Search

Cybercrime has been on a massive increase for several years. As more individuals conduct business and live their lives online, more criminals are turning to the internet to steal. With the developments in cybersecurity, adoption of best practices, cybersecurity awareness initiatives, and increased regulations and partnerships between businesses and governments to resolve the problems, cybercriminals may be feeling the pressure. In this paper, we are going to predict operating system vulnerabilities using machine learning algorithms. And evaluate their accuracies through training them. The aim of the paper is to use Machine Learning algorithms to predict with accuracy operating systems vulnerabilities. Different ML algorithms have been analyzed, such as Logistic Regression, Support Vector Machine and Random Forest. And, out of all of these algorithms, Random Forest had the highest accuracy of 99.33%. [ABSTRACT FROM AUTHOR]

Clinical laboratory testing is a crucial part of delivering high-quality health care. Lab tests are ordered by a doctor or another clinician to determine, control, or demonstrate a patient's condition. The process starts with the preparation of a pattern of the patient's blood, tissue, or other organic material, which is then sent to a lab to be uniquely identified and checked to make sure it is appropriate for the test. Some exams are graded by hand, but the majority are completed with the aid of technically advanced equipment. The electrocardiogram (ECG)is a crucial tool for identifying cardiovascular issues. The huge number of ECG records were formerly recorded on paper. Examining paper ECG recordings manually can be challenging and time-consuming. The Machine learning algorithms, Support vector machine and other algorithms in particular show promise for systems that have abilities to detect cardiovascular illnesses where the effectiveness differs in terms of accuracy, recall and precision. The proposed research provides a thorough analysis of several data models for predicting patient illness for clinical diagnosis in cardiology. [ABSTRACT FROM AUTHOR]

The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak impacted drastically to education and most of educational institutions started preferring online education for students. However, after the settlement of the pandemic there is uncertainty among people about whether they should prefer online education for furthermore or start in offline mode to make it more interactive, so this paper is about an analysis of people's sentiments and emotions through Tweets about COVID-19 Education. This paper aims to study the reaction of people around the world toward online education during COVID-19. This study is conducted on the basis of the responses of students, teachers, parents, college professors, etc. We started with labeling the data into three sentiments namely positive, neutral, and negative and for validation then we used Machine learning (ML) classifiers namely, Logistic regression, Decision tree, Random Forest, Multilayer Perceptron (MLP), Naïve Bayes, Support vector machine (SVM), K-nearest neighbors (KNN), and XG-Boost. Then we performed emotion detection by considering 5 emotions namely happy, surprise, sad, fear, and angry and for validation we used ML classifiers. After applying all these ML approaches, the XG Boost ML classifier achieved the highest accuracy of 94% in classifying the tweets as positive, neutral, or negative, and 96% accuracy in classifying the tweets as happy, surprised, sad, fearful, or angry. [ABSTRACT FROM AUTHOR]

Oxidative Coupling of Methane (OCM) is a chemical reaction which directly converts natural gas, primarily consisting of methane into value-added chemicals, specifically ethylene. The OCM process faces challenges in the limitations of yield and high separation cost in transforming natural gas into useful chemical products. Ethylene serves various purposes, including the production of fabricated plastics and is widely used as a raw material. This paper focuses on machine learning algorithms to predict ethylene production using a catalysis dataset. So initially the high ethylene yield is predicted from the 18 catalysts collected from the dataset. The clustering algorithm called K-Mean clustering and Agglomerative clustering is used to identify the high-yield ethylene. Among 18 catalysts, 9 elements are identified as the largest amount of ethylene from the catalysis data. The prediction models are used to predict the large yield of ethylene from the catalysis. The prediction model like Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Boosting (GB) are compared to identify the best model for the prediction of ethylene yield. Based on the analysis, the agglomerative clustering along with the Random Forest predicts the higher ethylene composition from the OCM dataset. The ethylene production of the Magnesium (Mg), Barium (Ba) and Zirconium (Zr) chemical compounds along with oxygen and methane is predicted with the error rate of 0.17, 0.89 and 0.99 respectively. [ABSTRACT FROM AUTHOR]

Chronic kidney disease, sometimes called chronic renal disease, is characterised by a typical kidney function or the incremental progression of renal failure over months or years. Due to an increasing patient population, a growing possibility of kidney disorders entering the death phase, and a poor prognosis for morbidity and mortality, chronic kidney disease (CKD) indicates a high burden on the healthcare system. Different complex conditions include developed stages in blood, anemia (low blood count), weak bones, and nerve damage. Early identification and medication prevents chronic uropathy is getting the worse situation. It is significant to have potential techniques during the initial analysis of CKD. In recent times, the Machine Learning (ML) methods had gain more attention due to its accurate and effective results. The primary objective of this task is to determine the presence of CKD. Hence in this paper A novel Hybrid Machine Learning based prediction of CKD is presented. For obtaining better and accurate results, two of the most popular ML classifiers SVM (Support Vector Machine) and Decision tree are combined as a hybrid model. This approach can give accurate and reliable outputs in form of accuracy, prediction time. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is actually a challenging and new issue within artificial intelligence discipline. One of the primary bottlenecks of daily speech emotion identification strategies is actually tonal perturbation of speech. The goal of this paper is actually using Support Vector Machine (SVM) classifier to categorizing 7 various feelings like anger, happiness, sad, disgust, normal, boredom and fear. The exploration of futures capabilities consists of pitch, Mel-frequency spectrum coefficients (MFCC), Spectro temporal characteristics, formants as well as power specifications. Within this paper we utilize 3 distinct Tamil emotional collection as well as attribute fusion method to blend all of the spatial element extractions. Performance examination is actually accomplished by utilizing the confusion matrix as well as the precision rate attained by 3 databases are actually 87.5%, 86.25% & 88.5&% and having a typical average accuracy pace of 87.4%. And finally, from the outcomes for spatial feature fusion method on various databases are actually compared and we examined SVM recognition exhibits much better functionality with the very best precision rates in database 3 with 90% in 10-fold for the collected Tamil Emotional database. [ABSTRACT FROM AUTHOR]

This research employs a novel approach to headcounting by comparing the performance of the ANN algorithm with that of the SVM algorithm. To determine how many individuals are moving under an overhead monitoring system, researchers have developed a revolutionary paper counting approach based on head recognition and tracking. Substances and Techniques: Twenty samples in all were taken from an electronic resource. We maintained a significance level of 0.05, a confidence interval of 95% (95% CI), an enrollment ratio of 1, and a significance level of 80% (G power). The precision value is calculated using the SPSS software. Result: The results of two separate t-tests show that the ANN algorithm outperformed the SVM method, although the difference is not statistically significant (p=0.721; p>0.05). In summary, it seems that the ANN algorithm outperforms the SVM method on the analysed datasets. [ABSTRACT FROM AUTHOR]

The goal of this study is to use Support Vector Machines (SVMs) to recognise handwritten characters and compare their predictive accuracy to that of Logistic Regression. In this paper, we explore the application of machine learning algorithms like Support Vector Machine and Logistic Regression to the problem of character recognition. For a 0.05 level of statistical significance, we need 15 samples out of a total of 30. The SVM algorithm achieves a higher recognition rate (96.58 percent) than the Logistic Regression algorithm (87.43 percent) when both are used for character recognition. A p-value of 0.005 indicates a statistically significant difference between the study groups. Support Vector Machine was found to have better prediction accuracy than the Logistic Regression algorithm in this study of character recognition. [ABSTRACT FROM AUTHOR]

False news is characterized as a made-up story with a goal to bamboozle or to delude. In this paper we present the answer for the errand of fake news discovery by using Deep Learning structures. Fake news identification is carried out utilizing two AI calculations, Natural Language Processing Algorithm(N=10) and Deep Learning algorithm(N=10) calculations. False and True these two sorts of dataset is utilized for Fake news recognition, and it is gathered from kaggle.com. Dataset comprises lines and 6 fundamental boundaries that are connected with the False news that information gathered from twitter. For each gathering more than 30 examples are taken, and it is separated into preparing and testing. Accuracy for Natural Language handling calculation is 91.300% and for Support Vector Machine calculation is 77.500%. There exists an insightful critical distinction between Natural Language Processing Technique and Support Vector Machine calculations with p<0.05. Fake news location utilizing Natural Language Processing calculation seems to acquire higher precision than the Support Vector Machine calculation. [ABSTRACT FROM AUTHOR]

The rapid developments of deep learning techniques are the process that can easily create and use a fake media known as Deepfakes. Deep learning techniques can generate facial motion transferring, gender-changing, and face swapping with others. Currently, it is possible to create hyper-realistic digital media like images and videos through deep learning techniques that are easily accessible. However, that becomes a threat for everyone, if used for harmful purposes. This work presents how deep learning is used for Deepfake generation and detection. There are two methods that are most popular for Deepfake generation: Generative Adversarial Networks (GANs) and autoencoder techniques that is presented in this work. It also, introduces a survey of some researchers' works and methods that used for Deepfake detection during the recent years. This paper proposed a method to discriminate if that video is real or fake by using a Support Vector Machine (SVM) classifier, the method presented in this work for deepfake detection is based on Gray Level Co-occurrence Matrix (GLCM) as a feature extraction method applying on 4 bands to obtain 13 feature form each one to achieve good accuracy of the classifier with a score 70.47% that batter than previous work that didn't exceed 60% of accuracy. [ABSTRACT FROM AUTHOR]

Techniques for detecting oral cancer are a developing area in medicine that includes cancer diagnosis and early detection. For the classification of oral cancer, we have tried to develop machine learning models like logistic regression (LR), naive Bayes (NB), K-Nearest Neighbors (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), as well as hybrid models like KNN-SVM and SVM-KNN and deep learning models like Xception, VGG, CNN, Inception, and MobileNet. A few researchers have already used the aforementioned models to categorize oral cancer using genetic data. Nevertheless, the database used for this study includes pictures of the lips and tongue that have been divided into malignant and non-cancerous categories. With the assistance of ENT doctors, the photos were categorized after being captured in various ENT hospitals in Ahmedabad. To determine which model is the most effective, the ML, hybrid, and DL models are evaluated based on numerous evaluation criteria. Experimental result shows that the accuracy, precision, recall, and Fl score of classification of the ML, Hybrid, and deep learning are discussed in this research paper. Out of which hybrid model SVM-KNN with PCA has 74%, Gradient boosting with PCA with 73%, and deep learning model VGG has I 00% accuracy. Hence, it may be concluded that Gradient boosting with PCA and advanced deep learning models like VGG, and Xception is the most favorable model for predicting the survival rate of oral cancer patients by classifying them into cancerous or non-cancerous. This research study motivates towards the early prediction comparatively reduces the severity, and economic cost as the treatment for early diagnosis is less. [ABSTRACT FROM AUTHOR]

Now a day's Internet of Thing's (IoT's) is considered growing technology. Ever increasing demand of IoT devices must have reliable channels for communication therefore, security needs to be ensured. Wireless enabled IoT use to have many real-time applications which include smart education, intelligent transportation, smart home, smart grid, health care and farming etc. Data collection can be made possible with the help of massive IoT devices. Intruder directly sends illegal data packets which lead to hijack overall network. IoT-networks have improved human experience in many real-time applications. Cyber-security threats usually limit computational resources. Therefore, detection of cyber-threats is the main problem for IoT-networks. This paper presents a novel concept of leNet model-based IDS to detect cyber- attacks. Although, two benchmark datasets such as IoTID20 and CICIDS2017 are utilized for simulation. LeNet model is compared with other traditional techniques like linear discriminant analysis, quadratic discriminant analysis, support vector machine and multi-layer perceptron. Accordingly, metrics like accuracy, precision, recall, F1-score, confusion matrix and correlation matrix are used for performance evaluation. Extensive analysis is performed which indicates that with IoTIDS20 dataset, LeNet achieves recall-99.91%, precision-99.74%, F1-score-99.83% and accuracy-99.68%. While, using CICIDS2017 dataset, LeNet presents optimal results in terms of recall-97.96%, precision-96.41%, F1-score- 97.18% and accuracy-98.87%. Proposed LeNet model is light weighted and adaptable. [ABSTRACT FROM AUTHOR]

Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms. [ABSTRACT FROM AUTHOR]

For the majority of IoT applications, positioning is critical. For LoRa IoT devices, global positioning system (GPS) chips are too pricey and power-hungry. A rapidly developing technology is LoRa. LoRa technology benefits embedded devices that run on batteries. Long-range low-power LoRa technology. Alternatively LoRa GPS can be used. In this paper, the localization of Fingerprint was used by three algorithms K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM)and these algorithms were evaluated in which the error is the least possible. Where the lowest RMSE in all regions and the second region for ANN was 22.88138 in all regions and 25.57278 in the second region. As for the first region, the lowest RMSE for optimizable SVM was 11.099. [ABSTRACT FROM AUTHOR]

Premature birth (PTB) is the most common public health issue in gynecology and obstetrics. It has a variety of consequences, ranging from increased infant mortality to chronic disease and disability throughout one's life. Many PTB prediction studies have been published in recent years to improve the accuracy of PTB prediction. However, no single study has addressed the issue of premature birth using a data set from the Iraqi community. There are many research from different countries that have been collected regarding premature birth, where the pregnant woman differs from one region to another in terms of lifestyle as well as the diagnostic methods used, where in Iraq only manual methods are adopted in the diagnosis. The aim of this paper is to provide and improve the prediction accuracy of premature births using real data from Iraqi hospitals. Additionally, provide a validation of the results in terms of reliability and accuracy. The novel approach is used to identify the best maternal characteristics (those associated with PTB) from an obstetric dataset. The following classifiers (Decision Tree, Random Forest, Logistic Regression, K-Neighbor Nearest, Naive Bayes Classifier, Neural Networks, Support Vector Machine) were used in this study to classify all birth cases into term normal birth and preterm birth. As a result, the Random Forest classifier outperformed all other classifiers with a 98% percent accuracy. Its worth noting that the classifiers' performance is quantified in terms of accuracy, sensitivity, Specificity, Precision, Recall and F1 score. [ABSTRACT FROM AUTHOR]

After a deep questioner among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies. [ABSTRACT FROM AUTHOR]

Data security has become a major issue as digitalization across the world is rapidly increasing. Personal information of the user is extracted and exploited using one of the cyber-attack known as phishing. To detect phishing, and to avoid the user from visiting a non-legitimate website, a method is proposed in this paper. This method uses unique features of Uniform Resource Locator which can differentiate between legitimate and non-legitimate websites. To classify websites, Random forest and Support Vector Machines are two machine learning methods that are employed in this work. [ABSTRACT FROM AUTHOR]

Customer churn referred as customer who ceases his relationship with service provider. Customer churn analysis is performed to retain existing customers from churning. Churn prediction is gaining importance in telecom sector now a days because of its importance to analyze behaviors of different customers to predict who are in verge in leaving the subscription from service provider. Retaining existing clients is cheaper than gaining a newer one. This paper focuses on various machine learning and neural network methods to predict customer churn and identify the customers who are closer to leave their service from the respective service provider. Machine learning algorithms that are used to predict the customer churn such as Logistic Regression, Support Vector Machine, Random Forest, Adaboost and Artificial Neural Network. Data is collected from AT&T website and learning models are evaluated based on two criteria's i.e., Accuracy and Area under curve giving special weightage on Accuracy. Algorithm that proves to be the most accurate in all the test cases is compared and performance is measured. [ABSTRACT FROM AUTHOR]

Aim: This research work carried out to determine the accuracy in leaf disease detection using Naive Bayes (NB) classification algorithm and comparing its accuracy features with Support Vector Machine (SVM) algorithm for improving the accuracy of the prediction. Methods: In this proposed paper, the plant leaf disease detection has been carried out as an experiment using machine learning techniques such as SVM (N=10) and NB classification (N=10). The accuracy is evaluated for leaf disease detection. Result: The NB classification and the SVM algorithms were implemented and compared accuracy results. The SVM appears to be more significant with 95% accuracy than NB with 91% with significant value (0.081). Conclusion: The result shows that SVM algorithm's accuracy appeared to be better than other machine learning algorithms for leaf disease detection. [ABSTRACT FROM AUTHOR]

This paper presents machine learning algorithms for predicting different stages of breast cancer tumour such as benign and malignant. Machine learning (ML) models such as Logistic regression (LR), Decision tree (DT), Naive Bayes (NB), Support vector machine (SVM), K-nearest neighbour (KNN), Random Forest (RF) and XG Boost (XGB) used for predicting different stages of breast cancer. The Breast cancer data is collected from UCI machine learning Repository, this data is used training and testing the machine algorithms. Performance and evaluation of machine learning algorithms using confusion matrix and metrics are used Accuracy, Precision, Recall and f1-score. Support vector machine (SVM) performed better result of metrics score to compared with other machine learning algorithms. [ABSTRACT FROM AUTHOR]

Occupancy detection is critical during building operations, particularly in energy efficiency, space utility, heating, ventilation control, and air conditioning (HVAC) to optimize user comfort. However, applying occupancy sensors to detect occupants can violate a person's privacy, and the resulting data could be more precise. Therefore, this study aims to detect occupancy through environmental sensors by utilizing the accuracy of machine learning-based classification results. The ultimate goal of this study is to detect whether there are people in a room based on the benchmarks studied. Benchmarks for classifying whether people are in a room include CO2, humidity, lighting, temperature, and occupancy. This paper presents a method for comparing and evaluating a set of different machine learning techniques based on a given performance measure (for example, precision, accuracy, f1 score, and algorithm recall) and then using the algorithm with the best performance. This study uses five machine learning classification algorithms: K-Nearest Neighbors, Support Vector Machine, Decision Tree, Naive Bayes and Random Forest, and the method used to evaluate the results is cross-validation. Experiments were conducted using occupancy detection datasets from the UCI Machine Learning Repository. After performing the experiment, the predictive results of the five algorithms are high. That is, each has an accuracy value of more than 90%. Support Vector Machine has the highest accuracy value compared to other algorithms, namely 98.5%, and Decision Tree has the lowest accuracy value, namely 91.9%. The results show that selecting the right features and the suitable classification model can significantly impact prediction accuracy. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) outbreak–first emerged in December 2019–has left a mark on how people throughout the world live, particularly students at educational institutions. Those students had to prepare for the rest of their studies emotionally and physically by adjusting to virtual teaching and learning methods, which could make a substantial impact on their academic achievement. As a result, this study was carriedout to investigate students' academic performance expectations depending on their various localities during the pandemic to forecast their academic success for the following semester. This research enables a variety of steps to be constructed to improve and maintain students' achievement in their learning activities. The research involved university students from the University of Education Sultan Idris' Faculty of Science and Mathematics. The Support Vector Machine model was used to predict a student's academic performance based on the respective locality in this paper. [ABSTRACT FROM AUTHOR]