N. Dharini^1,*, Jeevaa Katiravan², S. M. Udhaya Sankar³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 249-264, 2023, DOI:10.32604/csse.2023.024419

Abstract This work utilizes a statistical approach of Principal Component Analysis (PCA) towards the detection of Methane (CH₄)-Carbon Monoxide (CO) Poisoning occurring in coal mines, forest fires, drainage systems etc. where the CH₄ and CO emissions are very high in closed buildings or confined spaces during oxidation processes. Both methane and carbon monoxide are highly toxic, colorless and odorless gases. Both of the gases have their own toxic levels to be detected. But during their combined presence, the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion. By using PCA,… More >

R. Bhaskaran^1,*, S. Saravanan¹, M. Kavitha², C. Jeyalakshmi³, Seifedine Kadry⁴, Hafiz Tayyab Rauf⁵, Reem Alkhammash⁶

Computer Systems Science and Engineering, Vol.44, No.1, pp. 235-247, 2023, DOI:10.32604/csse.2023.024399

Abstract Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover,… More >

R. Prabhu^1,*, S. Rajesh²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 281-295, 2023, DOI:10.32604/csse.2023.024339

Abstract Cloud computing distributes task-parallel among the various resources. Applications with self-service supported and on-demand service have rapid growth. For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements. Proper resource allocation is vital for fulfilling user requirements. In contrast, improper resource allocations result to load imbalance, which leads to severe service issues. The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations. The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex. This paper proposes an NMDS (Network Manager based Dynamic Scheduling) for achieving a… More >

Fuad A. M. Al-Yarimi^*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 129-142, 2023, DOI:10.32604/csse.2023.024297

Abstract A critical component of dealing with heart disease is real-time identification, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contributions to cardiac arrhythmia prediction using supervised learning approaches generally involve the use of demographic features (electronic health records), signal features (electrocardiogram features as signals), and temporal features. Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats, it is possible to detect some of the irregularities in the early stages of arrhythmia. This paper describes the training… More >

K. Dinesh Babu^1,*, C. Senthil Singh²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 709-720, 2023, DOI:10.32604/csse.2023.024295

Abstract Brain tumors are potentially fatal presence of cancer cells over a human brain, and they need to be segmented for accurate and reliable planning of diagnosis. Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived. Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities. The scanner used for sensing the location of tumors cells will be subjected to additional protocols and measures for accuracy, in turn, increasing the time… More >

P. Renukadevi^1,*, A. Rajiv Kannan²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 629-645, 2023, DOI:10.32604/csse.2023.024217

Abstract About 170 nations have been affected by the COvid VIrus Disease-19 (COVID-19) epidemic. On governing bodies across the globe, a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive, and they feel challenging to tackle this situation. Most researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these situations. In the previous works, Long Short-Term Memory (LSTM) was used to predict future COVID-19 cases. According to LSTM network data, the outbreak is expected to finish by June 2020. However, there is a chance of an over-fitting problem in… More >

Kingsley A. Ogudo¹, R. Surendran^2,*, Osamah Ibrahim Khalaf³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 693-707, 2023, DOI:10.32604/csse.2023.024154

Abstract Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish… More >

B. Lalitha^1,*, V. Gomathi²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 173-189, 2023, DOI:10.32604/csse.2023.024118

Abstract IC (Image Captioning) is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements. However, in existing works, because of the complexity in images, neglecting major relation between the object in an image, poor quality image, labelling it remains a big problem for researchers. Hence, the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC. So in this research work the main contribution deals with the framework consists of three phases that is image understanding, textual understanding and decoding. Initially, the image… More >

R. N. Karthika^*, C. Valliyammai, M. Naveena

Computer Systems Science and Engineering, Vol.44, No.1, pp. 777-795, 2023, DOI:10.32604/csse.2023.024086

Abstract The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy. But, deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes. In Spite of much technological advancement, phishing acts as the first step in a series of attacks. With technological advancements, availability and access to the phishing kits has improved drastically, thus making it an ideal tool for the hackers to execute the attacks. The phishing cases indicate use of foreign characters to disguise the original Uniform Resource Locator (URL), typosquatting the popular domain names, using reserved characters… More >

R. Ganesh Babu^1,*, D. Hemanand², V. Amudha³, S. Sugumaran⁴

Computer Systems Science and Engineering, Vol.44, No.1, pp. 441-456, 2023, DOI:10.32604/csse.2023.024049

Abstract In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity. In this research, multiple random… More >