Samina Amin¹, Muhammad Ali Zeb¹, Hani Alshahrani^2,*, Mohammed Hamdi², Mohammad Alsulami², Asadullah Shaikh³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921

Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the… More >

Fares Alharbi¹, Reem Alshahrani², Mohammed Zakariah^3,*, Amjad Aldweesh¹, Abdulrahman Abdullah Alghamdi¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3697-3722, 2023, DOI:10.32604/cmc.2023.040086

Abstract Privacy and trust are significant issues in intelligent transportation systems (ITS). Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels, optical fiber, and blockchain technology. The Internet of Things (IoT) is a network of connected, interconnected gadgets. Privacy issues occasionally arise due to the amount of data generated. However, they have been primarily addressed by blockchain and smart contract technology. While there are still security issues with smart contracts, primarily due to the complexity of writing the code, there are still… More >

Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved… More >

Volkan Kaya¹, İsmail Akgül¹, Özge Zencir Tanır^2,*

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2519-2534, 2023, DOI:10.32604/phyton.2023.029811

Abstract Classification and monitoring of microalgae species in aquatic ecosystems are important for understanding population dynamics. However, manual classification of algae is a time-consuming method and requires a lot of effort with expertise due to the large number of families and genera in its classification. The recognition of microalgae species has become an increasingly important research area in image recognition in recent years. In this study, machine learning and deep learning methods were proposed to classify images of 12 different microalgae species in order to successfully classify algae cells. 8 Different novel models (MobileNetV3Small-Lr, MobileNetV3SmallRf, MobileNetV3Small-Xg, MobileNetV3Large-Lr, MobileNetV3Large-Rf, MobileNetV3Large-Xg, MobileNetV3Small-Improved and… More >

Abdus Saboor^1,4, Arif Hussain², Bless Lord Y. Agbley³, Amin ul Haq^3,*, Jian Ping Li³, Rajesh Kumar^1,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1325-1344, 2023, DOI:10.32604/iasc.2023.038849

Abstract Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic. With the objective of constructing an effective prediction model, both linear and machine learning tools have been investigated for the past couple of decades. In recent years, recurrent neural networks (RNNs) have been observed to perform well on tasks involving sequence-based data in many research domains. With this motivation, we investigated the performance of long-short term memory (LSTM) and gated recurrent units (GRU) and their combination with the attention mechanism; LSTM + Attention, GRU + Attention, and LSTM + GRU + Attention.… More >

Jason Elroy Martis¹, Sannidhan Manjaya Shetty^2,*, Manas Ranjan Pradhan³, Usha Desai⁴, Biswaranjan Acharya^5,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 915-938, 2023, DOI:10.32604/cmc.2023.038847

Abstract In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of modern technologies such as Generative Adversarial Networks (GANs), generating images using verbal and textual cues is now possible, resulting in more accurate sketch depictions. In this study, we propose an adversarial network that generates human facial sketches using such cues provided by an observer. Additionally, we… More >

Mohamed Abouhawwash^1,2, S. Sridevi³, Suma Christal Mary Sundararajan⁴, Rohit Pachlor⁵, Faten Khalid Karim⁶, Doaa Sami Khafaga^6,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 239-253, 2023, DOI:10.32604/csse.2023.037812

Abstract One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers. In… More >

Somasundaram Devaraj^*, Senthil Kumar Arunachalam

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1283-1299, 2023, DOI:10.32604/iasc.2023.033200

Abstract Retinal fundus images are used to discover many diseases. Several Machine learning algorithms are designed to identify the Glaucoma disease. But the accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time. In MPCNKFTRS-RBIC Model, the retinal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuan filter. Then, preprocessed retinal fundus is given for hidden… More >

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 >

K. M. Monica, R. Parvathi^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6229-6249, 2023, DOI:10.32604/cmc.2023.032273

Abstract Human Activity Recognition (HAR) has always been a difficult task to tackle. It is mainly used in security surveillance, human-computer interaction, and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things (IoT). Human Activity Recognition data can be recorded with the help of sensors, images, or smartphones. Recognizing daily routine-based human activities such as walking, standing, sitting, etc., could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network (2D CNN) MODEL, Long Short Term Memory (LSTM) Model, Bidirectional long short-term memory (Bi-LSTM) are used… More >