Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments by Hanan Abbas Mohammad

“…Hence, a number of researchers are attempting to build an intrusion detection system utilizing machine learning and deep learning algorithms. In this work, a novel attack detection model is proposed by superimposing Whale Optimization Algorithm and Bidirectional Long Short-Term Memory (WB-LSTM) together. …”
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A Roadway Safety Sustainable Approach: Modeling for Real-Time Traffic Crash with Limited Data and Its Reliability Verification by Zhenzhou Yuan, Kun He, Yang Yang

“…Finally, the applicability of machine learning and statistical methods to low precision data set was compared. …”
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Investigations on tool wear behavior in turning AISI 304 stainless steel: An empirical and neural network modeling approach by Satish Chinchanikar, Mahendra Gadge

“…An ANN was modeled using a feedforward backpropagation machine learning technique. In this study, a higher prediction accuracy of 0.9975 was achieved with ANN model as compared to the empirical model. …”
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Using Burstiness for Network Applications Classification by Hussein Oudah, Bogdan Ghita, Taimur Bakhshi, Abdulrahman Alruban, David J. Walker

“…This paper proposes a novel flow statistical-based set of features that may be used for classifying applications by leveraging machine learning algorithms to yield high accuracy in identifying the type of applications that generate the traffic. …”
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Naive Bayes-Guided Bat Algorithm for Feature Selection by Ahmed Majid Taha, Aida Mustapha, Soong-Der Chen

“…Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. …”
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An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival by Sören Schleibaum, Jörg P. Müller, Monika Sester

“…To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. …”
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Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review by Mei Yang, Shah Nazir, Qingshan Xu, Shaukat Ali

“…The research finds novel means to make the decision support system for the problems of big data using multiple criteria in integration with machine learning and artificial intelligence approaches.…”
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A Systematic Literature Review on Defense Techniques Against Routing Attacks in Internet of Things by Lanka Chris Sejaphala, Vusimuzi Malele, Francis Lugayizi

“…., Secure-Protocol, conventional Intrusion Detection Systems (IDS), and Machine Learning (ML)-based. This systematic literature review explores 39 published papers in the domain of defense techniques against routing attacks in RPL-based IoT. …”
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Effectiveness of the Spatial Domain Techniques in Digital Image Steganography by Rosshini Selvamani, Yusliza Yusoff

“…In addition to using statistics as a foundation, convolution neural networks (CNN), generative adversarial networks (GAN), coverless approaches, and machine learning are all used to construct steganographic methods. …”
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Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors by Mitra Madanchian, Hamed Taherdoost

“…Change aversion, data security worries, and integration expenses are major roadblocks, but strong digital leadership, company culture, and advancements in NLP and machine learning are key enablers. This paper presents a complex analysis that questions the common perception of AI as only disruptive by delving into the relationship between power dynamics, corporate culture, and technology infrastructures. …”
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Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics by Carlos González-Gutiérrez, María Luisa Sánchez-Rodríguez, José Luis Calvo-Rolle, Francisco Javier de Cos Juez

“…The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN) is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. …”
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Fault Prediction of Centrifugal Pump Based on Improved KNN by YunFei Chen, Jianping Yuan, Yin Luo, Wenqi Zhang

“…To effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is proposed. …”
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AI-assisted discovery of quantitative and formal models in social science by Julia Balla, Sihao Huang, Owen Dugan, Rumen Dangovski, Marin Soljačić

“…Here, we demonstrate the use of a machine learning system to aid the discovery of symbolic models that capture non-linear and dynamical relationships in social science datasets. …”
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Validation and Calibration of an Agent-Based Model: A Surrogate Approach by Yi Zhang, Zhe Li, Yongchao Zhang

“…In this paper, we present a surrogate analysis method for calibration by combining supervised machine-learning and intelligent iterative sampling. Without any prior assumptions regarding the distribution of the parameter space, the proposed method can learn a surrogate model as the approximation of the original system with a relatively small number of training points, which will serve the needs of further sensitivity analysis and parameter calibration research. …”
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Few-Shot Contrail Segmentation in Remote Sensing Imagery With Loss Function in Hough Space by Junzi Sun, Esther Roosenbrand

“…Traditional computer vision techniques struggle with varying imagery conditions, and supervised machine learning approaches often require a large amount of hand-labeled images. …”
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Research and Model Prediction on the Performance of Recycled Brick Powder Foam Concrete by Hongyang Xie, Jianjun Dong, Yong Deng, Yiwen Dai

“…In this paper, the effect of air bubble swarm admixture, recycled brick powder admixture, water to material ratio, and HPMC content on the physical and mechanical properties of recycled brick powder foam concrete was investigated by conducting a 4-factor, 5-level orthogonal test with recycled brick powder as fine aggregate, and the effect of each factor on the physical and mechanical properties of recycled brick powder foam concrete was derived, and the optimum ratio of recycled brick powder foam concrete was determined by analysing the specific strength. Five machine learning models, namely, back propagation neural network improved by particle swarm optimization (PSO-BP), support vector machine (SVM), multiple linear regression (MLR), random forest (RF), and back propagation neural network (BP), were used to predict the compressive strength of recycled brick powder foam concrete, and the PSO-BP model was found to have obvious advantages in terms of prediction accuracy and model stability. …”
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Improving ocean reanalyses of observationally sparse regions with transfer learning by Simon Lentz, Sebastian Brune, Christopher Kadow, Johanna Baehr

“…Consequently, with infrequent input data, machine learning reconstructions exhibit similar physical structures, while correcting for known errors compared to state-of-the-art data assimilation products. …”
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Federated Learning Based on OPTICS Clustering Optimization by Chenyang Lu, Su Deng, Yahui Wu, Haohao Zhou, Wubin Ma

“…Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. Each client node uploads the trained model parameter information to the central server based on the local training data, and the central server aggregates the parameter information to achieve the purpose of common training. …”
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Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm by Babuli Sahu, Sangram Keshari Swain, Sudheer Mangalampalli, Satyasis Mishra

“…., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.…”
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Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach by Sanaz Zamani, Minh Nguyen, Roopak Sinha

“…This study presents a groundbreaking IoT-based system that integrates big data analytics, fuzzy logic, and machine learning to revolutionise mental health monitoring. …”
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