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S. Prabu, K. Joseph Abraham Sundar^*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2071-2086, 2023, DOI:10.32604/iasc.2023.029105
Abstract Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain… More >

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451

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Yu Shih, Chien-Chih Kuo, Ching-Hung Lee^*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2087-2099, 2023, DOI:10.32604/iasc.2023.027659
Abstract The recent trends in Industry 4.0 and Internet of Things have encouraged many factory managers to improve inspection processes to achieve automation and high detection rates. However, the corresponding cost results of sample tests are still used for quality control. A low-cost automated optical inspection system that can be integrated with production lines to fully inspect products without adjustments is introduced herein. The corresponding mechanism design enables each product to maintain a fixed position and orientation during inspection to accelerate the inspection process. The proposed system combines image recognition and deep learning to measure the dimensions of the thread and… More >

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564

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R. Rajakumar^1,*, S. Sathiya Devi²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2101-2116, 2023, DOI:10.32604/iasc.2023.028889
Abstract Due to the advancements in information technologies, massive quantity of data is being produced by social media, smartphones, and sensor devices. The investigation of data stream by the use of machine learning (ML) approaches to address regression, prediction, and classification problems have received considerable interest. At the same time, the detection of anomalies or outliers and feature selection (FS) processes becomes important. This study develops an outlier detection with feature selection technique for streaming data classification, named ODFST-SDC technique. Initially, streaming data is pre-processed in two ways namely categorical encoding and null value removal. In addition, Local Correlation Integral (LOCI)… More >

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394

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Anas Quteishat^1,2,*, M. A. A. Younis²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2117-2134, 2023, DOI:10.32604/iasc.2023.029419
Abstract Renewable energy is created by renewable natural resources such as geothermal heat, sunlight, tides, rain, and wind. Energy resources are vital for all countries in terms of their economies and politics. As a result, selecting the optimal option for any country is critical in terms of energy investments. Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming. In the present work, the authors suggest fuzzy multi-characteristic decision-making approaches for renewable energy source selection, and fuzzy set theory is a valuable methodology for dealing with uncertainty in… More >

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442

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Reem Alkanhel¹, Mohamed Abouhawwash^2,3, S. N. Sangeethaa⁴, K. Venkatachalam⁵, Doaa Sami Khafaga^6,*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2135-2149, 2023, DOI:10.32604/iasc.2023.028852
Abstract Real-time applications based on Wireless Sensor Network (WSN) technologies are quickly increasing due to intelligent surroundings. Among the most significant resources in the WSN are battery power and security. Clustering strategies improve the power factor and secure the WSN environment. It takes more electricity to forward data in a WSN. Though numerous clustering methods have been developed to provide energy consumption, there is indeed a risk of unequal load balancing, resulting in a decrease in the network’s lifetime due to network inequalities and less security. These possibilities arise due to the cluster head’s limited life span. These cluster heads (CH)… More >

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378

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Wahidul Hasan Abir¹, Faria Rahman Khanam¹, Kazi Nabiul Alam¹, Myriam Hadjouni², Hela Elmannai³, Sami Bourouis⁴, Rajesh Dey⁵, Mohammad Monirujjaman Khan^1,*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2151-2169, 2023, DOI:10.32604/iasc.2023.029653
Abstract Nowadays, deepfake is wreaking havoc on society. Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos. Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information. These manipulated pictures and videos will undoubtedly have an enormous societal impact. Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human… More >

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S. Raguvaran^1,*, S. Anandamurugan², A. M. J. Md. Zubair Rahman³
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2171-2187, 2023, DOI:10.32604/iasc.2023.028605
Abstract A customized nutrition-rich diet plan is of utmost importance for cancer patients to intake healthy and nutritious foods that help them to be strong enough to maintain their body weight and body tissues. Consuming nutrition-rich diet foods will prevent them from the side effects caused before and after treatment thereby minimizing it. This work is proposed here to provide them with an effective diet assessment plan using deep learning-based automated medical diet system. Hence, an Enhanced Long-Short Term Memory (E-LSTM) has been proposed in this paper, especially for cancer patients. This proposed method will be very useful for cancer patients… More >

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456

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T. S. Balaji^1,2, S. Srinivasan^3,*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2189-2203, 2023, DOI:10.32604/iasc.2023.028785
Abstract The vehicular ad hoc network (VANET) is an emerging network technology that has gained popularity because to its low cost, flexibility, and seamless services. Software defined networking (SDN) technology plays a critical role in network administration in the future generation of VANET with fifth generation (5G) networks. Regardless of the benefits of VANET, energy economy and traffic control are significant architectural challenges. Accurate and real-time traffic flow prediction (TFP) becomes critical for managing traffic effectively in the VANET. SDN controllers are a critical issue in VANET, which has garnered much interest in recent years. With this objective, this study develops… More >

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447

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Saleem Mustafa¹, Arfan Jaffar¹, Muhammad Waseem Iqbal^2,*, Asma Abubakar², Abdullah S. Alshahrani³, Ahmed Alghamdi⁴
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549
Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of… More >

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Dalel Ben Ismail¹, Hela Elmannai^2,*, Souham Meshoul², Mohamed Saber Naceur¹
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2219-2236, 2023, DOI:10.32604/iasc.2023.028944
Abstract Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity information. Then, the maxillofacial anomalies… More >

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502

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Kazim Ali^1,*, Adnan N. Qureshi¹, Muhammad Shahid Bhatti², Abid Sohail², Mohammad Hijji³
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2237-2256, 2023, DOI:10.32604/iasc.2023.028810
Abstract Deep learning-based systems have succeeded in many computer vision tasks. However, it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks. These attacks can quickly spoil deep learning models, e.g., different convolutional neural networks (CNNs), used in various computer vision tasks from image classification to object detection. The adversarial examples are carefully designed by injecting a slight perturbation into the clean images. The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense, Generative Adversarial Network Defense, Deep Regret Analytic Generative Adversarial Networks Defense, Deep Denoising… More >

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520

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S. Prabu^1,*, J. M. Gnanasekar²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2257-2271, 2023, DOI:10.32604/iasc.2023.029877
Abstract Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video, first we analyze the images… More >

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618

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ARTICLE

A. Deepak Kumar^1,2,*, T. Sasipraba¹
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2273-2288, 2023, DOI:10.32604/iasc.2023.028996
Abstract Diabetic Retinopathy is a disease, which happens due to abnormal growth of blood vessels that causes spots on the vision and vision loss. Various techniques are applied to identify the disease in the early stage with different methods and parameters. Machine Learning (ML) techniques are used for analyzing the images and finding out the location of the disease. The restriction of the ML is a dataset size, which is used for model evaluation. This problem has been overcome by using an augmentation method by generating larger datasets with multidimensional features. Existing models are using only one augmentation technique, which produces… More >

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507

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Jazem Mutared Alanazi, Ahmad Ali AlZubi^*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2289-2308, 2023, DOI:10.32604/iasc.2023.029181
Abstract Accounting Information System (AIS), which is the foundation of any enterprise resource planning (ERP) system, is often built as centralized system. The technologies that allow the Internet-of-Value, which is built on five aspects that are network, algorithms, distributed ledger, transfers, and assets, are based on blockchain. Cryptography and consensus protocols boost the blockchain platform implementation, acting as a deterrent to cyber-attacks and hacks. Blockchain platforms foster innovation among supply chain participants, resulting in ecosystem development. Traditional business processes have been severely disrupted by blockchains since apps and transactions that previously required centralized structures or trusted third-parties to authenticate them may… More >

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551

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Srilekha Muthukaruppankaruppiah^1,*, Shanker Rajendiran Nagalingam², Priya Murugasen³, Rajesh Nandaamarnath⁴
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2309-2323, 2023, DOI:10.32604/iasc.2023.029598
Abstract Malfunction of human liver happens due to non-alcoholic fatty liver. Fatty liver measurement is used for grading hepatic steatosis, fibrosis and cirrhosis. The various imaging techniques for measuring fatty liver are Magnetic Resonance Imaging, Ultrasound and Computed Tomography. Imaging modalities lead to the exposure of harmful radiation of electromagnetic waves because of frequent measurement. The continuous monitoring of fatty liver is never achieved through imaging techniques. In this paper, the human fatty liver measured through a Fatty Liver Sensor (FLS). The continuous monitoring of the fatty liver is achieved through the FLS. FLS is fabricated through the screen-printing with materials… More >

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532

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R. Senthil Rama¹, C. R. Edwin Selva Rex², N. Herald Anantha Rufus^3,*, J. Annrose⁴
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2325-2336, 2023, DOI:10.32604/iasc.2023.028930
Abstract Any nonlinear behavior of the system is analyzed by a useful way of Total Harmonic Distortion (THD) technique. Reduced THD achieves lower peak current, higher efficiency and longer equipment life span. Simulated annealing (SA) is applied due to the effectiveness of locating solutions that are close to ideal and to challenge large-scale combinatorial optimization for Permanent Magnet Synchronous Machine (PMSM). The parameters of direct torque controllers (DTC) for the drive are automatically adjusted by the optimization algorithm. Advantages of the PI-Fuzzy-SA algorithm are retained when used together. It also improves the rate of system convergence. Speed response improvement and harmonic… More >

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585

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S. M. Udhaya Sankar^1,*, Mary Subaja Christo², P. S. Uma Priyadarsini³
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2337-2351, 2023, DOI:10.32604/iasc.2023.030278
Abstract Energy conservation has become a significant consideration in wireless sensor networks (WSN). In the sensor network, the sensor nodes have internal batteries, and as a result, they expire after a certain period. As a result, expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efficient way remains a challenge. Also, the clustering strategy employs to enhance or extend the life cycle of WSNs. We identify the supervisory head node (SH) or cluster head (CH) in every grouping considered the feasible strategy for power-saving route discovery in the clustering model, which diminishes the communication… More >

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667

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B. Chellapraba^1,*, D. Manohari², K. Periyakaruppan³, M. S. Kavitha⁴
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2353-2366, 2023, DOI:10.32604/iasc.2023.029923
Abstract Hyperspectral (HS) image classification is a hot research area due to challenging issues such as existence of high dimensionality, restricted training data, etc. Precise recognition of features from the HS images is important for effective classification outcomes. Additionally, the recent advancements of deep learning (DL) models make it possible in several application areas. In addition, the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics. In this view, this article develops an automated red deer algorithm with deep learning enabled hyperspectral image (HSI) classification (RDADL-HIC) technique. The proposed… More >

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602

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S. Sathish Kumar^1,*, G. Ravi²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2367-2382, 2023, DOI:10.32604/iasc.2023.030054
Abstract Due to the scattered nature of the network, data transmission in a distributed Mobile Ad-hoc Network (MANET) consumes more energy resources (ER) than in a centralized network, resulting in a shorter network lifespan (NL). As a result, we build an Enhanced Opportunistic Routing (EORP) protocol architecture in order to address the issues raised before. This proposed routing protocol goal is to manage the routing cost by employing power, load, and delay to manage the routing energy consumption based on the flooding of control packets from the target node. According to the goal of the proposed protocol technique, it is possible… More >

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423

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Nithya Rekha Sivakumar^*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2383-2398, 2023, DOI:10.32604/iasc.2023.028574
Abstract Internet of things (IoT) has brought a greater transformation in healthcare sector thereby improving patient care, minimizing treatment costs. The present method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart healthcare system is introduced for COVID-19 pandemic disease diagnosis. The Collected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data or feature in a Cloud… More >

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634

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G. Rajasekaran^1,*, J. Raja Sekar²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2399-2412, 2023, DOI:10.32604/iasc.2023.029119
Abstract Abnormal behavior detection is challenging and one of the growing research areas in computer vision. The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events. In this work, Pyramidal Lucas Kanade algorithm is optimized using EMEHOs to achieve the objective. First stage, OPLKT-EMEHOs algorithm is used to generate the optical flow from MIIs. Second stage, the MIIs optical flow is applied as input to 3 layer CNN for detect the abnormal crowd behavior. University of Minnesota (UMN) dataset is used to evaluate the proposed system. The experimental result shows… More >

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V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2413-2429, 2023, DOI:10.32604/iasc.2023.030527
Abstract In recent days the usage of android smartphones has increased extensively by end-users. There are several applications in different categories banking/finance, social engineering, education, sports and fitness, and many more applications. The android stack is more vulnerable compared to other mobile platforms like IOS, Windows, or Blackberry because of the open-source platform. In the Existing system, malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware. The attackers target the victim with various attacks like adware, backdoor, spyware, ransomware, and zero-day exploits and create threat hunts on… More >

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Shyla^1,*, Vishal Bhatnagar²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2431-2447, 2023, DOI:10.32604/iasc.2023.029126
Abstract The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of… More >

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436

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R. Saravana Ram¹, M. Vinoth Kumar², Tareq M. Al-shami³, Mehedi Masud⁴, Hanan Aljuaid⁵, Mohamed Abouhawwash^6,7,*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2449-2462, 2023, DOI:10.32604/iasc.2023.030486
Abstract Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition. Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography. The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics. This paper has analyzed the features related to the computer vision of digital content to determine its integrity. This method has checked the computer vision features of the image frames… More >

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D. Anitha^1,*, R. A. Karthika²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2463-2477, 2023, DOI:10.32604/iasc.2023.023361
Abstract Achieving sound communication systems in Under Water Acoustic (UWA) environment remains challenging for researchers. The communication scheme is complex since these acoustic channels exhibit uneven characteristics such as long propagation delay and irregular Doppler shifts. The development of machine and deep learning algorithms has reduced the burden of achieving reliable and good communication schemes in the underwater acoustic environment. This paper proposes a novel intelligent selection method between the different modulation schemes such as Code Division Multiple Access(CDMA), Time Division Multiple Access(TDMA), and Orthogonal Frequency Division Multiplexing(OFDM) techniques using the hybrid combination of the convolutional neural networks(CNN) and ensemble single… More >

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481

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Thien Khai Tran¹, Kha Tu Huynh^2,*, Dac-Nhuong Le³, Muhammad Arif⁴, Hoa Minh Dinh¹
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2479-2491, 2023, DOI:10.32604/iasc.2023.029078
Abstract Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The… More >

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550

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V. Jesus Bobin^*, M. MarsalineBeno
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2493-2511, 2023, DOI:10.32604/iasc.2023.024679
Abstract Three-phase induction motors are becoming increasingly utilized in industrial field due to their better efficiency and simple manufacture. The speed control of an induction motor is essential in a variety of applications, but it is difficult to control. This research analyses the three-phase induction motor’s performance using field-oriented control (FOC) and direct torque control (DTC) techniques. The major aim of this work is to provide a critical evaluation of developing a simple speed controller for induction motors with improving the performance of Induction Motor (IM). For controlling a motor, different optimization approaches are accessible; in this research, a Fuzzy Logic… More >

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524

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M. Prabhu^1,*, B. Muthu Kumar²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2513-2525, 2023, DOI:10.32604/iasc.2023.027823
Abstract Orthogonal Frequency-Division Multiplexing (OFDM) is the form of a digital system and a way of encoding digital data across multiple frequency components that are used in telecommunication services. Carrier Frequency Offset (CFO) inaccuracy is a major disadvantage of OFDM. This paper proposed a feasible and elegant fuzzy-based resource allocation technique, that overcomes the constraints of the CFO. The suggested Fuzzy linear CFO estimation (FL-CFO) not only estimates the CFO with increased precision but also allocates resources effectively, and achieves maximum utilization of dynamic resources. The suggested FL-CFO error estimation algorithm in OFDM systems employing 1-bit Quadrate errors ADC (1-bit QE)… More >

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Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2527-2540, 2023, DOI:10.32604/iasc.2023.030168
Abstract The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The model performance is verified with… More >

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S. Neelavathy Pari^1,*, K. Sudharson²
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2541-2554, 2023, DOI:10.32604/iasc.2023.030284
Abstract The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority. The intensity of the harm largely depends on the attacker’s intentions during hostile assaults. As a result, the loss of Information, power, or capacity may occur. The authors propose an Enhanced Trust-Based Secure Route Protocol (ETBSRP) using features extraction. First, the primary and secondary trust characteristics are retrieved and achieved routing using a calculation. The complete trust characteristic obtains by integrating all logical and physical trust from every node. To assure intermediate node trustworthiness, we designed an ETBSRP, and it calculates and… More >

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