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Deep learning Based Speculative Analysis of Diverse Nature Inspired optimization algorithms in Agriculture 5

Deep Learning Based Speculative Analysis of Diverse Nature I...

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5th international conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Chandraprabha, M. Dhanaraj, Rajesh Kumar School of Computing Science and Engineering Galgotias University U.P. Greater Noida India Pune India

ISBN: (纸本)9798350321425

the main process involved in Deep learning is the optimization as it helps to select the best from the different available alternatives such as weights and learning rates. Deep Neural Networks (DNNs) have shown a great potential in a variety of fields. However, as neural networks get deeper and datasets become larger, optimizing these networks becomes increasingly challenging. In a deep learning model, there are some functions that are highly responsible for making accurate predictions. they are objective function, activation function, and loss function. Deep learning model training includes learning the parameters to fulfill the objective function. Typically, the goal is to decrease the loss experienced throughout the learning process. To increase the accuracy of the model, an optimisation process is performed repeatedly until it achieves a good level of convergence. In this research study, diverse optimization algorithms ranging from behavioral patterns of animals or birds such as FFO, PSO, HOA, ALO, WOA, LWBA, FOA and CSOA are reviewed and the evaluation has been done based on the following performance metrics like accuracy, precision, f1 score, and recall. Among all these algorithms, WOA outperformed and produced an accuracy of about 99% with less error rate. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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A Binary Particle Swarm optimization Based Hybrid Feature Selection Method for Accident Severity Prediction 1

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5th international conference on Smart applications and Data Analysis for Smart Cyber Physical Systems

作者： Hamim, Mohammed Enaanai, Adil Jadli, Aissam Moutachaouik, Hicham El Moudden, Ismail Univ Hassan 2 AICSE Lab Casablanca Morocco Fac Sci Dept Comp Sci Tetouan Morocco Eastern Virginia Med Sch EVMS Sentara Healthcare Analyt & Delivery Sci Norfolk VA USA

ISBN: (数字)9783031770401

ISBN: (纸本)9783031770395;9783031770401

According to recent reports, road traffic injuries are the leading cause of death among children and young adults. Various systems and strategies have been designed to reduce accident severity. With the development of data mining tools, the use of big traffic data and machine learning techniques holds potential for implementing effective road safety strategies. Using a dataset collected from Addis Ababa, Ethiopia, our research introduces an innovative severity prediction system integrating a hybrid Feature Selection (FS) approach, named OWABPSO, with machine learning algorithms. the OWABPSO approach combines a One-Way-ANOVA-based filter method with a Binary Particle Swarm optimization-based wrapper method. Six algorithms, including K-Nearest Neighbors, Random Forest, Decision Tree, Light Gradient Boosting Machine, Artificial Neural Network, and Extreme Gradient Boosting, are proposed for severity prediction. Experimental outcomes of this work demonstrate that, compared to state-of-the-art methods, by combining our FS approach with Decision Tree-based classifiers, we achieved competitive results. Our study presents an effective integration of FS approaches in predicting accident severity levels, thus contributing to advanced road safety strategies.

关键词： Road Traffic Accidents Feature Selection Binary Particle Swarm optimization Machine learning

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Diffuse reflection metasurface optimization design based on Genetic algorithm 4

Diffuse reflection metasurface optimization design based on ...

引用

4th international conference on Machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Zhang, Yuan Zhang, Hui Wu, Shan Wan, Ziyi Key Laboratory of Functional Materials and Devices for Informatics Anhui Higher Education Institutes Fuyang Normal University Anhui Fuyang China

Conventional metasurfaces rely heavily on manual design and optimization, which is tedious and inefficient and cannot be adapted to the engineering applications of modern wireless communication networks. the use of intelligent algorithms to design smart metasurfaces with high accuracy and efficiency is becoming a growing trend. In this paper, a uniform scattering smart metasurface based on a 2-bit cell structure is implemented by optimizing the coding sequence of the metasurface using genetic algorithm. By increasing the number of scattering beams, the incident wave is reflected to as many directions as possible with uniform energy. It has an important application prospect in the fields of future 6G mobile communication and radar stealth technology. © 2024 the Authors.

关键词： Stealth technology

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Design of Multi-Output Power Supply Based on improved BPNN-PID and Extended State Observer Control 4

Design of Multi-Output Power Supply Based on improved BPNN-P...

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4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Liu, Jiantao Wang, Yanqiu Sun, Luwei China Oilfield Services Ltd. Geophysical Division Tianjin China Research Institute of Tsinghua University in Shenzhen Shenzhen China

ISBN: (纸本)9798350359589

In order to meet the requirements of high stability, fast response and strong anti-interference for the power supply of the underwater detector, a single-ended flyback dual-output isolated DC-DC converter circuit is designed as the power supply of the detector. However, the power supply based on traditional PID control has problems of poor output voltage stability, slow response speed and large ripple. In order to improve the output voltage response speed and stability of the power supply, a BP-PID control strategy based on ADE optimization is proposed, which combines neural network and PID control. It adapts the PID parameters according to the output voltage situation, making the power supply have good robustness. For the problem of large output voltage ripple, a ripple disturbance suppression extended state observer based on bandwidth method is proposed to sample the DC voltage observation. the method of integrating the extended state observer effectively suppresses the ripple, thus improving the performance of the underwater detector and ensuring that the output voltage of the DC-DC buck converter is in the optimal state. Finally, the experimental results show that the proposed method can effectively improve the system output voltage stability, response speed and reduce the voltage ripple, and has good practical engineering application value. © 2024 IEEE.

关键词： ADE optimization BP-PID control strategy extended state observer isolated DC-DC converter circuit

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Security Analysis of Conventional Attack by Suitable RFID Based Deep learning Method in Industrial IoT 4

Security Analysis of Conventional Attack by Suitable RFID Ba...

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4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Firdos, Kauser Deshpande, Bhargavi School of Arts & Humanities Iimt University Uttar Pradesh Meerut India Isdi - School of Design & Innovation Atlas Skill Tech University Maharashtra Mumbai India

ISBN: (纸本)9798350333558

Radio Frequency Identification (RFID) is a technology for tracking and identifying objects, creatures or people even in a harsh environment. this technology makes use of a electromagnetic field generated by an RFID reader to identify and locate tags attached to objects. With its advantages such as long-range sensing, high mobility, and overall cost effectiveness, RFID has been widely adopted by the industrial Internet of things (IoT) industry. Recently, RFID based deep learning methods are emerging as promising solutions for industrial IoT applications. Deep learning, a type of artificial intelligence, uses large datasets to accurately and quickly identify objects and patterns. By combining this technology with RFID, industrial IoT applications can address challenges such as efficient resource management, production optimization, quality assurance and maintenance. RFID based deep learning methods have already been implemented in various industries such as manufacturing and logistics, wherein it efficiently tracks and monitors resources, processes, and personnel. With the advancement of this technology, future industrial IoT applications can offer greater efficiency, scalability, and reliability. © 2023 IEEE.

关键词： Internet of things

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optimization of Pose Recognition algorithms in Smart Wearable Devices 4

Optimization of Pose Recognition Algorithms in Smart Wearabl...

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4th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Luo, Mingze Huang, Jueqiao Zheng, Bowen Lin, Guofei Zhao, Tianze Cao, Kangjie School of Electronic Science and Engineering Southeast University Jiangsu Nanjing China Minzu University of China Department of Computer Science and Technology China Fok Ying Tung Graduate School Hong Kong University of Science and Technology Hong Kong School of Accounting and Finance Jiageng College Xiamen University Fujian Zhangzhou China Minzu University of China Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350343113

With the surge in popularity of smart wearable devices, the necessity for pose recognition has become paramount, especially in fields like motion analysis, health monitoring, and virtual reality. Yet, achieving efficient and precise pose recognition on devices with limited resources poses significant hurdles. Addressing this, we introduce an innovative optimization method for pose recognition algorithms, harnessing the power of the C++ compiler. Our approach uniquely integrates traditional machine learning paradigms with model simplification techniques. Furthermore, by exploiting C++ functionalities such as inline assembly, template metaprogramming, and compile-time optimization, we craft an optimization tailored explicitly for wearable hardware. Evaluations conducted on various devices testify to our method's superiority, marking a 10% surge in accuracy, 30% boost in efficiency, and a 15% dip in energy consumption relative to contemporary methods. Apart from achieving stellar accuracy and real-time performance, our method distinguishes itself in resource efficiency and scalability. this article offers an in-depth exploration of our design philosophies, optimization tactics, experimentation methodologies, and seminal discoveries, signposting the future trajectory of pose recognition in smart wearables and highlighting its commercial potential. © 2023 IEEE.

关键词： Machine learning

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Clamping Force Prediction of Different Fastener Connections Using Machine learning 4

Clamping Force Prediction of Different Fastener Connections ...

引用

4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Tim, Hertzschuch Martin, Baumgarten Anika, Lamm Volker, Johne Johannes, Koal Hans-Christian, Schmale TU Dresden Dresden Germany

ISBN: (纸本)9798350391183

Fasteners are widely used elements for joining sheet metal. Real-Time monitoring is desired to predict the quality respectively a sufficient clamping force between the joining partners. Usually, measured process signals are evaluated through expert systems or control windows. these methods are restricted to one specific use case and need to be reevaluated for every new joint combination. Often those systems can only determine significant deviations from the reference curve. the quality criteria of the joint, the clamping force, is usually not predicted and a process monitoring using ultrasonic testing is time consuming and highly cost intensive. Another possibility is quality prediction using machine learning algorithms to detect patterns in the process signals which correlate with the quality criteria. Many studies introduced machine learning algorithms to detect outlier curves or different failure types. therefore, this paper presents an approach of unifying the shape of different torque-Angle-curves from different fastener applications while simultaneously achieving high accuracy on the prediction of the clamping force with this novel method. © 2024 IEEE.

关键词： Ultrasonic testing

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Parameter optimization for Long Short-Term Memory (LSTM) and Bi-LSTM in Netflix Recommendation System 4

Parameter Optimization for Long Short-Term Memory (LSTM) and...

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4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Nafiyanto, Mohamad Irfan Setiawan, Erwin Budi School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331506490

Watching movies is a popular activity enjoyed by many individuals. Netflix is a leading entertainment platform that offers a vast selection of movies. this research aims to enhance the recommendation system by integrating Content-Based Filtering (CB) with Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) to assist users in discovering movies that align with their preferences. Additionally, three algorithms were used to optimize each method with the most suitable parameter to determine the appropriate method for this data. this research aims to evaluate the optimization's performance using the optimal parameter for each method. Several scenarios have been run using 854 movies and 34,086 movie reviews provided by 44 active Twitter users. the study's results indicate that the movie recommendation system that utilizes CB with LSTM classification, applying SMOTE and SGD optimization, achieves the highest accuracy of 87.28%. this represents an increase of 13.88% over the baseline. Additionally, the highest accuracy of 87.28% was achieved through Bi-LSTM classification using SMOTE and SGD optimization, representing a significant increase of 13.99% over the baseline. this demonstrates that CB can be combined with LSTM and Bi-LSTM to achieve high accuracy and improved results. © 2024 IEEE.

关键词： Content based retrieval

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Simulation and research of underground cable echo optimization based on machine learning and radar 4

Simulation and research of underground cable echo optimizati...

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4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Meng, Qiushi Xu, Yan Hu, Ran Li, Hanming Liu, Fei Chen, Jiabao Xue, Xiaolan Guangdong Power Grid Co. Ltd. Csg Guangzhou Power Supply Bureau Guangdong Guangzhou China

ISBN: (纸本)9798350394375

Ground Penetrating Radar (GPR) forward modeling is crucial in interpreting real radar data and conducting full waveform inversion. To address the limitations of traditional GPR forward simulations, such as extensive computational demands and inefficiency for real-time detection, we propose a near real-time GPR forward simulation method utilizing a machine learning framework. this method is specifically designed for underground cable detection scenarios, including cable trenches, cable banks, or pipelines. the model considers cable radius and burial depth as key parameters. We employ Principal Component Analysis (PCA) to compress the echo data, deriving principal component weight coefficients. these coefficients serve as outputs for the machine learning network. Our approach features a multi-layer cyclic network architecture, coupled with a learning strategy based on Random Forest algorithms. this design aims to thoroughly examine the relationships between model parameters and principal components, enabling swift GPR simulations through machine learning. Furthermore, we integrate a Deep Neural Network with the Random Forest approach. this combination, which takes the principal component coefficients of echo data as input, establishes a scene parameter prediction model grounded in machine learning. through inversion simulation and error analysis, we achieved a maximum depth and radius measurement error of 2 cm, with a standard deviation under 1 cm, validating the method's effectiveness. © 2024 IEEE.

关键词： Network architecture

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Advancement of Drug Discovery Using Artificial Intelligence and Machine learning

Advancement of Drug Discovery Using Artificial Intelligence ...

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2024 IEEE international conference on Computing, applications and Systems, COMPAS 2024

作者： Nilima, Sadia Islam Hossain, Md Azhad Sharmin, Sadia Rahman, Rukshanda Esa, Hammed Manik, Mia Md Tofayel Gonee Hasan, Rakibul International American University Department of Business Administration 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States International American University Department of Information Technology Wilshire Blvd STE 1000 Los AngelesCA90010 United States Westcliff University Department of Computer Science 17877 Von Karman Ave 4th Floor IrvineCA92614 United States Westcliff University Department of Business Administration 17877 Von Karman Ave 4th Floor IrvineCA92614 United States

ISBN: (纸本)9798331529765

the current boom in artificial intelligence and machine learning has significantly reduced human labor and greatly improved quality of life. this paper explores how AI and ML are being applied to streamline drug discovery and development, enhancing both efficiency and accuracy. Nevertheless, the limited effectiveness, distribution to unintended targets, time-consuming nature, and exorbitant expenses provide significant obstacles and difficulties that affect the process of drug design and development. the study involved an extensive literature review of numerous research articles and effectively highlight and discuss about the application of various advanced algorithms for drug discovery. the study highlighted how AI and ML contribute to reducing the time and cost associated with drug development and discovery. these technologies allow for the use of simulations, which can potentially obviate the need for clinical trials, and enable researchers to conduct extensive studies on various compounds without experimental trials. the results illustrate the extensive application of AI and ML in drug discovery, indicating a promising future for these technologies. they hold significant potential for further exploration and application in drug discovery and development by researchers, students, and the pharmaceutical industry. Finally, the progress in AI and deep learning presents a promising chance for the rational development and exploration of drugs, ultimately benefiting humanity. © 2024 IEEE.

关键词： Drug discovery

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