E. Srimathi^1,*, S. P. Chokkalingam²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 903-918, 2023, DOI:10.32604/csse.2023.029790

Abstract When it comes to data storage, cloud computing and cloud storage providers play a critical role. The cloud data can be accessed from any location with an internet connection. Additionally, the risk of losing privacy when data is stored in a cloud environment is also increased. A variety of security techniques are employed in the cloud to enhance security. In this paper, we aim at maintaining the privacy of stored data in cloud environment by implementing block-based modelling to boost the privacy level with Anti-Codify Technique (ACoT) and block cipher-based algorithms. Initially, the cipher text is generated using Deoxyribo Nucleic… More >

Abeer D. Algarni^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1233-1247, 2023, DOI:10.32604/csse.2022.026915

Abstract Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches. Several natural language processing (NLP) tools are commonly used to examine the large quantity of data generated online. Particularly, sentiment analysis (SA) is an effective way of classifying the data into different classes of user opinions or sentiments. The latest advances in machine learning (ML) and deep learning (DL) approaches offer an intelligent way of analyzing sentiments. In this view, this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for sentiment analysis… More >

Fang Yu¹, Wenbin Bi², Ning Cao^3,4,*, Hongjun Li¹, Russell Higgs⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.018349

Abstract In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation, at the smart contract level of the blockchain, a customer churn prediction framework based on situational awareness and integrating customer attributes, the impact of project hotspots on customer interests, and customer satisfaction with the project has been built. This framework introduces the background factors in the financial customer environment, and further discusses the relationship between customers, the background of customers and the characteristics of pre-lost customers. The improved Singular Value Decomposition (SVD) algorithm and… More >

Mohamed Ibrahim Waly^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3159-3174, 2023, DOI:10.32604/csse.2023.035900

Abstract Recently, computer assisted diagnosis (CAD) model creation has become more dependent on medical picture categorization. It is often used to identify several conditions, including brain disorders, diabetic retinopathy, and skin cancer. Most traditional CAD methods relied on textures, colours, and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain notions. Recent deep learning (DL) models have been published, providing a practical way to develop models specifically for classifying input medical pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL) method for classifying medical images facilitated by deep transfer… More >

Shahid Mohammad Ganie¹, Pijush Kanti Dutta Pramanik², Majid Bashir Malik³, Anand Nayyar⁴, Kyung Sup Kwak^5,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3993-4006, 2023, DOI:10.32604/csse.2023.035244

Abstract Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was… More >

Vidit Kumar^1,*, Hemant Petwal², Ajay Krishan Gairola¹, Pareshwar Prasad Barmola¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2711-2724, 2023, DOI:10.32604/csse.2023.032047

Abstract Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded… More >

Abeeda Akram¹, Kashif Zafar¹, Adnan Noor Mian², Abdul Rauf Baig³, Riyad Almakki³, Lulwah AlSuwaidan³, Shakir Khan^3,4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3685-3701, 2023, DOI:10.32604/csse.2023.032024

Abstract One of the important research issues in wireless sensor networks (WSNs) is the optimal layout designing for the deployment of sensor nodes. It directly affects the quality of monitoring, cost, and detection capability of WSNs. Layout optimization is an NP-hard combinatorial problem, which requires optimization of multiple competing objectives like cost, coverage, connectivity, lifetime, load balancing, and energy consumption of sensor nodes. In the last decade, several meta-heuristic optimization techniques have been proposed to solve this problem, such as genetic algorithms (GA) and particle swarm optimization (PSO). However, these approaches either provided computationally expensive solutions or covered a limited number… More >

G. Venkatesan^1,*, N. Ramadass²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3979-3991, 2023, DOI:10.32604/csse.2023.031627

Abstract Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years. Nodes must advance their energy use for expanding network lifetime. The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results. Due to different network resource constraints and malicious attacks, security assurance in wireless sensor networks has been a difficult task. The implementation of these features requires larger space due to distributed module. This research work proposes new sensor node architecture integrated with a self-testing core and cryptoprocessor… More >

Noura Alenezi, Ahamed Aljuhani^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2899-2915, 2023, DOI:10.32604/csse.2023.036657

Abstract The rapid growth of the Internet of Things (IoT) in the industrial sector has given rise to a new term: the Industrial Internet of Things (IIoT). The IIoT is a collection of devices, apps, and services that connect physical and virtual worlds to create smart, cost-effective, and scalable systems. Although the IIoT has been implemented and incorporated into a wide range of industrial control systems, maintaining its security and privacy remains a significant concern. In the IIoT contexts, an intrusion detection system (IDS) can be an effective security solution for ensuring data confidentiality, integrity, and availability. In this paper, we… More >

R. Surendran^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3371-3386, 2023, DOI:10.32604/csse.2023.034465

Abstract High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model… More >