leap forward in natural language processing(NLP), sparking a revolution in human-machine interactions. this comprehensive review delves into the transformative potential of Generative AI within LLMs, unveiling its profound impact on content creation, language translation, and conversational interfaces. through a meticulous analysis of recent advancements, the review illuminates how GenerativeAI is reshaping the NLP landscape, empowering LLMs to craft captivating narratives and bridge linguistic barriers with unparalleled finesse. However,ethical considerations loom large amidst this innovation, underscoring the imperative of model interpretability and data privacy in ensuring responsible deployment across domains like healthcare and finance. By navigating these challenges, Generative AI in LLMs heralds a new era of collaborative innovation and global connectivity, where human ingenuity and machine intelligence converge to unlock boundless possibilities. © 2024 IEEE.

关键词： Generative adversarial networks

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Non-Intrusive Biomass Estimation in Aquaculture Using Structure from Motion Within Decision Support Systems 6

Non-Intrusive Biomass Estimation in Aquaculture Using Struct...

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6th IEEE international conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Tang, Nyiak Tien Lim, Kit Guan Yoong, Hou Pin Ching, Fui Fui Wang, Tianlei Teo, Kenneth Tze Kin Borneo Marine Research Institute Universiti Malaysia Sabah Kota Kinabalu Malaysia Wuyi University Faculty of Intelligent Manufacturing Jiangmen China Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

this paper details an advanced implementation of Structure from Motion (SfM) techniques within Decision Support Systems (DSS) for non-intrusive fish weight measurement in Precision Fish Farming (PFF). Unlike conventional methods that often require physical handling or intrusive sensors, this study utilizes a synchronized multi-camera array strategically positioned underwater to capture high-resolution images across multiple viewpoints. Employing sophisticated algorithms, the research meticulously handles image distortion typical in underwater environments through tailored rectification processes, enhancing feature detection and matching accuracy under variable lighting and turbidity. Our approach integrates dense point cloud generation and sophisticated 3D mesh reconstruction to derive volumetric measurements of fish, which are then correlated with biomass estimations through empirical models refined with machine learning techniques for predictive accuracy. Initial validations against standard physical weighing methods demonstrate a significant enhancement in measurement precision and repeatability, with a reduced error margin by over 30%. these advancements not only streamline operational efficiencies but also pave the way for more sustainable fish farming practices by minimizing human-fish interactions and the associated stress-induced behavioral alterations. © 2024 IEEE.

关键词： Machine learning

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Weather Prediction Platform using Data Mining 6

Weather Prediction Platform using Data Mining

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6th international conference on Computing and Informatics, ICCI 2024

作者： Elsonbaty, Haya K. Ibrahim, Manar A. Elnakeib, Noure A. Noh, Eman A. Fayez, Haneen I. Elbanhawy, Magy Mazrua, Ahmed T. Moawad, Ibrahim F. Elzeki, Omar M. Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Engineering Portsaid University Egypt Mansoura University Mansoura Egypt Faculty of Computer and Information Sciences Ain shams University Cairo Egypt

ISBN: (纸本)9798350373875

this study focuses on using the Weka toolkit's machine learning algorithms to forecast weather conditions based on a dataset that includes columns for precipitation, temperature_max, temperature_min, and wind. Forecasting various weather conditions, such as drizzle, rain, sun, snow, and fog, is the goal. A thorough examination has been carried out utilizing a range of Weka algorithms, covering models with higher performance and lesser accuracy. After extensive testing, the best performing logistic algorithms meta. logitboost, *** Classifier, ***, trees.j48, and functions. Logisticwere able to predict the weather with exceptional accuracy. these models demonstrated how well they could comprehend and predict weather conditions using the parameters from the given dataset. On the other hand, weather state predictions were relatively less accurate for models using the meta. classificationvia Regression, meta .bagging, ***, misc. input, and *** algorithms. In order to furnish stakeholders with practical and captivating insights, the outcomes were condensed and shown via user-friendly and aesthetically pleasing Google Looker dashboards. With the help of these dashboards, stakeholders may interactively examine and understand the predicted weather conditions that are based on the reliable machine learning algorithms. this research attempts to provide decision-makers with easily accessible, data-driven weather forecasts by combining these predictive algorithms with Google Looker's visualization capabilities. the dashboard system's extensive analysis and visualization are intended to improve the comprehension and use of machine learning in weather prediction, promoting well-informed decision-making across a range of industries dependent on weather-sensitive operations. © 2024 IEEE.

关键词： Decision making

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Evaluating the Efficacy of Feature Selection Methods in Cardiovascular Disease Prediction with Machine Learning 6

Evaluating the Efficacy of Feature Selection Methods in Card...

引用

6th international conference on Electrical Information and Communication Technology, EICT 2023

作者： Haque, Rezwanul Mahedy Hasan, S.M. Srizon, Azmain Yakin Efat, Anwar Hossain Faruk, Md. Farukuzzaman Oishe, Mahjabin Jannat, Nahrin Mitu, Mostarina Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350370836

Cardiovascular disease (CVD) remains a primary contributor to global mortality, necessitating refined predictive models. Machine learning (ML) has emerged as a pivotal tool in augmenting clinical decisions, particularly in CVD prediction. Nonetheless, the performance of ML classifiers is substantially influenced by the judicious selection of relevant features. this manuscript presents a focused comparative analysis of feature selection methodologies in CVD prediction through ML classifiers. Utilizing a clinical dataset, the study assesses the efficacy of various classifiers based on distinct feature subsets. Empirical results indicate that the Random Forest classifier, with a mere five selected features, attains a remarkable 99.70% accuracy, thus qualifying as an optimal choice for clinical settings. the research underscores the necessity of scrupulously evaluating diverse combinations of feature selection and learning algorithms to derive the most effective model. Collectively, this work provides pivotal insights into optimal feature selection for precise CVD prediction, thereby contributing to the development of more efficient predictive strategies. © 2023 IEEE.

关键词： Forecasting

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Bug predictive models based on Data Analytics and Soft Computing Techniques: A Survey 10

Bug Predictive Models based on Data Analytics and Soft Compu...

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Jadhav, Veena Devale, Prakash Jadhav, Rohini Molawade, Mayuri Mohite, Sagar Bidwe, Ranjeet Vasant College of Engineering Department of Computer Engineering Pune India Lavale Pune India

ISBN: (纸本)9789380544472

As software projects become larger and more complex, bugs pose a significant threat to their consistency and efficiency. During early phases of software development, bug prediction essentially enhances vital aspects such as software efficiency reliability quality, cost of development. Hence, various software bug prediction techniques have been presented previously but there is no research that focuses on surveying logical analysis and Just-In-Time (JIT) bug predictions with the consideration of data analytics and soft computing techniques. Hence this research investigates Soft computing and hybrid logistic model by determining imperfect range of data, class labels with heuristics prediction of bugs. Also, survey data analytics techniques such as clustering and oversampling techniques with JIT prediction for forecasting software bug in future. the study also compares various software bug prediction techniques to determine their effectiveness. then, mentioned the basic benefits and drawbacks of the various bug prediction techniques. Moreover, future directions are provided to overcome the existing impact of various techniques in software bug prediction. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Data Analytics

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Empowering Ultra-Low Power with RISC-V ISA Extension Using Reversible Logic Gates for IOT Devices 6

Empowering Ultra-Low Power with RISC-V ISA Extension Using R...

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6th international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Narendra Kumar, B. Soni, K. Sangala, Ashok Anjani, C. Bhagyalakshmi, U. Sridevi Women's engineering college Department of Information Technology Telangana Hyderabad India Sridevi Women's engineering college Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9798331505790

An open-source RISC-V instruction-set augmentation (RV32IM) is introduced in this study for use in software-defined Wi-Fi Internet of things (IoT) transceiver devices with very low power consumption. the purpose of the updated instructions is to address a widespread need for complicated math in quadrature modulations using 8 / 16 / 32 bits of numerical data. For the most part, the suggested extension's energy cost is near to nothing, and it uses only two main op-codes. the two novel architectural models, the instruction exact (IA) and the process accurate (CA), are used to evaluate six IoT test seats, such as FSK demodulating and LoRa preamble detection. Simulation findings reveal a large variation in cycle count improvements, ranging from 19 % through 68 %. In post-synthesis simulations, the 22 nm FD-SOI technology outperforms a baseline RV32IM design with an electricity overhead of less than 1 % and an area overhead of 28 %. According to the power calculations, for BW=500 kHz with SF=11,380 μW is used for Bluetooth LE demodulation by peak electrical power, and 225 μW is used for LoRa preamble detection. © 2024 IEEE.

关键词： Demodulation

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A Systematic analysis on Security and Anomaly Detection using Machine Learning in Cloud Computing 6

A Systematic analysis on Security and Anomaly Detection usin...

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6th international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Tanam, Aparna Raja, G. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798331505790

the term 'cloud computing' describes a way of using the Internet to access resources, software, and databases without being constrained by local hardware. Businesses adopting this technology can grow their operations more flexibly by outsourcing all or a portion of the management of their infrastructure to outside hosting companies. Cloud-based security services are increasingly being used by contemporary enterprises. the dynamic and unpredictable environment, particularly in online applications and apps for the general public, can provide a number of issues even when organizations fully resource their departments. these as-a-service business models let companies outsource a variety of time-consuming IT-related jobs. the traditional intended approaches have significant difficulties with data loss prevention, anomaly detection, and data analysis. Moreover, the current cloud computing system has shortcomings in the areas of causal effect analytics, anomaly detection, and data loss prevention. the machine learning technique greatly increases the effectiveness of anomaly detection in a cloud computing network. this research effort introduces a novel approach to deal with the system's constraints in its current state. the suggested work introduces proactive security mechanisms to stop the loss of sensitive data. A signature system based on machine learning is utilised to detect anomalies. © 2024 IEEE.

关键词： Cloud security

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Prescribed-Time Clusters Flocking for Multi-Agent Systems of UAVs and USVs 6

Prescribed-Time Clusters Flocking for Multi-Agent Systems of...

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6th international conference on Electronic engineering and Informatics, EEI 2024

作者： Shang, Hanjun Wang, Zhenhuan Tian, Yingxin Hou, Qimin Kuang, Jiyuan Gao, Yabin School of Astronautics Harbin Institute of Technology Harbin150001 China Elite Engineers School Harbin Institute of Technology Harbin150001 China The National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China School of Software Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9798350353594

this paper deals with the problem of clusters flocking for a heterogeneous multi-agent system of Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vessels (USVs). the dynamics models of the UAVs and USVs are established separately. Due to the different state dimensions and dynamics models of the UAVs and USVs, the two types of agents are formulated with two clusters. then, the functions of formation errors of the two clusters are constructed. For the two clusters, a new type of prescribed-time formation control laws is designed respectively. Moreover, compared with the conventional formation control that can only ensure that the multi-agent system completes ideal formations without strict time constraints theoretically, the present prescribed-time control laws can enable the multi-agent system to complete ideal formation strictly at a prescribed time independent of initial formation errors. Finally, the effectiveness of the prescribed-time formation control is verified by simulations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Detection & Classification of Lung Diseases using Hybrid Deep Learning Algorithms 6

Detection & Classification of Lung Diseases using Hybrid Dee...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Chopra, Cherry Ahuja, Naman Tyagi, Neha Amity School of Engineering & Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350369748

Accurate and early diagnosis of lung diseases remains crucial for effective treatment and improved patient care. Conventional methods often lack precision and objectivity. Deep learning algorithms have emerged as promising tools for lung disease detection and classification, with Convolutional Neural Networks (CNNs) like VGG16, Xception and Inception architecture and transfer learning models like ResNet34 demonstrating remarkable performance. Hence, this paper explores the applicability of these models for enhanced lung disease detection, focusing primarily on three most widespread lung diseases - pneumonia, lung cancer and Covid-19. the Lung & Colon Cancer dataset, containing 15000 images and Lung X-Ray dataset containing more than 5500 images, both have been taken from Kaggle and utilized for model training and testing purposes. Inclusion of a user-friendly web application that integrates the three models into a single platform is also explored. the results shed light on the advantages and limitations of different deep learning architectures, offering valuable insights for further research and development of robust and reliable lung disease detection systems. © 2024 IEEE.

关键词： Lung cancer

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Anomaly Detection of EEG in patients with epilepsy based on Graph Deviation Network 6

Anomaly Detection of EEG in patients with epilepsy based on ...

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6th international conference on Computer Information Science and Application Technology, CISAT 2023

作者： Chen, Ming Liu, Changyu Gao, Huaijin Tao, Haomin Lin, Siyuan Computer Technology and Software Engineering Wuhan Polytechnic Wuhan430074 China Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781510668546

A chronic neurological condition called epilepsy affects about 50 million people globally. Although there has been a lot of study on deep neural networks to identify seizures, it is still difficult to precisely define the structural relations between electrodes in EEG signals. the difficulty involved may lead to less accurate prediction. We provide a novel method that combines structured learning methods with graph neural networks and incorporates graph convolution to provide explanations for detected anomalies in EEG signals in order to overcome this constraint. Our investigations on the CHB-MIT dataset show that our approach is more effective and efficient than baseline methods and conventional neural network models at identifying the onset of seizures. © 2023 SPIE.

关键词： Anomaly detection

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