Machine Learning Algorithms For Detection Research Articles

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME.

The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis. Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model. These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.

Effective Spam Detection with Machine Learning

Abstract This paper aims to provide results of empirical experiments on the accuracy of different machine learning algorithms for detecting spam messages, using a public dataset of spam messages. The originality of our study lies in the integration of topic modeling, specifically employing Latent Dirichlet Allocation (LDA) alongside machine learning algorithms for spam detection. By extracting hidden topics and uncovering patterns in spam and non-spam messages, we provide unique insights into the distinguishing characteristics of spam messages. Moreover, the integration of machine learning is a powerful tool in bolstering risk control measures ensuring the sustainability of digital platforms and communication channels. The research tests the accuracy of spam detection classifiers on an open-source dataset of spam messages. The key findings of this study reveal that the Logistic Regression classifier achieved the highest F score of 0.986, followed by the Support Vector Machine classifier with a score of 0.98 and the Naive Bayes classifier with a score of 0.955. The study concludes that Logistic Regression outperforms Naive Bayes and Support Vector Machine in text classification, particularly in spam detection, emphasizing the role of machine learning techniques in optimizing risk management strategies for sustained digital ecosystems. This capability stems from Logistic Regression’s adeptness in modeling complex relationships, enabling it to achieve high accuracy on training and test datasets.

A Comprehensive Study on Machine Learning Algorithms for detection and Classification of Parkinson’s disease

A Comprehensive Study on Machine Learning Algorithms for detection and Classification of Parkinson’s disease

Effective Segmentation and Brain Tumor Classification Using Sparse Bayesian ELM in MRI Images

Classification of tumors from MRI plays very important role for diagnosing various diseases. But, it consumes an enormous amount of time for classification. Due to the similar structure of anomalous and typical tissues in the brain, it is difficult to complete the detection process successfully. Many researchers have developed new methods for detection and classification of tumors. But most of them failed at some point due to these limitations. Therefore in our work, we introduced a new machine learning algorithm for detection and classification of tumors. In addition to this, an intellectual segmentation technique known as Improved Binomial Thresholding technique is also introduced in this paper. This newly developed approach is used to differentiate the normal and abnormal slices from brain MRI. We can extract different features from the segmented image. The extracted features may be Wavelet Transform based (WT) or Scattering Wavelet Transform based (SWT). Feature selection process is achieved using a hybrid algorithm known as CS-FS (Hybrid Cuckoo Search with Fish Swarm) to minimize the dimension of extracted features. Finally, feature classification process is performed using Sparse Bayesian Extreme Learning Machine (SBELM) classifier. The proposed method is executed with the help of BRATS (Brain Tumor Image Segmentation) 2015 dataset. The result of the proposed method is evaluated on different parameters like accuracy, specificity, and sensitivity. The values of these parameters are obtained by computing four factors such as TP, TN, FN and FP. The final evaluation results showed that, our proposed SBELM classifier has attained 97.2% accuracy, which is better than the other existing method like state-of-the-art method.

Magnetic Fe3O4@COF@Ag SERS substrate combined with machine learning algorithms for detection of three quinolone antibiotics: Ciprofloxacin, norfloxacin and levofloxacin

Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy industry. Currently, the problem of excessive antibiotics in dairy products is very serious. As an ultra-sensitive detection technology, Surface-Enhanced Raman Scattering (SERS) was applied to the detection of quinolone antibiotics in this work. In order to classify and quantify three antibiotics (Ciprofloxacin, Norfloxacin, Levofloxacin) with highly similar molecular structures, a combination of magnetic COF-based SERS substrate and machine learning algorithms (PCA-k-NN, PCA-SVM, PCA-Decision Tree) was used. The classification accuracy of the spectral dataset could reach 100% and the results of LOD calculation were: CIP: 5.61 × 10−9M, LEV: 1.44 × 10−8M, NFX: 1.56 × 10−8M. This provides a new method for the detection of antibiotics in dairy products.

SVINnovation Challenge: Fostering Innovation in Vascular and Interventional Neurology

SVINnovation Challenge: Fostering Innovation in Vascular and Interventional Neurology

A Hybrid Machine Learning Algorithm for Detection of Simulated Expiratory Markers of Diabetic Patients Based on Gas Sensor Array

The method for breath detection using gas sensor array is gaining popularity. It was found that acetone can be used as breath marker in diabetic patients. In this article, seven metal oxide gas sensors were used to collect acetone and ethanol gas, which are used to simulate the exhaled breath of diabetic patients to obtain multidimensional response data. The kernel principal component analysis (KPCA) algorithm is used to extract the characteristics from the data collected by the sensor array. The kind of gases is qualitatively identified using the adaptive boosting (AdaBoost) algorithm, and the grid search (GS) method is used to automatically optimize the parameters of AdaBoost algorithm. The quantitative analysis of gas concentration is performed using multivariate relevance vector machine (MVRVM) and it is also trained using the gas sensor array drift dataset at different concentrations from the University of California (UCI) database. The experimental results show that the accuracy of the algorithm in the qualitative identification of acetone and ethanol gas reaches 99.722%, and the root-mean-square errors (RMSE) for quantification of acetone and ethanol gases are 0.027 and 0.030, respectively. The algorithm is used for qualitative identification on the gas sensor array drift dataset at different concentrations with an accuracy of 94.55%, and the RMSE for quantification of acetone and ethanol gases are 11.59 and 8.72, respectively.

Mapping of Virtual Machines Using Machine Learning Algorithms for Detection of Faulty Nodes

A distributed system is characterized by a large number of nodes that are linked to a network and are mostly used for transaction processing. Large set of users are likely to communicate information over the network to the nodes, consistency and dependability remain a critical problem in the distributed environments. Independent failure of the component is one of the major problems in the distributed systems as it slowly impacts the performance of the other nodes in the system. The quality of service - QoS of a distributed network may be improved by a quick way of detecting problematic nodes. Sometime heavy nodes required high computation for transaction processing while idle nodes take low computation. In this paper, we proposed identification of straggler nodes in distributed environment with the help of hybrid machine learning algorithm. The work basically carried out to set up of large number of virtual machines and collect current log audits of each VM. According to the available parameters of audit files to each machine, algorithms decide that specific node is overheated or ideal condition. In expensive experimental analysis we demonstrate a accuracy of proposed hybrid machine learning algorithm. The proposed algorithm produces higher precision up to 4.5% than state-of-art methods. Key highlights of the VM mapping strategy were also investigated through a scrutiny of ongoing contracts. Main focus remains on machine learning (ML) to distinguish PM (Physical Machine) congestion, determining VMs from crowded PMs, and VM conditions as major exercises. This paper aims to review and characterize research on the planning and status of VMs that use ML using asset usage history. Energy productivity, VM migration, and quality of service were the main exhibition boundaries used to investigate cloud data center presentations.

Application of Machine Learning Algorithms for Detection of Vulnerability in Web Applications

Application of Machine Learning Algorithms for Detection of Vulnerability in Web Applications

Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations.

Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.

Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia.

Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.

Effects of Feature Selection with Machine Learning Algorithms in Detection of Credit Card Fraud

As an effect of developments in e-commerce systems and communication technologies, Credit cards have become the most common mode of payment for purchases. The payments through the credit cards also involve the risk of credit card fraud such as application fraud, identity theft, lost/stolen card misuse, and phishing. These frauds lead to huge losses and require automatic and real-time fraud detection. Many studies have used Machine Learning (ML) techniques to detect fraudulent transactions. This study focuses on proposing a framework for the detection of credit card frauds by applying machine learning techniques like Random Forest (RF) and Naïve Bayes and testing the results on balanced and unbalanced datasets with and without performing the feature selection on the dataset. After comparing the results, it was discovered that Random Forest outperformed the Naïve Bayes on a balanced dataset with feature selection performed using Recursive Feature Elimination and Information Gain.

Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications.

Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).

Using machine learning algorithm for detection of cyber-attacks in cyber physical systems

Network integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. Machine Learning (ML) and Artificial Intelligence (AI) have the potential to make these the worst of moments, but it may also be the finest of times. There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. Hence, in this paper, we propose a novel cyberattack detection framework by integrating AI and ML (ML) methods. Here, initially we collect the dataset from the CPS database and preprocess the data using normalization for removal of errors and redundant data. The features are extracted using Linear Discriminant Analysis (LDA). We have proposed Self-tuned Fuzzy Logic-based Hidden Markov Model (SFL-HMM) with Heuristic Multi-Swarm Optimization (HMS-ACO) algorithm for detection of the cyberattacks. The proposed method is evaluated using the MATLAB simulation tool and the metrics are compared with existing approaches. The results of the experiments reveal that the framework is more successful than traditional strategies in achieving high degrees of privacy. Furthermore, in terms of detection rate, false positive rate, and computing time, the framework beats traditional detection algorithms.

Machine Learning Algorithm for Detection of Deadliest Forms of Skin Cancer

Skin cancer is one of the most growing types and dangerous cancer in the world. The early diagnosis of melanoma and other ski cancer is a critical issue for dermatologists. In this paper, we using Machine Learning Algorithm for Detection of Deadliest Forms of Skin Cancer. This project aims to develop a skin cancer detection ML Model which can classify the skin cancer types and help in early detection. The ML Model is developed in Dot Net (. Net). The model is developed and tested with different network architectures by varying the type of layers used to train the machine. Basically our model use DNN (Deep Neural Network) and ResNet50 for detection of skin cancer. The model will be tested and trained on the dataset collected from the International Skin Imaging Collaboration (ISIC)

EEG Signal Processing for Detection of Colour Vision Deficiencies

The clinical assessment methods of detection of colour blindness are prone to subjective bias, human errors, and lack of consistency. Also, heterogeneous nature of vision deficiencies makes manual detection difficult. Hence, objective methods based on physiological signals can be used. Various studies have been undertaken to analyze the effect that colour perceived has on EEG signals and as a result can be extended to detection of colour blindness. The results of this survey indicate that there are significant differences produced in EEG signals due to changes in colour, as well as due to differences in colour perception. EEG signals also differ between healthy and colour vision deficient individuals, thereby providing a biomarker for detection. This biomarker can be exploited using machine learning algorithms for detection of colour-related vision deficiencies.

A novel hybrid machine learning algorithm for detection in smart cities

This article tries to develop a novel hybrid method for the state estimation as well as the broken rotor bar (BRB) detection of the induction motors (IMs) in smart grids. This is so significant to survive the IM from harsh damages and avoid malfunctioning in the system. The suggested method is constructed based on the Kalman filter as a successful machine learning and sigma point which have shown superior performance over the other well-known methods in the area. In order to enhance its efficiency, sigma points would make a transformation which can make the model more stable and precise. The experimental results on a test IM reveal the high accuracy and stable performance of the proposed model. Three varied scenarios are simulated to show the quality and appropriate performance of the proposed intelligent method at different loading situations. The simulation results validate the high quality and promising performance of the proposed model.

A Network Intrusion Detection System Based on Categorical Boosting Technique using NSL-KDD

Massive volumes of network traffic & data are generated by common technology including the Internet of Things, cloud computing & social networking. Intrusion Detection Systems are therefore required to track the network which dynamically analyses incoming traffic. The purpose of the IDS is to carry out attacks inspection or provide security management with desirable help along with intrusion data. To date, several approaches to intrusion detection have been suggested to anticipate network malicious traffic. The NSL-KDD dataset is being applied in the paper to test intrusion detection machine learning algorithms. We research the potential viability of ELM by evaluating the advantages and disadvantages of ELM. In the preceding part on this issue, we noted that ELM does not degrade the generalisation potential in the expectation sense by selecting the activation function correctly. In this paper, we initiate a separate analysis & demonstrate that the randomness of ELM often contributes to some negative effects. For this reason, we have employed a new technique of machine learning for overcoming the problems of ELM by using the Categorical Boosting technique (CATBoost).

A Network Intrusion Detection System Based on Categorical Boosting Technique using NSL-KDD

Massive volumes of network traffic & data are generated by common technology including the Internet of Things, cloud computing & social networking. Intrusion Detection Systems are therefore required to track the network which dynamically analyses incoming traffic. The purpose of the IDS is to carry out attacks inspection or provide security management with desirable help along with intrusion data. To date, several approaches to intrusion detection have been suggested to anticipate network malicious traffic. The NSL-KDD dataset is being applied in the paper to test intrusion detection machine learning algorithms. We research the potential viability of ELM by evaluating the advantages and disadvantages of ELM. In the preceding part on this issue, we noted that ELM does not degrade the generalisation potential in the expectation sense by selecting the activation function correctly. In this paper, we initiate a separate analysis & demonstrate that the randomness of ELM often contributes to some negative effects. For this reason, we have employed a new technique of machine learning for overcoming the problems of ELM by using the Categorical Boosting technique (CAT Boost).

Evaluation of Machine Learning Algorithms for Detection of Malicious Traffic in SCADA Network

Evaluation of Machine Learning Algorithms for Detection of Malicious Traffic in SCADA Network