Machine Learning Techniques For Detection Research Articles

Machine Learning Approaches for Detecting Driver Drowsiness: A Critical Review

Driver drowsiness is a serious issue that poses a significant threat to road safety, as it can lead to accidents and injuries. In response to this problem, a thorough review of machine learning techniques for detecting driver drowsiness was conducted. The review examined a range of techniques, including more recent approaches that use machine learning and deep learning algorithms as well as different types of data sources driver behaviours, physiological signals, and vehicle behaviours. The primary objective of this paper was to critically analyse and provide a comprehensive overview of the current state-of-the-art in detecting driver drowsiness, evaluate the effectiveness of each technique in terms of accuracy and reliability, and identify potential areas for future research and improvement. In order to achieve this, a systematic review of relevant research studies was undertaken. The review determined that machine learning-based techniques can improve the accuracy and reliability of driver drowsiness detection systems. However, certain limitations, such as the need for large amounts of data, feature extraction, and model structure, must be addressed. By overcoming these limitations, machine learning-based systems have the potential to enhance road safety and prevent accidents. In conclusion, this paper provides a thorough review of machine learning techniques for driver drowsiness detection, evaluates their effectiveness, identifies potential research directions, and highlights their significance and contribution to road safety. The insights gained from this study can be used to guide the development of more effective driver drowsiness detection systems and improve road safety for the community.

Application of Machine Learning Techniques for Detection and Segmentation of Brain Tumors

Application of Machine Learning Techniques for Detection and Segmentation of Brain Tumors

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

Classification of malware using multinomial linked latent modular double q learning

In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detection that extracts the behavior based on the network pattern. The behavior is extracted which are grouped to perform on the path protocol for analyzing repeated behaviors. Finally, the modular double q learning malware classification model is the grouped behaviors for significant malware detection. The effectiveness of proposed MLLD-M DQL method is compared with existing models. The results obtained by the proposed method show that the model combined with the machine learning (ML) significantly determined malware detection time and also reduced the false positive rate (FPR). The results showed that the false positive rate is significantly reduced by 42% for the proposed method better when compared to the existing behavior based malware detection model that obtained 62% of FPR.

Analysis of Methods of Machine Learning Techniques for Detection and Classification of Brown Spot (Rice) Disease

Rice is a major crop and the increased production is pertinent to ensure our food security. Modern techniques and advancements are required in agriculture to enable us to improve productivity and environment-friendliness as well as improve their farming conditions. Automatic disease detection techniques can help in identifying the various rice plant diseases. In agriculture, image processing is commonly used to obtain valuable information about crops. Images are often considered a source of information and data. Machine learning (ML) is a promising modern technique for image processing, and it has been successfully used in various areas such as agriculture. This paper aims to study the various research efforts that are focused on this technology in the field of agriculture. One particular class of ML that is commonly used in this work is convolutional neural networks. Here convolutional neural networks (CNNs) have been developed over the support vector machine (SVM) techniques to identify rice diseases (Brown spot) and measure their accuracy. Their potential applications include the development of screening tools and solutions for agricultural production. This paper presents an overview and research outcomes using SVM and CNN techniques in the study of agricultural problems. This study includes a dataset of 1488 healthy leaves and 523 brown spot leaf data samples. The research outcomes are based on studies and development using SVM and CNN. ML techniques give 82% accuracy using the SVM classification method, while the CNN method gives 95% accuracy.

Implementation of Machine Learning Techniques for detection of Lung Cancer

Implementation of Machine Learning Techniques for detection of Lung Cancer

A Survey on Hardware Vulnerability Analysis Using Machine Learning

Electronic systems rely on efficient hardware, popularly known as system-on-chip (SoC), to support its core functionalities. A typical SoC consists of diverse components gathered from third-party vendors to reduce SoC design cost and meet time-to-market constraints. Unfortunately, the participation of third-party companies in global supply chain introduces potential security vulnerabilities. There is a critical need to efficiently detect and mitigate hardware vulnerabilities. Machine learning has been successfully used in hardware security verification as well as development of effective countermeasures. There are recent surveys on hardware Trojan detection using machine learning. To the best of our knowledge, there are no comprehensive surveys on utilization of machine learning techniques for detection and mitigation of a wide variety of hardware vulnerabilities including malicious implants (e.g., hardware Trojans), side-channel leakage, reverse engineering, and supply-chain vulnerabilities (e.g., counterfeiting, overbuilding and recycling). In this paper, we provide a comprehensive survey of hardware vulnerability analysis using machine learning techniques. Specifically, we discuss how existing approaches effectively utilize machine learning algorithms for hardware security verification using simulation-based validation, formal verification as well as side-channel analysis.

A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients

COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.

Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network

Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

Detection of Retinal Disease using Image Processing

India is one of the countries which is emerging in the field of telemedicine in recent years. We are still far away from our desired goal. To add to that the patients with eye diseases are also increasing rapidly. To provide them with a better treatment at a lower price is the main goal. The people in urban areas still manage an eye checkup but for the people in rural areas it becomes difficult. Mobile phones are reaching to every nook and corner of the country with the help of that telemedicine becomes possible. We want to come up with a solution in which this becomes possible. It is applied on image processing and machine learning. Image processing is having significance for disease detection on medical images. With help of image processing and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. This project synopsis describes the application of various image processing and machine learning techniques for detection of eye diseases. Data is the future of technology. With the technological revolution the amount of data is increasing rapidly in any field. Thus using this data to distinguish between two images becomes our primary goal. The preprocessing technique leads to enhance the boundaries and feature extraction process and along with conversion of image type and then by combining the image processing part with the machine learning part we are able to design the algorithm. For this we are using concept of Template Matching template is nothing but a sub image which is small. The goal is to find similarities in template and input image. Due to this idea process will be done easily at faster rate

Empirical Exploration of Machine Learning Techniques for Detection of Anomalies Based on NIDS

Computer crimes and attacks on data networks have increased significantly, so it has become necessary to implement techniques that detect these threats and safeguard the information of organizations. Network Intrusion Detection Systems (NIDS) allow detecting anomalies and attacks in real time, by analyzing the local and outgoing traffic of the network. At present, to improve its performance, it has been chosen to use Machine Learning (ML) techniques that automate these processes and improve the detection of an anomaly. This paper implements ML techniques through the use of datasets, in the context of a NIDS, for the detection and prediction of anomalies on networks. Tests were performed with non-supervised and supervised learning algorithms on NSL-KDD and UNSW-NB15 datasets. An exploratory analysis of data together with dimensionality reduction techniques allowed us to understand the nature of the data, prior to the modeling. The results show that the methodology can be extrapolated for real scenarios with different network configurations.

Novel machine learning techniques for detection of diabetes

Diabetes mellitus is a typical infection of human body brought about by a gathering of metabolic issue where the sugar levels over a drawn-out period is high. It influences various organs of the human body which in this way hurt an enormous number of the body's framework, specifically the blood veins and nerves. Early expectation in such illness can be controlled and spare human life. AI methods give productive outcome to remove information by developing anticipating models from demonstrative clinical datasets gathered from the diabetic patients. Extricating information from such information can be helpful to anticipate diabetic patients. In this work, we utilize four famous AI calculations, to be specific Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and C4.5 Decision Tree (DT), Random forest (RF), Logistic regression (LR) on grown-up populace information to anticipate diabetic mellitus. Logistic regression (LR), Support Vector Machine (SVM), Naive Bayes (GaussianNB) shows highest results.

An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks

The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.

UP-Fall Detection Dataset: A Multimodal Approach

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.

An analogy between various machine-learning techniques for detecting construction materials in digital images

Digital images and video clips collected at construction jobsites are commonly used for extracting useful information. Exploring new applications for image processing techniques within the area of construction engineering and management is a steady growing field of research. One of the initial steps for various image processing applications is automatically detecting various types of construction materials on construction images. In this paper, the authors conducted a comparison study to evaluate the performance of different machine learning techniques for detection of three common categorists of building materials: Concrete, red brick, and OSB boards. The employed classifiers in this research are: Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM). To achieve this goal, the feature vectors extracted from image blocks are classified to perform a comparison between the efficiency of these methods for building material detection. The results indicate that for all three types of materials, SVM outperformed the other two techniques in terms of accurately detecting the material textures in images. The results also reveals that the common material detection algorithms perform very well in cases of detecting materials with distinct color and appearance (e.g., red brick); while their performance for detecting materials with color and texture variance (e.g., concrete) as well as materials containing similar color and appearance properties with other elements of the scene (e.g., ORB boards) might be less accurate.

Timed Probabilistic Automaton: A Bridge between Raven and Song Scope for Automatic Species Recognition


 
 
 Raven and Song Scope are two, state-of-the-art automated sound analysis tools, based on machine learning techniques for detection of species vocalisations. Individually, these systems have been the subject of a number of reviews; however, to date there have been no comparisons made of their relative performance. This paper compares the tools based on six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential applications. Examining these tools, we identified that they fail to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables and clusters them into complex structures.
 
 

Investigating Machine Learning Techniques for Detection of Depression Using Structural MRI Volumetric Features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.

A comparison of machine learning techniques for detection of drug target articles

Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure.