Muhammad Rizwan^1,2, Muhammad Faheem Mushtaq¹, Maryam Rafiq², Arif Mehmood³, Isabel de la Torre Diez⁴, Monica Gracia Villar^5,6,7, Helena Garay^5,8,9, Imran Ashraf^10,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2047-2066, 2024, DOI:10.32604/cmc.2024.037347

Abstract Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter… More >

Wenlong Zhu^1,2,*, Yu Miao¹, Shuangshuang Yang³, Zuozheng Lian^1,2, Lianhe Cui¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3095-3117, 2023, DOI:10.32604/cmc.2023.045646

Abstract Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose a node feature-aware voting… More >

Harsh Mankodiya¹, Priyal Palkhiwala¹, Rajesh Gupta^1,*, Nilesh Kumar Jadav¹, Sudeep Tanwar¹, Osama Alfarraj², Amr Tolba², Maria Simona Raboaca^3,4,*, Verdes Marina⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1123-1142, 2023, DOI:10.32604/cmc.2023.038556

Abstract The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online… More >

Shadi Al-Sarawi¹, Mohammed Anbar^1,*, Basim Ahmad Alabsi², Mohammad Adnan Aladaileh³, Shaza Dawood Ahmed Rihan²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 491-515, 2023, DOI:10.32604/cmc.2023.041108

Abstract The Internet of Things (IoT) consists of interconnected smart devices communicating and collecting data. The Routing Protocol for Low-Power and Lossy Networks (RPL) is the standard protocol for Internet Protocol Version 6 (IPv6) in the IoT. However, RPL is vulnerable to various attacks, including the sinkhole attack, which disrupts the network by manipulating routing information. This paper proposes the Unweighted Voting Method (UVM) for sinkhole node identification, utilizing three key behavioral indicators: DODAG Information Object (DIO) Transaction Frequency, Rank Harmony, and Power Consumption. These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant… More >

Mahmoud Ragab^1,2,*, Sultanah M. Alshammari^2,3, Abdullah S. Al-Malaise Al-Ghamdi^2,4

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2497-2512, 2023, DOI:10.32604/csse.2023.041446

Abstract The Internet of Things (IoT) system has confronted dramatic growth in high dimensionality and data traffic. The system named intrusion detection systems (IDS) is broadly utilized for the enhancement of security posture in an IT infrastructure. An IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive hackers. Nowadays, machine learning (ML)-related techniques were used for detecting intrusion in IoTs IDSs. But, the IoT IDS mechanism faces significant challenges because of physical and functional diversity. Such IoT features use every attribute and feature for IDS self-protection unrealistic and difficult. This study… More >

P. Sabitha^*, G. Meeragandhi

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1983-2000, 2023, DOI:10.32604/iasc.2023.034402

Abstract Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet) to classify the H&E stained… More >

Mesfer Al Duhayyim^1,*, Sidra Abbas², Abdullah Al Hejaili³, Natalia Kryvinska^4,*, Ahmad Almadhor⁵, Huma Mughal⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 823-842, 2023, DOI:10.32604/csse.2023.037488

Abstract : Cardiotocography (CTG) represents the fetus’s health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective:: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method:: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories:… More >

Hend Alshede^1,2,*, Laila Nassef¹, Nahed Alowidi¹, Etimad Fadel¹

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3257-3278, 2023, DOI:10.32604/iasc.2023.035874

Abstract Advanced Metering Infrastructure (AMI) is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center. The massive amount of data collected supports the real-time decision-making required for diverse applications. The communication infrastructure relies on different network types, including the Internet. This makes the infrastructure vulnerable to various attacks, which could compromise security or have devastating effects. However, traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks. The objective of this paper is to develop an Anomaly Detection System (ADS) based on deep learning using the… More >

Ushaa Eswaran^1,*, S. Anand²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1917-1928, 2023, DOI:10.32604/csse.2023.032523

Abstract Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance according to the combined models… More >

Doaa Sami Khafaga¹, Faten Khalid Karim^1,*, Abdelaziz A. Abdelhamid^2,3, El-Sayed M. El-kenawy⁴, Hend K. Alkahtani¹, Nima Khodadadi⁵, Mohammed Hadwan⁶, Abdelhameed Ibrahim⁷

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3183-3198, 2023, DOI:10.32604/cmc.2023.033513

Abstract Managing physical objects in the network’s periphery is made possible by the Internet of Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible with these IoT devices, which exchange data to enable remote access. These attacks are often detected using intrusion detection methodologies, although these systems’ effectiveness and accuracy are subpar. This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization. The employed metaheuristic optimizer is a new version of the whale optimization algorithm (WOA), which is guided by the dipper throated optimizer (DTO) to improve… More >