Home / Journals / IASC / Vol.36, No.2, 2023
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    ARTICLE

    An Automatic Deep Neural Network Model for Fingerprint Classification

    Amira Tarek Mahmoud1,*, Wael A. Awad2, Gamal Behery2, Mohamed Abouhawwash3,4, Mehedi Masud5, Hanan Aljuaid6, Ahmed Ismail Ebada7
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2007-2023, 2023, DOI:10.32604/iasc.2023.031692
    Abstract The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving… More >

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    ARTICLE

    Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

    Donghun Wang, Jonghyun Lee, Minchan Kim, Insoo Lee*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2025-2040, 2023, DOI:10.32604/iasc.2023.034749
    Abstract Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops. These batteries demonstrate several advantages, such as environmental friendliness, high energy density, and long life. However, battery overcharging and overdischarging may occur if the batteries are not monitored continuously. Overcharging causes fire and explosion casualties, and overdischarging causes a reduction in the battery capacity and life. In addition, the internal resistance of such batteries varies depending on their external temperature, electrolyte, cathode material, and other factors; the capacity of the batteries decreases with temperature. In this study, we develop a method for estimating the state of charge (SOC)… More >

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    ARTICLE

    Spectrum Sensing Using Optimized Deep Learning Techniques in Reconfigurable Embedded Systems

    Priyesh Kumar*, Ponniyin Selvan
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2041-2054, 2023, DOI:10.32604/iasc.2023.030291
    Abstract The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users. In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication. IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics. Implementing the above learning methods on the embedded chip leads… More >

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    ARTICLE

    Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

    A. Ramachandran1,*, P. Ramadevi2, Ahmed Alkhayyat3, Yousif Kerrar Yousif4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2055-2070, 2023, DOI:10.32604/iasc.2023.032942
    Abstract Nowadays, numerous applications are associated with cloud and user data gets collected globally and stored in cloud units. In addition to shared data storage, cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud, public cloud, community cloud and private cloud. Though cloud-based computing solutions are highly convenient to the users, it also brings a challenge i.e., security of the data shared. Hence, in current research paper, blockchain with data integrity authentication technique is developed for an efficient and secure operation with user authentication process. Blockchain technology is utilized in this study to enable… More >

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    ARTICLE

    Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

    R. B. Sarooraj*, S. Prayla Shyry
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2071-2084, 2023, DOI:10.32604/iasc.2023.034716
    Abstract In Intelligent Transportation Systems (ITS), controlling the traffic flow of a region in a city is the major challenge. Particularly, allocation of the traffic-free route to the taxi drivers during peak hours is one of the challenges to control the traffic flow. So, in this paper, the route between the taxi driver and pickup location or hotspot with the spatial-temporal dependencies is optimized. Initially, the hotspots in a region are clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to find the hot spots at the peak hours in an urban area. Then, the optimal route is… More >

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    ARTICLE

    Sensor-Based Gait Analysis for Parkinson’s Disease Prediction

    Sathya Bama B*, Bevish Jinila Y
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2085-2097, 2023, DOI:10.32604/iasc.2023.028481
    Abstract Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson’s… More >

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    ARTICLE

    Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

    G. Murugesan1,*, J. Jeyapriya2, M. Hemalatha3, S. Rajeshkannan4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2099-2109, 2023, DOI:10.32604/iasc.2023.032068
    Abstract The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of clusters (k = 3,… More >

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    ARTICLE

    Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

    Sakorn Mekruksavanich1, Anuchit Jitpattanakul2,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2111-2128, 2023, DOI:10.32604/iasc.2023.033542
    Abstract The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturing firm are vital for the rapid and accurate diagnosis of work performance, particularly during the training of a new worker. Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques. Despite widespread computer vision-based approaches, it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where camera deployment is problematic. Through the use of wearable inertial sensors, we propose a deep learning method for automatically recognizing the activities of construction workers. The… More >

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    ARTICLE

    Federated Blockchain Model for Cyber Intrusion Analysis in Smart Grid Networks

    N. Sundareswaran*, S. Sasirekha
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2129-2143, 2023, DOI:10.32604/iasc.2023.034381
    Abstract Smart internet of things (IoT) devices are used to manage domestic and industrial energy needs using sustainable and renewable energy sources. Due to cyber infiltration and a lack of transparency, the traditional transaction process is inefficient, unsafe and expensive. Smart grid systems are now efficient, safe and transparent owing to the development of blockchain (BC) technology and its smart contract (SC) solution. In this study, federated learning extreme gradient boosting (FL-XGB) framework has been developed along with BC to learn the intrusion inside the smart energy system. FL is best suited for a decentralized BC-enabled system to adapt learning models… More >

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    ARTICLE

    B-Spline-Based Curve Fitting to Cam Pitch Curve Using Reinforcement Learning

    Zhiwei Lin1, Tianding Chen1,*, Yingtao Jiang2, Hui Wang1, Shuqin Lin1, Ming Zhu2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2145-2164, 2023, DOI:10.32604/iasc.2023.035555
    Abstract Directly applying the B-spline interpolation function to process plate cams in a computer numerical control (CNC) system may produce verbose tool-path codes and unsmooth trajectories. This paper is devoted to addressing the problem of B-spline fitting for cam pitch curves. Considering that the B-spline curve needs to meet the motion law of the follower to approximate the pitch curve, we use the radial error to quantify the effects of the fitting B-spline curve and the pitch curve. The problem thus boils down to solving a difficult global optimization problem to find the numbers and positions of the control points or… More >

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    ARTICLE

    Shaped Offset Quadrature Phase Shift Keying Based Waveform for Fifth Generation Communication

    R. Ann Caroline Jenifer*, M. A. Bhagyaveni, V. Saroj Malini, M. Shanmugapriya
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2165-2176, 2023, DOI:10.32604/iasc.2023.031840
    Abstract Fifth generation (5G) wireless networks must meet the needs of emerging technologies like the Internet of Things (IoT), Vehicle-to-everything (V2X), Video on Demand (VoD) services, Device to Device communication (D2D) and many other bandwidth-hungry multimedia applications that connect a huge number of devices. 5G wireless networks demand better bandwidth efficiency, high data rates, low latency, and reduced spectral leakage. To meet these requirements, a suitable 5G waveform must be designed. In this work, a waveform namely Shaped Offset Quadrature Phase Shift Keying based Orthogonal Frequency Division Multiplexing (SOQPSK-OFDM) is proposed for 5G to provide bandwidth efficiency, reduced spectral leakage, and… More >

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    ARTICLE

    Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

    P. S. Ramapraba1,*, P. Epsiba2, K. Umapathy3, E. Sivanantham4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2177-2189, 2023, DOI:10.32604/iasc.2023.032040
    Abstract The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the lung region only, flood filling… More >

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    ARTICLE

    Implementation of High-Q Embedded Band Pass Filter in Wireless Communication

    V. Satheesh Kumar1,*, S. Ramesh2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2191-2200, 2023, DOI:10.32604/iasc.2023.021188
    Abstract At 12.8 MHz center frequency, the advanced miniaturized polymer-based planar high quality factor (Q) passive elements embedded bandpass filter works in the L-band. Because most of the demands operate inside the spectrum, the wideband or high-speed operation necessary to enhance must be acquired in microwave frequency ranges. The channel has a quiet, high-performance microfilter with wideband rejection. Capacitors and inductors are used in the high quality factor (Q) passive components, and related networks are incorporated in the filter. Embedded layers are concatenated using Three-Dimensional Integrated Circuit (3D-IC) integration, parasitics are removed, and interconnection losses are negotiated using de-embedding methods. A… More >

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    ARTICLE

    A Sensor-less Surface Mounted PMSM for Electronic Speed Control in Multilevel Inverter

    S. Dinesh Kumar1,*, A. Jagadeeshwaran2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2201-2215, 2023, DOI:10.32604/iasc.2023.027467
    Abstract Recent advancements in power electronics technology evolves inverter fed electric motors. Speed signals and rotor position are essential for controlling an electric motor accurately. In this paper, the sensorless speed control of surface-mounted permanent magnet synchronous motor (SPMSM) has been attempted. SPMSM wants a digital inverter for its precise working. Hence, this study incorporates fifteen level inverter to the SPMSM. A sliding mode observer (SMO) based sensorless speed control scheme is projected to determine rotor spot and speed of the multilevel inverter (MLI) fed SPMSM. MLI has been operated using a multi carrier pulse width modulation (MCPWM) strategy for generation… More >

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    ARTICLE

    A Broker-Based Task-Scheduling Mechanism Using Replication Approach for Cloud Systems

    Abdulelah Alwabel*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2217-2232, 2023, DOI:10.32604/iasc.2023.033703
    Abstract The reliability and availability of cloud systems have become major concerns of service providers, brokers, and end-users. Therefore, studying fault-tolerance mechanisms in cloud computing attracts intense attention in industry and academia. The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems. A task-scheduling mechanism distributes tasks to a group of instances to be executed. Much work has been undertaken in this direction to improve the overall outcome of cloud computing, such as improving service quality and reducing power consumption. However, little work on task scheduling has studied the problem of lost tasks from the broker’s perspective. Task loss can… More >

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    ARTICLE

    Drone for Dynamic Monitoring and Tracking with Intelligent Image Analysis

    Ching-Bang Yao1, Chang-Yi Kao2,*, Jiong-Ting Lin3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2233-2252, 2023, DOI:10.32604/iasc.2023.034488
    Abstract Traditional monitoring systems that are used in shopping malls or community management, mostly use a remote control to monitor and track specific objects; therefore, it is often impossible to effectively monitor the entire environment. When finding a suspicious person, the tracked object cannot be locked in time for tracking. This research replaces the traditional fixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person. This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system. In this article, we proposed… More >

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    ARTICLE

    New Ranking of Generalized Quadrilateral Shape Fuzzy Number Using Centroid Technique

    A. Thiruppathi*, C. K. Kirubhashankar
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2253-2266, 2023, DOI:10.32604/iasc.2023.033870
    Abstract The output of the fuzzy set is reduced by one for the defuzzification procedure. It is employed to provide a comprehensible outcome from a fuzzy inference process. This page provides further information about the defuzzification approach for quadrilateral fuzzy numbers, which may be used to convert them into discrete values. Defuzzification demonstrates how useful fuzzy ranking systems can be. Our major purpose is to develop a new ranking method for generalized quadrilateral fuzzy numbers. The primary objective of the research is to provide a novel approach to the accurate evaluation of various kinds of fuzzy integers. Fuzzy ranking properties are… More >

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    ARTICLE

    Enhanced Rsa (Ersa): An Advanced Mechanism for Improving the Security

    S. Castro1,*, R. PushpaLakshmi2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2267-2279, 2023, DOI:10.32604/iasc.2023.032222
    Abstract Cloud computing has become ubiquitous in our daily lives in recent years. Data are the source of technology that is generated hugely by various sources. Big data is dealing with huge data volumes or complex data. The major concern in big data is security threats. Security concerns create a negative impact on the user on the aspect of trust. In big data still, security threats exist as commonly known as DDOS (Distributed-Denial-of-Service) attacks, data loss, Inadequate Data Backups, System Vulnerabilities, and Phishing as well as Social Engineering Attacks. In our work, we have taken the data loss and Inadequate Data… More >

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    ARTICLE

    Interpretive Structural Modeling Based Assessment and Optimization of Cloud with Internet of Things (CloudIoT) Issues Through Effective Scheduling

    Anju Shukla1, Mohammad Zubair Khan2, Shishir Kumar3,*, Abdulrahman Alahmadi2, Reem Ibrahim A. Altamimi2, Ahmed H. Alahmadi2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2281-2297, 2023, DOI:10.32604/iasc.2023.031931
    Abstract Integrated CloudIoT is an emerging field of study that integrates the Cloud and the Internet of Things (IoT) to make machines smarter and deal with real-world objects in a distributed manner. It collects data from various devices and analyses it to increase efficiency and productivity. Because Cloud and IoT are complementary technologies with distinct areas of application, integrating them is difficult. This paper identifies various CloudIoT issues and analyzes them to make a relational model. The Interpretive Structural Modeling (ISM) approach establishes the interrelationship among the problems identified. The issues are categorised based on driving and dependent power, and a… More >

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    ARTICLE

    Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

    B. Karthikeyan1,*, K. Nithya2, Ahmed Alkhayyat3, Yousif Kerrar Yousif4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2299-2313, 2023, DOI:10.32604/iasc.2023.032585
    Abstract In today’s digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient’s health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System (SFLODL-DSS) is designed for the… More >

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    ARTICLE

    Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

    Dahyeon Kim1, Jiyoung Min1, Yongwoo Song1, Chulsu Kim2, Junho Ahn1,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2315-2331, 2023, DOI:10.32604/iasc.2023.034394
    Abstract Existing firefighting robots are focused on simple storage or fire suppression outside buildings rather than detection or recognition. Utilizing a large number of robots using expensive equipment is challenging. This study aims to increase the efficiency of search and rescue operations and the safety of firefighters by detecting and identifying the disaster site by recognizing collapsed areas, obstacles, and rescuers on-site. A fusion algorithm combining a camera and three-dimension light detection and ranging (3D LiDAR) is proposed to detect and localize the interiors of disaster sites. The algorithm detects obstacles by analyzing floor segmentation and edge patterns using a mask… More >

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    ARTICLE

    Smart Grid Communication Under Elliptic Curve Cryptography

    B. Prabakaran1,*, T. R. Sumithira2, V. Nagaraj3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2333-2347, 2023, DOI:10.32604/iasc.2023.029725
    Abstract Smart Grids (SGs) are introduced as a solution for standard power distribution. The significant capabilities of smart grids help to monitor consumer behaviors and power systems. However, the delay-sensitive network faces numerous challenges in which security and privacy gain more attention. Threats to transmitted messages, control over smart grid information and user privacy are the major concerns in smart grid security. Providing secure communication between the service provider and the user is the only possible solution for these security issues. So, this research work presents an efficient mutual authentication and key agreement protocol for smart grid communication using elliptic curve… More >

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    ARTICLE

    Computing Connected Resolvability of Graphs Using Binary Enhanced Harris Hawks Optimization

    Basma Mohamed1,*, Linda Mohaisen2, Mohamed Amin1
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2349-2361, 2023, DOI:10.32604/iasc.2023.032930
    Abstract In this paper, we consider the NP-hard problem of finding the minimum connected resolving set of graphs. A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B. A resolving set B of G is connected if the subgraph induced by B is a nontrivial connected subgraph of G. The cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of G. The problem is solved heuristically… More >

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    ARTICLE

    Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

    S. J. K. Jagadeesh Kumar1, P. Parthasarathi2, Mofreh A. Hogo3, Mehedi Masud4, Jehad F. Al-Amri5, Mohamed Abouhawwash6,7,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2363-2378, 2023, DOI:10.32604/iasc.2023.033800
    Abstract Women from middle age to old age are mostly screened positive for Breast cancer which leads to death. Times over the past decades, the overall survival rate in breast cancer has improved due to advancements in early-stage diagnosis and tailored therapy. Today all hospital brings high awareness and early detection technologies for breast cancer. This increases the survival rate of women. Though traditional breast cancer treatment takes so long, early cancer techniques require an automation system. This research provides a new methodology for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best characteristics.… More >

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    ARTICLE

    Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

    P. Ramya1,*, B. Karthik2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2379-2391, 2023, DOI:10.32604/iasc.2023.026291
    Abstract Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning. Mining core features and performing the text classification still exist as a challenging task. Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach. This paper presented the text document classification that has wide applications in information retrieval, which uses movie review datasets. Here the document indexing based on controlled vocabulary, adjective, word sense disambiguation, generating hierarchical categorization of web pages, spam detection, topic labeling, web search, document summarization, etc. Here the… More >

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    ARTICLE

    Hybrid Convolutional Neural Network for Plant Diseases Prediction

    S. Poornima1,*, N. Sripriya1, Adel Fahad Alrasheedi2, S. S. Askar2, Mohamed Abouhawwash3,4
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2393-2409, 2023, DOI:10.32604/iasc.2023.024820
    Abstract Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products. The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agriculture. Manual system to monitor the diseases in plant is time consuming and report a lot of errors. There is high demand for technology to detect the plant diseases automatically. Recently image processing approach and deep learning approach are highly invited in detection of plant diseases. The diseases like late blight, bacterial spots, spots on Septoria leaf and yellow leaf curved are widely found in plants.… More >

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    ARTICLE

    Human Factors While Using Head-Up-Display in Low Visibility Flying Conditions

    Jhulan Kumar1,2, Surender Singh Saini1,2, Divya Agrawal1,2, Vinod Karar1,2,*, Aman Kataria2
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2411-2423, 2023, DOI:10.32604/iasc.2023.034203
    Abstract Flying an aircraft in low visibility is still a challenging task for the pilot. It requires precise and accurate situational awareness (SA) in real-time. A Head-up Display (HUD) is used to project collimated internal and external flight information on a transparent screen in the pilot’s forward field of view, which eliminates the change of eye position between Head-Down-Display (HDD) instruments and outer view through the windshield. Implementation of HUD increases the SA and reduces the workload for the pilot. But to provide a better flying capability for the pilot, projecting extensive information on HUD causes human factor issues that reduce… More >

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    ARTICLE

    Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

    D. Palanikkumar1, R. Ramesh Kumar2, Mehedi Masud3, Mrim M. Alnfiai4, Mohamed Abouhawwash5,6,*
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2425-2440, 2023, DOI:10.32604/iasc.2023.033879
    Abstract The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage is not properly managed it will become a hazard to the environment and humans. Managing medical wastage is a major issue in the city, municipalities in the aspects of the environment, and logistics. An efficient supply chain with edge computing technology is used in managing medical waste. The supply chain operations include processing of waste collection, transportation, and disposal of waste. Many research works have been applied to improve the management of wastage. The main issues in the existing techniques are ineffective and expensive… More >

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    ARTICLE

    Fuzzy Feedback Control for Electro-Hydraulic Actuators

    Tan Nguyen Van1, Huy Q. Tran2,*, Vinh Xuan Ha3, Cheolkeun Ha4, Phu Huynh Minh1
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2441-2456, 2023, DOI:10.32604/iasc.2023.033368
    Abstract Electro-hydraulic actuators (EHA) have recently played a significant role in modern industrial applications, especially in systems requiring extremely high precision. This can be explained by EHA’s ability to precisely control the position and force through advanced sensors and innovative control algorithms. One of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms, in which the proportional–integral–derivative (PID) algorithm, fuzzy logic controller, and a hybrid of these methods are popular options. In this paper, we developed a novel version of the fuzzy control algorithm and linear feedback control method, namely fuzzy linear feedback… More >

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    ARTICLE

    Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

    J. Gobinath1,*, S. Hemajothi2, J. S. Leena Jasmine3
    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2457-2471, 2023, DOI:10.32604/iasc.2023.031171
    Abstract A Wireless Sensor Network (WSN) becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears impracticable. Concerning the energy production of the nodes, WSN has major issues that may influence the stability of the system. As a result, constructing WSN requires devising protocols and standards that make the most use of constrained capacity, especially the energy resources. WSN faces some issues with increased power utilization and an on going development due to the uneven energy usage between the nodes. Clustering has proven to be a more… More >

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