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  • Open Access

    ARTICLE

    Comprehensive DDoS Attack Classification Using Machine Learning Algorithms

    Olga Ussatova1,2, Aidana Zhumabekova1,*, Yenlik Begimbayeva2,3, Eric T. Matson4, Nikita Ussatov5

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 577-594, 2022, DOI:10.32604/cmc.2022.026552

    Abstract The fast development of Internet technologies ignited the growth of techniques for information security that protect data, networks, systems, and applications from various threats. There are many types of threats. The dedicated denial of service attack (DDoS) is one of the most serious and widespread attacks on Internet resources. This attack is intended to paralyze the victim's system and cause the service to fail. This work is devoted to the classification of DDoS attacks in the special network environment called Software-Defined Networking (SDN) using machine learning algorithms. The analyzed dataset included instances of two classes:… More >

  • Open Access

    ARTICLE

    Forecasting Mental Stress Using Machine Learning Algorithms

    Elias Hossain1, Abdulwahab Alazeb2,*, Naif Al Mudawi2, Sultan Almakdi2, Mohammed Alshehri2, M. Gazi Golam Faruque3, Wahidur Rahman3

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4945-4966, 2022, DOI:10.32604/cmc.2022.027058

    Abstract Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology… More >

  • Open Access

    ARTICLE

    Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

    Mujeeb Ur Rehman1,*, Maha Driss2,3, Abdukodir Khakimov4, Sohail Khalid1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5681-5697, 2022, DOI:10.32604/cmc.2022.025840

    Abstract Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence,… More >

  • Open Access

    ARTICLE

    Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

    Ayman Altameem1, Jaideep Singh Sachdev2, Vijander Singh2, Ramesh Chandra Poonia3, Sandeep Kumar4, Abdul Khader Jilani Saudagar5,*

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1095-1107, 2022, DOI:10.32604/csse.2022.023256

    Abstract Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals… More >

  • Open Access

    ARTICLE

    Accurate Location Estimation of Smart Dusts Using Machine Learning

    Shariq Bashir1,*, Owais Ahmed Malik2, Daphne Teck Ching Lai2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6165-6181, 2022, DOI:10.32604/cmc.2022.024269

    Abstract Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the… More >

  • Open Access

    ARTICLE

    Rainfall Forecasting Using Machine Learning Algorithms for Localized Events

    Ganapathy Pattukandan Ganapathy1, Kathiravan Srinivasan2, Debajit Datta2, Chuan-Yu Chang3,4,*, Om Purohit5, Vladislav Zaalishvili6, Olga Burdzieva6

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6333-6350, 2022, DOI:10.32604/cmc.2022.023254

    Abstract A substantial amount of the Indian economy depends solely on agriculture. Rainfall, on the other hand, plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing, if the amount is inordinate or scant, it can ruin the entire hard work of the farmers. In this work, the rainfall dataset of the Vellore region, of Tamil Nadu, India, in the years 2021 and 2022 is forecasted using several machine learning algorithms. Feature engineering has been performed in this work in order to generate new features that remove all sorts… More >

  • Open Access

    ARTICLE

    Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

    Ashutosh Kumar Dubey1,*, Umesh Gupta2, Sonal Jain2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4523-4543, 2022, DOI:10.32604/cmc.2022.021148

    Abstract This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means and fuzzy c-means (FCM) algorithms for their individual performance evaluation in terms of clustering and classification. In the first phase, the clustering (k-means and FCM) algorithms were employed independently and the clustering accuracy was evaluated using different computational measures. During the second phase, the non-clustered data obtained from the first phase were preprocessed with TLBO. TLBO was performed using k-means (TLBO-KM) and FCM (TLBO-FCM) (TLBO-KM/FCM) algorithms. The objective function was determined by considering both minimization and maximization criteria. Non-clustered data… More >

  • Open Access

    ARTICLE

    Detection of Fuel Adulteration Using Wave Optical with Machine Learning Algorithms

    S. Dilip Kumar1,*, T. V. Sivasubramonia Pillai2

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 19-33, 2022, DOI:10.32604/csse.2022.019366

    Abstract Fuel is a very important factor and has considerable influence on the air quality in the environment, which is the heart of the world. The increase of vehicles in lived-in areas results in greater emission of carbon particles in the environment. Adulterated fuel causes more contaminated particles to mix with breathing air and becomes the main source of dangerous pollution. Adulteration is the mixing of foreign substances in fuel, which damages vehicles and causes more health problems in living beings such as humans, birds, aquatic life, and even water resources by emitting high levels of… More >

  • Open Access

    ARTICLE

    Enrichment of Crop Yield Prophecy Using Machine Learning Algorithms

    R. Kingsy Grace*, K. Induja, M. Lincy

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 279-296, 2022, DOI:10.32604/iasc.2022.019947

    Abstract Strong associations exist between the crop productivity and the seasonal, biological, economical causes in natural ecosystems. The linkages like climatic conditions, health of a soil, growth of crop, irrigation, fertilizers, temperature, rainwater, pesticides desired to be preserved in comprehensively managed crop lands which impacts the crop potency. Crop yield prognosis plays a vibrant part in agricultural planning, administration and environs sustainability. Advancements in the field of Machine Learning have perceived novel expectations to improve the prediction performance in Agriculture. Highly gratifying prediction of crop yield helps the majority of agronomists for their rapid decision-making in… More >

  • Open Access

    ARTICLE

    Determination of COVID-19 Patients Using Machine Learning Algorithms

    Marium Malik1, Muhammad Waseem Iqbal1,*, Syed Khuram Shahzad2, Muhammad Tahir Mushtaq2, Muhammad Raza Naqvi3,4, Maira Kamran1, Babar Ayub Khan4, Muhammad Usman Tahir4

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 207-222, 2022, DOI:10.32604/iasc.2022.018753

    Abstract Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so… More >

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