Home / Journals / CSSE / Vol.47, No.1, 2023
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    ARTICLE

    Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

    Fang Yu1, Wenbin Bi2, Ning Cao3,4,*, 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… More >

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    ARTICLE

    Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model

    Fares Abdulhafidh Dael1,*, Ömer Çağrı Yavuz2, Uğur Yavuz1
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 19-35, 2023, DOI:10.32604/csse.2023.037903
    Abstract The key indication of a nation’s economic development and strength is the stock market. Inflation and economic expansion affect the volatility of the stock market. Given the multitude of factors, predicting stock prices is intrinsically challenging. Predicting the movement of stock price indexes is a difficult component of predicting financial time series. Accurately predicting the price movement of stocks can result in financial advantages for investors. Due to the complexity of stock market data, it is extremely challenging to create accurate forecasting models. Using machine learning and other algorithms to anticipate stock prices is an More >

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    ARTICLE

    Applying English Idiomatic Expressions to Classify Deep Sentiments in COVID-19 Tweets

    Bashar Tahayna, Ramesh Kumar Ayyasamy*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 37-54, 2023, DOI:10.32604/csse.2023.036648
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning in Electronic Healthcare)
    Abstract Millions of people are connecting and exchanging information on social media platforms, where interpersonal interactions are constantly being shared. However, due to inaccurate or misleading information about the COVID-19 pandemic, social media platforms became the scene of tense debates between believers and doubters. Healthcare professionals and public health agencies also use social media to inform the public about COVID-19 news and updates. However, they occasionally have trouble managing massive pandemic-related rumors and frauds. One reason is that people share and engage, regardless of the information source, by assuming the content is unquestionably true. On Twitter,… More >

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    ARTICLE

    A Novel Color Image Watermarking Method with Adaptive Scaling Factor Using Similarity-Based Edge Region

    Kali Gurkahraman1,*, Rukiye Karakis2, Hidayet Takci1
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 55-77, 2023, DOI:10.32604/csse.2023.037798
    (This article belongs to the Special Issue: Intelligent Devices and Computing Applications)
    Abstract This study aimed to deal with three challenges: robustness, imperceptibility, and capacity in the image watermarking field. To reach a high capacity, a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional techniques. The colored watermark image was created by inserting a randomly generated message on the edge points detected by this algorithm. To ensure robustness and imperceptibility, watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value Decomposition. In the watermarking stage, the watermark image was weighted by the adaptive scaling factor More >

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    ARTICLE

    A Lightweight Approach (BL-DAC) to Secure Storage Sharing in Cloud-IoT Environments

    Zakariae Dlimi*, Abdellah Ezzati, Said Ben Alla
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 79-103, 2023, DOI:10.32604/csse.2023.037099
    Abstract The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud… More >

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    ARTICLE

    Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

    Sami Saeed Binyamin1, Mahmoud Ragab2,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 105-119, 2023, DOI:10.32604/csse.2023.037311
    Abstract Cognitive radio wireless sensor networks (CRWSN) can be defined as a promising technology for developing bandwidth-limited applications. CRWSN is widely utilized by future Internet of Things (IoT) applications. Since a promising technology, Cognitive Radio (CR) can be modelled to alleviate the spectrum scarcity issue. Generally, CRWSN has cognitive radio-enabled sensor nodes (SNs), which are energy limited. Hierarchical cluster-related techniques for overall network management can be suitable for the scalability and stability of the network. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique More >

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    ARTICLE

    CBOE Volatility Index Forecasting under COVID-19: An Integrated BiLSTM-ARIMA-GARCH Model

    Min Hyung Park1, Dongyan Nan2,3, Yerin Kim1, Jang Hyun Kim1,2,3,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 121-134, 2023, DOI:10.32604/csse.2023.033247
    Abstract After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized More >

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    ARTICLE

    Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

    Sindhuja Manickavasagam*, Poonkuzhali Sugumaran
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 135-151, 2023, DOI:10.32604/csse.2023.030344
    Abstract Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique… More >

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    ARTICLE

    AI Method for Improving Crop Yield Prediction Accuracy Using ANN

    T. Sivaranjani1,*, S. P. Vimal2
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 153-170, 2023, DOI:10.32604/csse.2023.036724
    Abstract Crop Yield Prediction (CYP) is critical to world food production. Food safety is a top priority for policymakers. They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business. Crop Yield (CY) is a complex variable influenced by multiple factors, including genotype, environment, and their interactions. CYP is a significant agrarian issue. However, CYP is the main task due to many composite factors, such as climatic conditions and soil characteristics. Machine Learning (ML) is a powerful tool for supporting CYP decisions, including decision support on which… More >

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    ARTICLE

    An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

    Jaehwan Lee1, Sangoh Park2,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 171-186, 2023, DOI:10.32604/csse.2023.038139
    Abstract Recently, various mobile apps have included more features to improve user convenience. Mobile operating systems load as many apps into memory for faster app launching and execution. The least recently used (LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low. However, the LRU-based cached app termination does not distinguish between frequently or infrequently used apps. The app launch performance degrades if LRU terminates frequently used apps. Recent studies have suggested the potential of using users’ app usage patterns to predict the next app launch… More >

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    ARTICLE

    COVID TCL: A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

    Rui Wen1,*, Jie Zhou2, Zhongliang Shen1, Xiaorui Zhang2,3,4, Sunil Kumar Jha5
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889
    Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to… More >

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    ARTICLE

    Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network

    Zhaomin Liang, Tingting Liang*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 205-219, 2023, DOI:10.32604/csse.2023.037124
    (This article belongs to the Special Issue: Trustworthy AI and Its Applications)
    Abstract The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry. Most book recommendation systems also use this algorithm. However, the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well. This algorithm only uses the shallow feature design of the interaction between readers and books, so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books, leading to a decline in recommendation performance. Given the above problems, this study uses… More >

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    ARTICLE

    Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

    G. Anitha1, N. Supriya2, Fayadh Alenezi3, E. Laxmi Lydia4, Gyanendra Prasad Joshi5, Jinsang You6,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 221-238, 2023, DOI:10.32604/csse.2023.036479
    Abstract Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief More >

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    ARTICLE

    Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

    Mohamed Abouhawwash1,2, S. Sridevi3, Suma Christal Mary Sundararajan4, Rohit Pachlor5, Faten Khalid Karim6, Doaa Sami Khafaga6,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 239-253, 2023, DOI:10.32604/csse.2023.037812
    Abstract One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance… More >

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    ARTICLE

    An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

    Majida Kazmi1,*, Maria Tabasum Shoaib1,2, Arshad Aziz3, Hashim Raza Khan1,2, Saad Ahmed Qazi1,2
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 255-272, 2023, DOI:10.32604/csse.2023.038464
    (This article belongs to the Special Issue: Machine Learning for Industrial Internet of Things (IIoT))
    Abstract Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and… More >

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    ARTICLE

    Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

    Gang Xiang1,2, Xiaoyu Cheng3, Wei He3,4,*, Peng Han3
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 273-298, 2023, DOI:10.32604/csse.2023.037892
    Abstract A liquid launch vehicle is an important carrier in aviation, and its regular operation is essential to maintain space security. In the safety assessment of fluid launch vehicle body structure, it is necessary to ensure that the assessment model can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process. Therefore, a belief rule base with interpretability (BRB-i) assessment method of liquid launch vehicle structure safety status combines data and knowledge. Moreover, an innovative whale optimization algorithm with interpretable constraints is proposed. The experiments are carried out… More >

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    ARTICLE

    Fine-Grained Pornographic Image Recognition with Multi-Instance Learning

    Zhiqiang Wu*, Bing Xie
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 299-316, 2023, DOI:10.32604/csse.2023.038586
    Abstract Image has become an essential medium for expressing meaning and disseminating information. Many images are uploaded to the Internet, among which some are pornographic, causing adverse effects on public psychological health. To create a clean and positive Internet environment, network enforcement agencies need an automatic and efficient pornographic image recognition tool. Previous studies on pornographic images mainly rely on convolutional neural networks (CNN). Because of CNN’s many parameters, they must rely on a large labeled training dataset, which takes work to build. To reduce the effect of the database on the recognition performance of pornographic… More >

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    ARTICLE

    Application of Depth Learning Algorithm in Automatic Processing and Analysis of Sports Images

    Kai Yang*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 317-332, 2023, DOI:10.32604/csse.2023.037266
    (This article belongs to the Special Issue: Trustworthy AI and Its Applications)
    Abstract With the rapid development of sports, the number of sports images has increased dramatically. Intelligent and automatic processing and analysis of moving images are significant, which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry. In this paper, a method of table tennis identification and positioning based on a convolutional neural network is proposed, which solves the problem that the identification and positioning method based on color features and contour features More >

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    ARTICLE

    Signal Processing and AI-based Assessment of Rehabilitation Exercises for Diastasis Recti Abdominis

    R. Karthik1, R. Menaka1,*, P. Ponmathi2, Daehan Won3, P. Vinitha Joshy1, J. G. Aravindan4, S. Harshavardhan4, K. V. S. D. Aashish kumar4, R. Akileshkumar4
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 333-348, 2023, DOI:10.32604/csse.2023.037661
    Abstract Diastasis Recti Abdominis (DRA) is the separation of abdominal recti muscles which occurs in women during their pregnancy and postpartum time. This is because of the stretching of the linea alba, a fibrous connective tissue on the abdominal wall. The Linea Alba is elastic and retracts back after the delivery of the baby. When this tissue gets overstretched, it loses its elasticity and the gap in the abdominals may not be closed leading to DRA. The motive of this research is to analyze the postpartum rehabilitation for signals from Inertial Measurement Unit (IMU) sensors. The… More >

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    ARTICLE

    Statistical Time Series Forecasting Models for Pandemic Prediction

    Ahmed ElShafee1, Walid El-Shafai2,3, Abeer D. Algarni4,*, Naglaa F. Soliman4, Moustafa H. Aly5
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 349-374, 2023, DOI:10.32604/csse.2023.037408
    Abstract COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linear ML models, and nonlinear ML models. Examples of these models are… More >

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    ARTICLE

    Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

    Sultan Alahmari1, Saud Yonbawi2, Suneetha Racharla3, E. Laxmi Lydia4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6,*, Ayman Aljarbouh7, Samih M. Mostafa8
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 375-391, 2023, DOI:10.32604/csse.2023.036362
    Abstract Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial… More >

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    ARTICLE

    Hybrid Chameleon and Honey Badger Optimization Algorithm for QoS-Based Cloud Service Composition Problem

    G. Manimala*, A. Chinnasamy
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 393-412, 2023, DOI:10.32604/csse.2023.037066
    Abstract Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service (SaaS). Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources. In this context, the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests, as the services stored over the cloud are too complex and scalable. To achieve better service composition, the parameters of Quality of Service (QoS) related to each service considered to be… More >

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    ARTICLE

    An Ensemble Machine Learning Technique for Stroke Prognosis

    Mesfer Al Duhayyim1,*, Sidra Abbas2,*, 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… More >

  • Open AccessOpen Access

    ARTICLE

    HSPM: A Better Model to Effectively Preventing Open-Source Projects from Dying

    Zhifang Liao1, Fangying Fu1, Yiqi Zhao1, Sui Tan2,3,*, Zhiwu Yu2,3, Yan Zhang4
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 431-452, 2023, DOI:10.32604/csse.2023.038087
    Abstract With the rapid development of Open-Source (OS), more and more software projects are maintained and developed in the form of OS. These Open-Source projects depend on and influence each other, gradually forming a huge OS project network, namely an Open-Source Software ECOsystem (OSSECO). Unfortunately, not all OS projects in the open-source ecosystem can be healthy and stable in the long term, and more projects will go from active to inactive and gradually die. In a tightly connected ecosystem, the death of one project can potentially cause the collapse of the entire ecosystem network. How can… More >

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    ARTICLE

    Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

    Turke Althobaiti1,2, Yousef Sanjalawe3,*, Naeem Ramzan4
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 453-469, 2023, DOI:10.32604/csse.2023.039207
    (This article belongs to the Special Issue: Artificial Intelligence for Cyber Security)
    Abstract Flash Crowd attacks are a form of Distributed Denial of Service (DDoS) attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing (CC). Botnets are often used by attackers to perform a wide range of DDoS attacks. With advancements in technology, bots are now able to simulate DDoS attacks as flash crowd events, making them difficult to detect. When it comes to application layer DDoS attacks, the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue. This is… More >

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    ARTICLE

    Efficient Explanation and Evaluation Methodology Based on Hybrid Feature Dropout

    Jingang Kim, Suengbum Lim, Taejin Lee*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 471-490, 2023, DOI:10.32604/csse.2023.038413
    (This article belongs to the Special Issue: Artificial Intelligence for Cyber Security)
    Abstract AI-related research is conducted in various ways, but the reliability of AI prediction results is currently insufficient, so expert decisions are indispensable for tasks that require essential decision-making. XAI (eXplainable AI) is studied to improve the reliability of AI. However, each XAI methodology shows different results in the same data set and exact model. This means that XAI results must be given meaning, and a lot of noise value emerges. This paper proposes the HFD (Hybrid Feature Dropout)-based XAI and evaluation methodology. The proposed XAI methodology can mitigate shortcomings, such as incorrect feature weights and… More >

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    ARTICLE

    Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

    Musaed Alrashidi*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 491-513, 2023, DOI:10.32604/csse.2023.038628
    (This article belongs to the Special Issue: Application of Artificial Intelligence and Machine Learning in Renewable Energy Systems)
    Abstract Statistical distributions are used to model wind speed, and the two-parameters Weibull distribution has proven its effectiveness at characterizing wind speed. Accurate estimation of Weibull parameters, the scale (c) and shape (k), is crucial in describing the actual wind speed data and evaluating the wind energy potential. Therefore, this study compares the most common conventional numerical (CN) estimation methods and the recent intelligent optimization algorithms (IOA) to show how precise estimation of c and k affects the wind energy resource assessments. In addition, this study conducts technical and economic feasibility studies for five sites in the northern… More >

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    ARTICLE

    Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

    Sana Parez1, Naqqash Dilshad2, Turki M. Alanazi3, Jong Weon Lee1,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992
    Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a… More >

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    ARTICLE

    Higher Order OAM Mode Generation Using Wearable Antenna for 5G NR Bands

    Shehab Khan Noor1, Arif Mawardi Ismail1, Mohd Najib Mohd Yasin1,*, Mohamed Nasrun Osman1, Thennarasan Sabapathy1, Shakhirul Mat Salleh2, Ping Jack Soh3, Ali Hanafiah Rambe4, Nurulazlina Ramli5
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 537-551, 2023, DOI:10.32604/csse.2023.037381
    Abstract This paper presents a flexible and wearable textile array antenna designed to generate Orbital Angular Momentum (OAM) waves with Mode +2 at 3.5 GHz (3.4 to 3.6 GHz) of the sub-6 GHz fifth-generation (5G) New Radio (NR) band. The proposed antenna is based on a uniform circular array of eight microstrip patch antennas on a felt textile substrate. In contrast to previous works involving the use of rigid substrates to generate OAM waves, this work explored the use of flexible substrates to generate OAM waves for the first time. Other than that, the proposed antenna… More >

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    ARTICLE

    Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

    Adnan Hasan Bdair Aighuraibawi1,2, Selvakumar Manickam1,*, Rosni Abdullah3, Zaid Abdi Alkareem Alyasseri4,5, Ayman Khallel6, Dilovan Asaad Zebari9, Hussam Mohammed Jasim7, Mazin Mohammed Abed8, Zainb Hussein Arif7
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 553-574, 2023, DOI:10.32604/csse.2023.037948
    (This article belongs to the Special Issue: Role of Big Data Management, Machine Learning, and Deep Learning Techniques for Ubiquitous Computing)
    Abstract Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 1038 unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS)… More >

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    ARTICLE

    Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

    Mamoona Humayun1,*, Danish Javed2, Nz Jhanjhi2, Maram Fahaad Almufareh1, Saleh Naif Almuayqil1
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 575-591, 2023, DOI:10.32604/csse.2023.038765
    Abstract Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as… More >

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    ARTICLE

    Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders

    Adel Hafeezallah1, Ahlam Al-Dhamari2,3,*, Syed Abd Rahman Abu-Bakar2
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 593-611, 2023, DOI:10.32604/csse.2023.039479
    Abstract Visual motion segmentation (VMS) is an important and key part of many intelligent crowd systems. It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes, which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades. Trajectory clustering has become one of the most popular methods in VMS. However, complex data, such as a large number of samples and parameters, makes it difficult for trajectory clustering to work well with accurate motion segmentation… More >

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    ARTICLE

    Identifying Severity of COVID-19 Medical Images by Categorizing Using HSDC Model

    K. Ravishankar*, C. Jothikumar
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 613-635, 2023, DOI:10.32604/csse.2023.038343
    Abstract Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of… More >

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    ARTICLE

    A New System for Road Traffic Optimisation Using the Virtual Traffic Light Technology

    Ahmad A. A. Alkhatib*, Adnan A. Hnaif, Thaer Sawalha
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 637-656, 2023, DOI:10.32604/csse.2023.037345
    (This article belongs to the Special Issue: Intelligent Devices and Computing Applications)
    Abstract Large cities suffer from traffic congestion, particularly at intersections, due to a large number of vehicles, which leads to the loss of time by increasing carbon emissions, including fuel consumption. Therefore, the need for optimising the flow of vehicles at different intersections and reducing the waiting time is a critical challenge. Conventional traffic lights have been used to control traffic flow at different intersections and have been improved to become more efficient by using different algorithms, sensors and cameras. However, they also face some challenges, such as high-cost installation, operation, and maintenance issues. This paper… More >

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    ARTICLE

    Music Genre Classification Using DenseNet and Data Augmentation

    Dao Thi Le Thuy1, Trinh Van Loan2,*, Chu Ba Thanh3, Nguyen Hieu Cuong1
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 657-674, 2023, DOI:10.32604/csse.2023.036858
    Abstract It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation, distribution, and enjoyment of musical works have undergone huge changes. As the number of music products increases daily and the music genres are extremely rich, storing, classifying, and searching these works manually becomes difficult, if not impossible. Automatic classification of musical genres will contribute to making this possible. The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve… More >

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    ARTICLE

    Radon CLF: A Novel Approach for Skew Detection Using Radon Transform

    Yuhang Chen1, Mahdi Bahaghighat2,*, Aghil Esmaeili Kelishomi3, Jingyi Du1,*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 675-697, 2023, DOI:10.32604/csse.2023.038234
    Abstract In the digital world, a wide range of handwritten and printed documents should be converted to digital format using a variety of tools, including mobile phones and scanners. Unfortunately, this is not an optimal procedure, and the entire document image might be degraded. Imperfect conversion effects due to noise, motion blur, and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition (OCR) systems. In Document Image Analysis Systems (DIAS), skew estimation of images is a crucial step. In this paper, a novel,… More >

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    ARTICLE

    A Unique Discrete Wavelet & Deterministic Walk-Based Glaucoma Classification Approach Using Image-Specific Enhanced Retinal Images

    Krishna Santosh Naidana, Soubhagya Sankar Barpanda*
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 699-720, 2023, DOI:10.32604/csse.2023.036744
    Abstract Glaucoma is a group of ocular atrophy diseases that cause progressive vision loss by affecting the optic nerve. Because of its asymptomatic nature, glaucoma has become the leading cause of human blindness worldwide. In this paper, a novel computer-aided diagnosis (CAD) approach for glaucomatous retinal image classification has been introduced. It extracts graph-based texture features from structurally improved fundus images using discrete wavelet-transformation (DWT) and deterministic tree-walk (DTW) procedures. Retinal images are considered from both public repositories and eye hospitals. Images are enhanced with image-specific luminance and gradient transitions for both contrast and texture improvement.… More >

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    ARTICLE

    Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

    Mohamed Esmail Karar1,2,*, Amged Sayed A. Mahmoud2,3
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 721-739, 2023, DOI:10.32604/csse.2023.039235
    (This article belongs to the Special Issue: Explainable AI and Cybersecurity Techniques for IoT-Based Medical and Healthcare Applications)
    Abstract Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide. This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions. The infusion of vasoactive drugs to patients endures various issues, such as variation of sensitivity and noise, which require effective and powerful systems to ensure robustness and good performance. The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy (IT2F) logic and teaching-learning-based optimization… More >

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    ARTICLE

    Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

    Jiangyuan Yao1, Weiping Yang1, Shuhua Weng1, Minrui Wang1, Zheng Jiang2, Deshun Li1,*, Yahui Li3, Xingcan Cao4
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 741-755, 2023, DOI:10.32604/csse.2023.031531
    Abstract Software-defined networking (SDN) is widely used in multiple types of data center networks, and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers. However, the network topology of each control domain of SDN will affect the performance of the multi-domain network, so performance evaluation is required before the deployment of the multi-domain SDN. Besides, there is a high cost to build real multi-domain SDN networks with different topologies, so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network. As… More >

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    ARTICLE

    New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

    Ouail Mjahed1,*, Salah El Hadaj1, El Mahdi El Guarmah1,2, Soukaina Mjahed1
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.039111
    Abstract Denial of Service (DoS/DDoS) intrusions are damaging cyber-attacks, and their identification is of great interest to the Intrusion Detection System (IDS). Existing IDS are mainly based on Machine Learning (ML) methods including Deep Neural Networks (DNN), but which are rarely hybridized with other techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017, CSE-CICIDS2018 and CICDDoS 2019 datasets, according to the following key points. a) Three imbalanced CICIDS2017-2018-2019 datasets, including Benign and DoS/DDoS attack classes, are used.… More >

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    ARTICLE

    BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

    A. Richard William1,*, J. Senthilkumar2, Y. Suresh2, V. Mohanraj2
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753
    Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with… More >

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    ARTICLE

    Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

    Sundas Rukhsar1, Mazhar Javed Awan1, Usman Naseem2, Dilovan Asaad Zebari3, Mazin Abed Mohammed4,*, Marwan Ali Albahar5, Mohammed Thanoon5, Amena Mahmoud6
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 791-807, 2023, DOI:10.32604/csse.2023.038384
    (This article belongs to the Special Issue: Intelligent Telehealth Monitoring with Man-Computer Interface)
    Abstract Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative… More >

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    ARTICLE

    Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

    Hassan Shokouhandeh1, Mehrdad Ahmadi Kamarposhti2,*, William Holderbaum3, Ilhami Colak4, Phatiphat Thounthong5
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 809-822, 2023, DOI:10.32604/csse.2023.035827
    (This article belongs to the Special Issue: Metaheuristics Optimization for Real-World Applications)
    Abstract The widespread penetration of distributed energy sources and the use of load response programs, especially in a microgrid, have caused many power system issues, such as control and operation of these networks, to be affected. The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid. In this paper, the optimum operation of distributed generation resources and heat and power storage in a microgrid, was performed based on real-time pricing through the proposed gray wolf optimization (GWO) algorithm to reduce the… More >

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    ARTICLE

    Ensemble Learning for Fetal Health Classification

    Mesfer Al Duhayyim1,*, Sidra Abbas2, Abdullah Al Hejaili3, Natalia Kryvinska4,*, 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… More >

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    ARTICLE

    Network Learning-Enabled Sensor Association for Massive Internet of Things

    Alaa Omran Almagrabi1,*, Rashid Ali2, Daniyal Alghazzawi1, Bander A. Alzahrani1, Fahad M. Alotaibi1
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 843-853, 2023, DOI:10.32604/csse.2023.037652
    (This article belongs to the Special Issue: Machine Learning for Industrial Internet of Things (IIoT))
    Abstract The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with any GW in a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the… More >

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    ARTICLE

    Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

    Z. Faizal Khan1, Saeed M. Alshahrani2,*, Abdulrahman Alghamdi2, Someah Alangari3, Nouf Ibrahim Altamami4, Khalid A. Alissa5, Sana Alazwari6, Mesfer Al Duhayyim7, Fahd N. Al-Wesabi8
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 855-871, 2023, DOI:10.32604/csse.2023.036735
    Abstract The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this… More >

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    ARTICLE

    Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

    Mohamed Yacin Sikkandar1,*, K. Hemalatha2, M. Subashree3, S. Srinivasan4, Seifedine Kadry5,6,7, Jungeun Kim8, Keejun Han9
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 873-889, 2023, DOI:10.32604/csse.2023.035730
    Abstract Recently, COVID-19 has posed a challenging threat to researchers, scientists, healthcare professionals, and administrations over the globe, from its diagnosis to its treatment. The researchers are making persistent efforts to derive probable solutions for managing the pandemic in their areas. One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography (CT) scans. At the same time, the recent advances in machine learning (ML) and deep learning (DL) models show promising results in medical imaging. Particularly, the convolutional neural network (CNN) model can be applied to… More >

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    ARTICLE

    Detecting and Classifying Darknet Traffic Using Deep Network Chains

    Amr Munshi1,2,*, Majid Alotaibi1,2, Saud Alotaibi2,3, Wesam Al-Sabban2,3, Nasser Allheeib4
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 891-902, 2023, DOI:10.32604/csse.2023.039374
    (This article belongs to the Special Issue: Intelligent Uni-modal and Multi-modal Agents against Adversarial Cyber Attacks)
    Abstract The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden… More >

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    ARTICLE

    Improved Cloud Storage Encryption Using Block Cipher-Based DNA Anti-Codify Model

    E. Srimathi1,*, 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… More >

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    ARTICLE

    Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

    Ramya Nemani1, G. Jose Moses2, Fayadh Alenezi3, K. Vijaya Kumar4, Seifedine Kadry5,6,7,*, Jungeun Kim8, Keejun Han9
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 919-935, 2023, DOI:10.32604/csse.2023.034213
    Abstract Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime… More >

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