Rizwan Iqbal^1,*, Rashid Hussain², Sheeraz Arif³, Nadia Mustaqim Ansari⁴, Tayyab Ahmed Shaikh²

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1575-1598, 2023, DOI:10.32604/cmc.2023.042432

Abstract Software-Defined Networking (SDN) is a new network technology that uses programming to complement the data plane with a control plane. To enable safe connection, however, numerous security challenges must be addressed. Flooding attacks have been one of the most prominent risks on the internet for decades, and they are now becoming challenging difficulties in SDN networks. To solve these challenges, we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system. This study offers a systematic strategy for wrapping up the examination of SDN operations.… More >

Ramiz Gorkem Birdal^*, Ahmet Sertbas

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2727-2744, 2023, DOI:10.32604/cmc.2023.032069

Abstract Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’ walking patterns to be recognized. Existing research in this area has primarily focused on feature analysis through the extraction of individual features, which captures most of the information but fails to capture subtle variations in gait dynamics. Therefore, a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced. The gait features extracted from body halves divided by anatomical planes on vertical, horizontal, and diagonal axes are grouped to form… 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 >

Mesfer Al Duhayyim^1,*, Sidra Abbas^2,*, Abdullah Al Hejaili³, Natalia Kryvinska⁴, Ahmad Almadhor⁵, Uzma Ghulam Mohammad⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 413-429, 2023, DOI:10.32604/csse.2023.037127

Abstract Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain. It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity. Strokes can range from minor to severe (extensive). Thus, early stroke assessment and treatment can enhance survival rates. Manual prediction is extremely time and resource intensive. Automated prediction methods such as Modern Information and Communication Technologies (ICTs), particularly those in Machine Learning (ML) area, are crucial for the early diagnosis and prognosis of stroke. Therefore, this research proposed an ensemble voting… More >

Hélène Mathian¹, Lena Sanders²

Revue Internationale de Géomatique, Vol.31, No.1, pp. 21-45, 2022, DOI:10.3166/RIG31.21-45

Abstract The aim of this article is to compare a statistical approach, “geometric data analysis” (GDA), and a simulation approach, the multi-agent systems (MAS), considered as representative, respectively, of a numerical and a symbolic approach of modelling. The case study concerns segregation of scholar space in the Parisian area. First the different steps leading from a thematic question to the development of an operational model to analyze this question are presented. The central and essential role of a conceptual framework at the interface of both is shown. Indeed, before operationalisation, it is necessary to verify the compatibility between the theoretical backgrounds… More >

Meilin Wu^1,2, Lianggui Tang^1,2,*, Qingda Zhang^1,2, Ke Yan^1,2

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684

Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then… More >

Xudong Wang^1,2, Xueshuai Qin¹, Huiyan Zhang^2,*, Luis Ismael Minchala³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 385-400, 2023, DOI:10.32604/iasc.2023.036633

Abstract Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive… More >

Aisha Fayomi¹, Christophe Chesneau^2,*, Farrukh Jamal³, Ali Algarni¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2371-2403, 2023, DOI:10.32604/cmes.2023.027000

Abstract In this article, we highlight a new three-parameter heavy-tailed lifetime distribution that aims to extend the modeling possibilities of the Lomax distribution. It is called the extended Lomax distribution. The considered distribution naturally appears as the distribution of a transformation of a random variable following the logweighted power distribution recently introduced for percentage or proportion data analysis purposes. As a result, its cumulative distribution has the same functional basis as that of the Lomax distribution, but with a novel special logarithmic term depending on several parameters. The modulation of this logarithmic term reveals new types of asymetrical shapes, implying a… More >

Muhammad Umair^1,*, Zafar Saeed¹, Faisal Saeed², Hiba Ishtiaq¹, Muhammad Zubair¹, Hala Abdel Hameed^3,4

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5431-5446, 2023, DOI:10.32604/cmc.2023.033884

Abstract As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and… More >

P. Saranya^*, P. Asha

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1137-1149, 2023, DOI:10.32604/iasc.2023.031470

Abstract In today’s growing modern world environment, as human food activities are changing, it is affecting human health, thus leading to diseases like cancer. Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death. So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observation, which has become necessary to classify the type in cancer research. The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment. This paper introduces a… More >