Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier’s performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.

Nowadays, large amount of data is available everywhere. Therefore, it is very important to analyze this data in order to extract some useful information and to develop an algorithm based on this analysis. This can be achieved through data mining and machine learning. Machine learning is an integral part of artificial intelligence, which is used to design algorithms based on the data trends and historical relationships between data. Machine learning is used in various fields such as bioinformatics, intrusion detection, Information retrieval, game playing, marketing, malware detection, image deconvolution and so on. This paper presents the work done by various authors in the field of machine learning in various application areas.

The advent of various machine learning methods can add a distinct dimension to the domain of recognition. The realm of pattern recognition has been deeply influenced by the ongoing trend of artificial learning-based methodologies. The two-dimensional structure of mathematical symbols and expressions makes recognition tasks more difficult, particularly for mathematical expressions. This paper delves into recognition approaches based on machine learning and deep learning. The leading recognition algorithms from both categories, SVM, and CNN, have been deployed to recognize the Hasyv2 dataset. The competent accuracies of 62.3% and 76.21% have been given by SVM and CNN, respectively.

Alzheimer’s disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

Purpose: The purpose of this paper is to develop a prediction model to study the factors affecting the academic performance of students pursuing an undergraduate professional course (BCA). For this purpose, the ensemble model of decision tree, gradient boost algorithm and Naïve Bayes techniques is created to achieve best and accurate results. Monitoring the academic performance of students has emerged as an essential field as it plays a vital role in the accurate development and growth of students’ critical and cognitive thinking. If the academic performance of students during the initial years of the graduation can be predicted, different stakeholders, i.e. government, policymakers, academicians, can be helped to make significant remedial strategies. This comprehensible practice can go a long way in shaping the ideologies of young minds, enhancing pedagogical practices and reframing of curriculum. This study aims to develop positive steps that can be taken to enhance future endeavours in the field of education.Design/methodology/approach: A questionnaire was prepared specifically to find out influential factors affecting the academic performance of the students. Its specific area of investigation was demographic, social, academic and behavioural factors that influence the performance of the students. Then, an ensemble model was built using three techniques based on accuracy rate. A 10-fold cross-validation technique was applied to access the fitness of results obtained from proposed ensemble model.Findings: The result obtained from ensemble model provides efficient and accurate prediction of student performance and helps identify the students that are at risk of failing or being a drop-out. The effect of previous semester’s academic performance shows a significant impact on current academic performance along with other factors (such as number of siblings and distance of university from residence). Any major mishap during past one year also affects the academic performance along with habit-based behavioural factors such as consumption of alcohol and tobacco.Research limitations/implications: Though the existing model considers aspects related to a student’s family income and academic indicators, it tends to ignore major factors such as influence of peer pressure, self-study habits and time devoted to study after college hours. An attempt is made in this paper to examine the above cited factors in predicting the academic performance of the students. The need of the hour is to develop innovative models to assess and make advancements in the present educational set-up. The ensemble model is best suited to study all factors needed to accomplish a robust and reliable model.Originality\value: The present model is developed using classification and regression algorithms. The model is able to achieve 99 per cent accuracy with the existing data set and is able to identify the influential factors affecting the academic performance. As early detection of at-risk students is possible with the proposed model, preventive and corrective measures can be proposed for improving the overall academic performance of the students.