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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ml) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help? 23

Low-Resource Named Entity Recognition: Can One-vs-All AUC Ma...

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23rd IEEE International Conference on Data Mining, ICDM 2023

作者： Nguyen, Ngoc Dang Tan, Wei Du, Lan Buntine, Wray Beare, Richard Chen, Changyou Monash University Department of Data Science and Ai Melbourne Australia VinUniversity College of Engineering and Computer Science Hanoi Viet Nam Faculty of Medicine Peninsula Clinical School Melbourne Australia University at Buffalo Department of Cs and Engineering New York United States

ISBN: (纸本)9798350307887

Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, which is common in certain NER contexts, like biomedical NER (bioNER). To address these issues, we propose an innovative reformulation of the multi-class problem as a one-vs-all (OVA) learning problem and introduce a loss function based on the area under the receiver operating characteristic curve (AUC). To enhance the efficiency of our OVA-based approach, we propose two training strategies: one groups labels with similar linguistic characteristics, and another employs meta-learning. The superiority of our approach is confirmed by its performance, which surpasses traditional NER learning in varying NER settings. © 2023 IEEE.

关键词： Natural language processing systems

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Control of Two-Wheel Self-Balancing Robot: LQR and MPC Performance Analysis

Control of Two-Wheel Self-Balancing Robot: LQR and MPC Perfo...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Ashutosh Mishra Kritika Bansal Department of Electronics Engineering Madhav Institute of Technolgy and Science Gwalior India Centre for Artificial Intelligence (AI & ML) Madhav Institute of Technolgy and Science Gwalior India

This paper presents the design and comparative analysis of Linear Quadratic Control and Model Predictive Control strategies for a two-wheel self-balancing robot. A detailed mathematical model of the two-wheel self-balancing is provided. After that, the Linear Quadratic Regulator and Model Predictive Control law are designed. In Order to compare the performances of both the controllers, various performance indices such as settling time and peak overshoot value are evaluated. The robustness of these methods is tested against impulse and sinusoidal disturbance. The control signals from linear quadratic regulator and model predictive controllers are also examined. The simulation results are obtained using MATLAB wherein it is observed that model predictive control performed better in both tracing the set points and generating minimal control signal.

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ai in drug development:advances in response,combination therapy,repositioning,and molecular design

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science China(Information sciences) 2025年

作者： Ren QI Shujia LIU Xingqi HUI Alexey K.SHAYTAN Bin LIU School of Computer Science and Technology Beijing Institute of Technology Department of Biology Lomonosov Moscow State University International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer ScienceHSE University SMBU-MSU-BIT Joint Laboratory on Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Zhongguancun Academy

Artificial intelligence (ai) is revolutionizing the field of drug development, particularly in addressing key challenges such as drug response prediction, drug combination design, drug repositioning, and drug molecule generation. Traditional drug discovery is hindered by long timelines, high costs, and low success rates, necessitating innovative technologies to accelerate the process. ai technologies, such as deep learning, graph neural networks, and generative models, have demonstrated significant potential in enhancing the accuracy of drug response predictions, optimizing drug combination strategies,identifying opportunities for drug repositioning, and generating drug molecules with specific biological activities. These advancements not only accelerate the drug development process but also open up new possibilities for precision *** review discusses the latest applications and developments of ai in drug discovery, highlighting the breakthroughs and challenges ai addresses in drug development. By summarizing the current research progress, this study provides theoretical support and practical guidance for further applications of ai in drug development.

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Real-Time IoT Cybersecurity using Machine Learning-based ai Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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Fault Detection and Classification in Power Transmission Lines Using Discrete Wavelet Transform-Based Swish Recurrent Neural Network 3

Fault Detection and Classification in Power Transmission Lin...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Narasimhulu, N. Irfan, Md. Mujahid Sundaram, S Meenakshi Habelalmateen, Mohammed I. Gurumoorthy, Sasikumar Eee Department Ananthapuramu Gooty India Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

Transmission lines are an essential power grid component that is responsible for managing electricity from a production station to consumers and substations. The power network's transmission lines are primarily met with various faults. However, to maintain normal operation and minimize damage because of transmission line faults, it requires detecting and classifying faults. In this research, the Discrete Wavelet Transform-based Swish Recurrent Neural Network (DWT-SRNN) is proposed to detect and classify faults in power transmission lines using Deep Learning (DL). Initially, the data is acquired from the transmission line workspace by monitoring devices and sensors. Then, the Butterworth filtering is employed to increase the data quality. The DWT is performed to extract the features that transfer the time domain signal to the time-frequency signal during faulty and non-faulty conditions by decomposition. Finally, the SRNN is established to detect and classify faults in power transmission lines effectively. The existing techniques like Graph Convolutional Neural Network (GCNN), Machine Learning (ml), and Long Short-Term Memory (LSTM) are compared with DWT-SRNN. The proposed DWT-SRNN achieves a better accuracy of 99.89% compared to GCNN, ml, and LSTM respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Machine Learning Algorithms for Advanced Rainfall Prediction 23

Machine Learning Algorithms for Advanced Rainfall Prediction

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Vivek Saxena Uday Pratap Singh Bersha Kumari Ansh Khandelwal Department of Computer Science & Engineering Poornima Institute of Engineering & Technology India Department of AI & DS Poornima Institute of Engineering and Technology India Department of Computer Science Poornima Institute of Engineering and Technology India

ISBN: (纸本)9798400709418

Rainfall forecast is essential in water resource management, agricultural planning, and disaster preparedness. Traditional rainfall forecasting systems have accuracy and lead time constraints. The rise of machine learning (ml) techniques provides a viable route for addressing these issues. This review paper examines current advancements in the application of machine learning for forecasting rainfall. It investigates the difficulties connected with rainfall forecasting, such as the complex and nonlinear nature of precipitation systems, data scarcity, and the requirement for real-time forecasting. The research investigates several machine learning models, data sources, and pre-processing approaches used to improve prediction accuracy. It also covers the incorporation of satellite data, weather radar data, and climate models into ml-based rainfall forecast systems. A comprehensive review of model performance in various geographic locations and climatic situations is offered, along with an assessment of limits and future development potential. This paper intends to assist academics, meteorologists, and policymakers in utilizing ml to provide more accurate and dependable rainfall forecasts, hence improving water resource management and disaster mitigation.

关键词： Keywords— Rainfall forecast

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Vision-Based Object Sorting in Dynamic Environments using YOLO for RoboCup ARM Challenge 2023 7

Vision-Based Object Sorting in Dynamic Environments using YO...

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7th IEEE Conference on Information and Communication Technology, CICT 2023

作者： Sahu, Yash Kumar Mittal, Radhika Patel, Deep Maiti, Chayan Sreekumar, M. Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Mechanical Engineering Kancheepuram Chennai India Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Computer Science Kancheepuram Chennai India

ISBN: (纸本)9798350305173

In the intersection of Artificial Intelligence (ai) and Robotics, autonomous object manipulation has emerged as a pivotal research domain, finding applications in sectors as diverse as manufacturing and healthcare. While significant strides have been made in object identification, trajectory planning, and manipulation tasks, these advancements often fall short in real-world, dynamic settings, revealing a critical research gap in algorithmic adaptability and computational efficiency. This paper addresses the gap by applying YOLOv4, a state-of-the-art object detection ai algorithm, within the context of the RoboCup Autonomous Robot Manipulation (ARM) Challenge - a competitive platform designed to simulate real-world complexities in autonomous systems. The research objectives are twofold: firstly, the performance of YOLOv4 in object detection and classification under dynamically changing conditions is rigorously evaluated, employing varied training strategies in both simulated and real-world scenarios. Secondly, the robustness and adaptability of the algorithm in object manipulation tasks are scrutinized and facilitated by the real-world dynamics inherent to the competition. Empirical results demonstrate the algorithm's high accuracy, efficiency, and robustness in dynamic environments, substantiating its broader applicability in the development of autonomous systems. © 2023 IEEE.

关键词： Object detection

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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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Classification of Various Diseases in Tomato Crops using Neural Network Approaches

Classification of Various Diseases in Tomato Crops using Neu...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Kodem Sravan U Sadhana Sallauddin Mohmmad Bobby K Simon K Sridhar School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of CSE(AI&ML) CVR College of Engineering Hyderabad Telangana India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana India Vaageswari College of Engineering Karimnager Telangana India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Tomatoes are an important crop worldwide, playing a substantial role in the agriculture sector. However, they are sensitive to various diseases that can affect yield, quality of crops, and former economic stability. This research focused on developing a system for classifying tomato diseases based on leaf images using machine learning and image processing techniques. Our research utilized convolutional neural networks (CNNs) to predict and classify tomato diseases, employing a dataset from Kaggle containing five disease classes and one healthy leaf class. Our optimized models were tested, showing inconsistent accuracy in detecting and classifying five different tomato diseases. Apart from that, MobileNet and VGG-19 models showed prominent performance and achieved an accuracy of 93.87% compared to other models such as VGG16 and a Customized CNN.

关键词： Technological innovation Accuracy Biological system modeling Image processing Neural networks Crops Stability analysis Convolutional neural networks Protection Diseases

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