V. Nirmala^1,*, B. Gomathy²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2585-2601, 2023, DOI:10.32604/csse.2023.027512

Abstract In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method based on Convolutional Neural Network… More >

Hadil Shaiba¹, Jaber S. Alzahrani², Majdy M. Eltahir³, Radwa Marzouk⁴, Heba Mohsen⁵, Manar Ahmed Hamza^6,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6425-6441, 2022, DOI:10.32604/cmc.2022.031625

Abstract Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc. In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages. The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content. In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs). Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since… More >

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 >

Magdy M. Fadel^1,*, Sally M. El-Ghamrawy², Amr M. T. Ali-Eldin¹, Mohammed K. Hassan³, Ali I. El-Desoky¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2293-2312, 2022, DOI:10.32604/cmc.2022.028287

Abstract Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of failure, if it fails, the… More >

Rasha Almarshdi^1,2,*, Laila Nassef¹, Etimad Fadel¹, Nahed Alowidi¹

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 297-320, 2023, DOI:10.32604/iasc.2023.026799

Abstract Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security attacks accurately. This paper develops an IDS architecture based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) deep learning algorithms. We implement our model… More >

Radwa Marzouk¹, Fadwa Alrowais², Noha Negm³, Mimouna Abdullah Alkhonaini⁴, Manar Ahmed Hamza^5,*, Mohammed Rizwanullah⁵, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3763-3775, 2022, DOI:10.32604/cmc.2022.027483

Abstract Industrial Internet of Things (IIoT) is an emerging field which connects digital equipment as well as services to physical systems. Intrusion detection systems (IDS) can be designed to protect the system from intrusions or attacks. In this view, this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection (HDL-MEID) technique for clustered IIoT environments. The HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure communication. Primarily, the HDL-MEID technique designs a new chaotic mayfly optimization (CMFO) based clustering approach for the effective choice of the Cluster Heads (CH) and organize clusters. Moreover,… More >

Ashit Kumar Dutta¹, Jenyfal Sampson², Sultan Ahmad³, T. Avudaiappan⁴, Kanagaraj Narayanasamy^5,*, Irina V. Pustokhina⁶, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1157-1172, 2022, DOI:10.32604/cmc.2022.024109

Abstract Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell… More >

A. Saranya^1,*, R. Anandan²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1167-1182, 2022, DOI:10.32604/iasc.2022.023445

Abstract Hereditary Autism Spectrum Disorder (ASD) is a neuron disorder that affects a person's ability for communication, interaction, and also behaviors. Diagnostics of autism are available throughout all stages of life, from infancy through adolescence and adulthood. Facial Emotions detection is considered to be the most parameter for the detection of Autismdisorders among the different categories of people. Propelled with a machine and deep learning algorithms, detection of autism disorder using facial emotions has reached a new dimension and has even been considered as the precautionary warning system for caregivers. Since Facial emotions are limited to only seven expressions, detection of… More >

M. Maheswari^1,2,*, R. A. Karthika¹

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 365-382, 2022, DOI:10.32604/iasc.2022.022259

Abstract With the advent of wireless communication and digital technology, low power, Internet-enabled, and reconfigurable wireless devices have been developed, which revolutionized day-to-day human life and the economy across the globe. These devices are realized by leveraging the features of sensing, processing the data and nodes communications. The scale of Internet-enabled wireless devices has increased daily, and these devices are exposed to various cyber-attacks. Since the complexity and dynamics of the attacks on the devices are computationally high, intelligent, scalable and high-speed intrusion detection systems (IDS) are required. Moreover, the wireless devices are battery-driven; implementing them would consume more energy, weakening… More >

S. Nithyanantham^1,*, G. Singaravel²

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1905-1919, 2022, DOI:10.32604/iasc.2022.022499

Abstract In recent times, a huge amount of data is being created from different sources and the size of the data generated on the Internet has already surpassed two Exabytes. Big Data processing and analysis can be employed in many disciplines which can aid the decision-making process with privacy preservation of users’ private data. To store large quantity of data, Geo-Distributed Data Centres (GDDC) are developed. In recent times, several applications comprising data analytics and machine learning have been designed for GDDC. In this view, this paper presents a hybrid deep learning framework for privacy preservation in distributed DCs. The proposed… More >