Machine Learning Applications in Engineering Education and Management <h3>Machine Learning Applications in Engineering Education and Management (ISSN: 2984-7834)</h3> <p style="text-align: justify;"><strong>Frequency:</strong> Bi-Annually (Two times a year)</p> <p style="text-align: justify;"><strong>Article Processing Charges:</strong> None</p> <p style="text-align: justify;"><strong>About MLAEEM Journal</strong></p> <p style="text-align: justify;">Machine Learning Applications in Engineering Education and Management (MLAEEM) is a peer reviewed, open access journal focused on research related to <strong>machine learning</strong>. The journal encompasses all aspects of research and development in ML, including but not limited to data mining, computer vision, natural language processing (NLP), intelligent systems, neural networks, AI-based software engineering, bioinformatics, and their applications in the areas of engineering, business, and social sciences. It covers a broad spectrum of applications in the community, from <strong>industry</strong>, <strong>government</strong>, and <strong>academia</strong>.<br /><br />The journal publishes research results in addition to new approaches to ML, with a focus on value and effectiveness. Application papers should demonstrate how ML can be used to solve important practical problems. Research methodology papers should demonstrate an improvement to the way in which existing ML research is conducted.<br /><br />Submissions must be novel, technically sound, and clearly presented. MLAEEM accepts both regular papers and technical notes (technical notes are limited to a maximum of 12 pages). In addition, survey articles and discussion papers on ML are welcome.<br /><br />Submissions meeting journal criteria will undergo a double-blind review process, utilizing a minimum of two (2) external referees. Our dedicated editorial team, together with active researchers from all areas of ML, ensure that papers move through the evaluation and review as fast as possible without compromising on the quality of the process.<br /><br />The journal audience comprises academia, industry, and practitioners. Authors are strongly encouraged to make their datasets publicly accessible via a repository of their choosing. Please see our Guide for Authors for information on article submission</p> Yashika Journal Publication Private Limited en-US Machine Learning Applications in Engineering Education and Management 2984-7834 <p></p> Microarray Cancer Classification with Stacked Classifier in Machine Learning Integrated Grid L1-Regulated Feature Selection <p>Cancer is a group of diseases leads to higher mortality rate due to abnormal growth of cell within body tissues. Microarray dataset incorporates features those are challenge due to higher inherent complexity within the data. Through incorporation of the microarray dataset, different characteristics disciplines are incorporated for the different applications. The microarray dataset incorporates the higher dimensionality of data, size of sample, variance in features and imbalance in the class. To overcome the issues associated with the microarray dataset feature reduction and technique for the dimensionality reduction are implemented for the diagnosis and classification. To achieve the effective classification in the microarray dataset Stacked Machine Learning Classifier (SMLC) is developed. The developed SMLC model incorporates the different classifier those are stacked within the Machine learning model. The stacked classifier comprises of the k-nearest classifier, Random Forest and logistics classifier for the classification with the L1-regularization. Through the ranking based approach feature selection is performed within the microarray datasets. The performance evaluation of the proposed SMLC model exhibits the higher classification accuracy of the 99%. Additionally, the proposed SMLC model achieves the 97% of the precision, recall and F-measure.</p> Dr. S. K Hasane Ahammad Copyright (c) 2022 Author 2022-06-30 2022-06-30 2 1 01 10 EEG Signal Classification with Machine Learning model using PCA feature selection with Modified Hilbert transformation for Brain-Computer Interface Application <p>Brain Computer Interfaces (BCI’s) computes the communication and control channels that rely on the output channel normal brain for the peripheral nerves. The present BCI advancement incorporates the technologies for augmentative communication between the users for the conventional technique for the effective control of muscles. The application of the Electroencephalography (EEG) activity estimates the brain activity for effective communication technology. This paper presented a Principal Component Analysis (PCA) integrated with the Modified Hilbert Transformation defined as the PCAHT. The developed model uses the PCA model for the estimation of the feature selection in the EEG signal for processing. The EEG signals inputs are pre-processed and evaluated for the features within the signal for processing. The proposed PCAHT model incorporates the modified Hilbert Transformation model for signal conversion and is applied over the machine learning model. The deployed PCAHT model perform the classification of the EEG signal those are normal and abnormal activity in the brain signals. The simulation analysis expressed that proposed PCAHT model achieves a higher accuracy value of 98%, recall value is measured as 98% and precision value is measured as the 98%. The comparative analysis observed that the proposed PCAHT model achieves the ~4% improved performance than the conventional classifier models.</p> Dipannita Mondal Sheetal S. Patil Copyright (c) 2022 Author 2022-06-30 2022-06-30 2 1 11 19 Intrusion Detection System Attack Detection and Classification Model with Feed-Forward LSTM Gate in Conventional Dataset <p>With the increased network scale, intrusion detection is more frequent, advanced, and volatile for capturing large scale is challenging. The increased malicious attacks in the network system affect the security tool in the network causing illegal users, reliability, and robustness for network security. Recently, network security is increased with the illegal intrusion detection system for the security in each occasion for the sensitive users, governments, enterprises and governments. Network Intrusion Detection (NIDS) exhibits a reliable and effective technological form for the packets in the network, unauthorized users, and traffic monitoring for the computer network. The incorporation of the machine learning model exhibits effective performance for network traffic monitoring. Additionally, the NIDS model exhibits effective attack detection and traffic malicious activities in the network. Through intelligent capability, the machine learning model comprises of intrusion detection based on rule-based solution for the network attacks. This paper proposed a Long Short Term Memory Gate Recurrent Neural Network (LSTMgateRNN). The constructed LSTMgateRNN model comprises of the LSTM features for the attribute evaluation for the attack data processing. The incorporation of the gate function within the network the incorporated gate function increases the evaluation of the attributes in the network. Finally, the processed data is examined with the RNN model to perform the attack detection and classification. The proposed LSTMgateRNN model performance is evaluated for the three different datasets such as KDD’99, NSL-KDD and UNSW-SW15. Simulation analysis exhibited that proposed LSTMgateRNN model achieves an attack detection rate of 99%. The proposed LSTMgateRNN is comparatively examined with the with the existing model and achieves the ~6 – 12% improved performance.</p> Pravin R. Kshirsagar Rakesh Kumar Yadav Nitin Namdeo Patil Mali Makarand L Copyright (c) 2022 Author 2022-06-30 2022-06-30 2 1 20 29 Natural Language Processing Based on Name Entity With N-Gram Classifier Machine Learning Process Through GE-Based Hidden Markov Model <p>Natural Language Processing (NLP) is the computational linguistics mode for the identification and classification of text documents. The NLP process comprises the retrieval of the information for the classification of the sub-fields in the group based on the text document entities. With the higher morphological characters, it is challenging for the identify the entities in the languages. Recently, the machine learning model is deployed to evaluate the training data quality for the processing of text information data. This paper Hidden Markov Model (HMM) integrated with the name entity model for the categorization of the variables in the network. The HMM model uses the probability state such as start, transition and emission probability information for the computation of the sub-domain in the world files. The HMM model uses the n-gram classifier for the categorization for the sub-domain variables. The HMMNE model computes the ranking probability state of the work entities for the categorization of the variables in the network. The proposed HMMNE model achieves the higher precision value for the location with 99% and for the name and organization the precision state is achieved as the 98%. The examination of the developed HMMNE model is effective for the classification and identification of the variables in the name file entities variables.</p> Sandeep Dwarkanath Pande R. Kishore Kanna Imran Qureshi Copyright (c) 2022 Author 2022-06-30 2022-06-30 2 1 30 39