Search

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation. This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently. Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable. [ABSTRACT FROM AUTHOR]

This paper delves into the critical aspect of demand forecasting within the broader context of optimizing drop trailer management in volatile networks, with a specific focus on a large pallet manufacturer's supply chain operation. The study underscores the importance of accurate demand forecasting as a foundational element for informing subsequent optimization models. The main objective is to enhance our sponsor company's supply chain efficiency by accurately predicting future trailer requirements, which is crucial for the subsequent development of an effective inventory control and optimization model. This research utilizes forecasting methods like Gradient Boosted Trees and highlights the challenges of traditional forecasting methods in the context of our sponsor company's complex and dynamic supply chain network. The demand forecast is meant to inform the development of optimization models crucial for ensuring the effective allocation and management of trailer assets, ultimately leading to cost reductions and improved service levels within our sponsor company's network. The study contributes significantly to supply chain management literature by showcasing the application of sophisticated forecasting techniques in a real-world context and setting the stage for the development of robust optimization models in the domain of drop trailer management. [ABSTRACT FROM AUTHOR]

Ambulance travel time estimations play a pivotal role in ensuring timely and efficient emergency medical care by predicting the duration for an ambulance to reach a specific location. Overlooking factors such as local traffic situations, day of the week, hour of the day, or the weather may create a risk of inaccurately estimating the ambulance travel times, which might lead to delayed emergency response times, potentially impacting patient outcomes. In the current paper, we propose a novel framework for accurately estimating ambulance travel times using machine learning paradigms, employing real-world spatiotemporal ambulance data from the Skane region, Sweden. Our framework includes data preprocessing and feature engineering, with a focus on variables significantly correlated with travel time. First, through a comprehensive exploratory data analysis, we highlight the main characteristics, patterns, and underlying trends of the considered ambulance data set. Then, we present an extensive empirical analysis comparing the performance of different machine learning models across different ambulance travel trip scenarios and feature sets, revealing insights into the importance of each feature in improving the estimation accuracy. Our experiments indicate that the aforementioned factors play a significant role when estimating the travel time. [ABSTRACT FROM AUTHOR]

This research paper focuses on comparing two Machine Learning (ML) approaches—Time Series Analysis and a Feature-Based Prediction—for predicting traffic accident counts. Spanning the years 2005 to 2016 in Paris, the study leverages a comprehensive dataset that includes attributes such as weather conditions. Following dataset preprocessing and partitioning into training and test sets, the research systematically develops and evaluates these two contrasting ML modeling approaches. Furthermore, the study extends its comparison by evaluating the performance of various ML models, emphasizing the power of the proposed Neural Network (NN) model. Metrics such as Mean Absolute Percentage Error (MAPE) are employed to assess and contrast the predictive capabilities of each model. The results not only showcase the effectiveness of the neural network model but also elucidate its superiority over alternative ML approaches, particularly in the context of traffic accident prediction. [ABSTRACT FROM AUTHOR]

Autonomous driving development requires rigorous testing in real-world scenarios, including adverse weather, unpredictable events, object variations, and sensor limitations. However, these challenging "corner cases" are elusive in conventional datasets due to their unpredictability, high costs, and inherent risks. Recognizing the critical role of ground truth data in autonomous driving, the demand for synthetic data becomes evident. Contemporary machine learning-based algorithms essential to autonomous vehicles heavily depend on labeled data for training and validation. Simulation of scenarios not only mitigates the scarcity of real-world data but also facilitates controlled experimentation in situations that are challenging to replicate physically. The challenge extends beyond data scarcity, encompassing the impediment posed by the inability to systematically control and manipulate specific scenarios, hindering progress. To overcome these challenges, we present CornerSim, a dynamic virtualization framework simplifying the creation and modification of diverse driving scenarios. Leveraging simulation, CornerSim generates synthetic environments for comprehensive testing, providing essential outputs like raw sensor data (cameras, LiDAR, etc.) and labeled data (object detection bounding boxes, classes, semantic segmentation). The unpredictable nature of real-world corner cases complicates obtaining a sufficiently large and diverse annotated dataset. CornerSim addresses this challenge by not only generating synthetic data but also supplying necessary ground truth for training and evaluating machine learning models. This paper emphasizes the introduction of CornerSim and its ability to challenges related to testing autonomous vehicles in realistic scenarios. It focuses on the framework's capabilities, design principles, and integration, with the goal of enhancing thorough testing and validation of autonomous driving systems in a simulated environment, improving their robustness and safety. Our approach involves running simulations to generate datasets, which are statistically studied and compared with real data. Furthermore, we apply state-of-the-art detection algorithms to assess if data generated by CornerSim is suitable for both training and validation stages. [ABSTRACT FROM AUTHOR]

The mixed data sampling (MIDAS) model has attracted increasing attention due to its outstanding performance in dealing with mixed frequency data. However, most MIDAS model extension studies are based on statistical methods or machine learning models, which suffer from insufficient prediction performance and stability in small sample environments. To solve this problem, this paper proposes a novel mixed frequency sampling discrete grey model (MDGM(1, N)), which is a coupled form of the MIDAS model and discrete grey multivariate model. By adjusting the structure parameters, the model can be adapted to different sampling frequencies data, and degenerate into several types of grey models. Then, the unbiasedness and stability of the model are proved using the mathematical analysis method and numerical random experiment. The meta-heuristic algorithm is introduced to obtain the optimal weight parameters and the maximum lag order, improving the model's fitting ability to mixed frequency data. To demonstrate the effectiveness of the new model, a model evaluation system consisting of traditional evaluation metrics and a monotonicity test is established. Taking four hard disk drive failure datasets as research cases, the performance of the proposed model is compared with seven mainstream benchmark models. The results show that the proposed model has excellent applicability and outperforms other competition models in terms of validity, stability, and robustness. Furthermore, it is observed that the reported uncorrectable errors and the command timeout have a greater impact on hard disk drive failure. Finally, the new model is employed to forecast the failure of four hard disk drives. The forecasting results indicate that in the next four time points with a cycle of 21 days beginning in April 2023, the failure of the smaller capacity hard disk drives (0055 and 0086, corresponding to 8TB and 10TB) show a decreasing trend, reaching 67.442% and 89.7683%, respectively. The failure of the other larger capacity hard disk drives (0007 and 0138, corresponding to 12TB and 14TB) has increased, with a growth rate of 17.1016% and 123.7899%. [Display omitted] • A novel mixed frequency sampling discrete grey model is proposed. • The proposed model is proved to be unbiased and stable theoretically. • A Chimpanzee Optimization algorithm is introduced to globally search for weight parameters and maximum lag order. • A new model evaluation system for evaluating the effectiveness of prediction models is constructed. • The proposed model outperforms the other seven benchmark models in four case studies. [ABSTRACT FROM AUTHOR]

Seamless positioning and navigation requires an integration of outdoor and indoor positioning systems. Until recently, these systems mostly function in-silos. Though GNSS has become a standalone system for outdoors, no unified positioning modality could be found for indoor environments. Wi-Fi and Bluetooth signals are popular choices though. Increased adoption of different machine learning techniques for indoor–outdoor context detection and localization could be witnessed in the recent literature. The difficulty in precise data annotation, need for sensor fusion, the effect of different hardware configurations pose critical challenges that affect the success of indoor–outdoor (IO) positioning systems. Wireless sensor-based techniques are explicitly programmed, hence estimating locations dynamically becomes challenging. Machine learning and deep learning techniques can be used to overcome such situations and react appropriately by self-learning through experiences and actions without human intervention or reprogramming. Hence, the focus of the work is to present the readers a comprehensive survey of the applicability of machine learning and deep learning to achieve seamless navigation. The paper systematically discusses the application perspectives, research challenges, and the framework of ML (mostly) and DL (a few) based positioning approaches. The comparisons against various parameters like the technology used, the procedure applied, output metric and challenges are presented along with experimental results on benchmark datasets. The paper contributes to bridging the IO localization approaches with IO detection techniques so as to pave the way into the research domain for seamless positioning. Recent advances and hence, possible future research directions in the context of IO localization have also been articulated. • IO localization methods are presented from pragmatic view of seamless positioning. • ML techniques are categorized based on their applications in seamless positioning. • Performance results of ML techniques on benchmark datasets are reported. • The open issues along with future directions are discussed. [ABSTRACT FROM AUTHOR]

This paper designs an interval type-2 fuzzy neural network sliding mode robust controller (IT2FNNSMRC) to improve the stability of the vibrational angle of the orbital plane in magnetic rigid spacecraft attitude control. The control system consists of an interval type-2 fuzzy neural network (IT2FNN) controller, a PD controller, and a robust controller in parallel connection. The IT2FNN controller, as a nonlinear regulator, compensates the nonlinearity of the controlled object; the PD controller, as a feedback controller, ensures the global asymptotic stability of the control system; the robust controller inhibits input load disturbance. The IT2FNN controller hereof has a self-organizing function which enables it to automatically determine the network structure and parameters online. At the stage of IT2FNN structure learning, the standard on rule growth is set according to the incentive intensities of IT2FNN rule premises. A new rule is generated when the incentive intensities of rules are all smaller than a certain threshold; next, a significance index is set for each rule. When the significance index of some rule decays to a certain threshold, the corresponding rule shall be deleted to achieve the goals of optimizing IT2FNN structure and reducing system complexity. At the stage of parameter learning, adaptive adjustment of IT2FNN parameters is made via the sliding mode control theory learning algorithm, and the stabilities of the algorithm and control system are proven using Lyapunov function. Finally, the proposed control scheme is used in the control of a magnetic rigid spacecraft, as compared to three other designed control methods. Simulation results show that IT2FNNSMRC has superior control precision and stability. And the IT2FNN which adopts the proposed learning algorithm can address uncertainty satisfactorily, with higher computational implementability. • In the control scheme for the PD controller and IT2FNN parallel operation, a robust controller has been added so that the IT2FNNSMRC can be applied to second-order systems with more complex disturbances. • The self-organizing algorithm based on the incentive intensity of fuzzy rules has been proposed for online optimization of the IT2FNN structure, ensuring the correlation between the input signal and fuzzy rules and determining the optimal structure of the neural network. • The IT2FNN parameters have been optimized using the sliding mode learning algorithm at lower computational cost, which is easier to implement compared to the gradient method. • The computational cost of the IT2FNNSMRC has been analyzed in terms of time complexity, and it has been proven that the computational complexity of IT2FNN in this paper has been reduced below that of traditional fixed structure IT2FNN. • The IT2FNN parameter learning algorithm is tested by Lyapunov stability analysis, and it is further proven that the robust controller can effectively enhance the stability of the system. [ABSTRACT FROM AUTHOR]

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper. • This pre-Editorial provides an overview of some hot topics in AI for life. • The benefit for the reader is to get a short, crisp, concise intro into AI. • Authors interested in this Special Issue will get a scope of topics. • The take away message is that in the future no biotechnology can do without AI. [ABSTRACT FROM AUTHOR]

Data normalization is essential in many fields, such as speech recognition, deep learning, machine learning, and optimization. Many researchers focus on developing various normalization techniques, such as min-max, score-based, maximum, vector, or sum, on processing data efficiently. However, classical normalization methods may need to be improved in Multi-Criteria Decision-Analysis (MCDA), where criteria are often non-monotonic. This paper proposes a new approach for identifying and normalizing nonmonotonic criteria in MCDA. The method combines a stochastic optimization technique - a genetic algorithm - with normalization based on triangular fuzzy numbers. This paper compares the proposed approach with classical normalization techniques, such as min-max, maximum, vector, or non-linear, using the classical MCDA method - the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS). The study results showed that the proposed approach to normalizing non-monotonic criteria yields a better match between decision preferences and the actual reference model than traditional normalization methods. Optimization using the genetic algorithm helps identify the means of triangular fuzzy numbers, which generate more accurate results. Analysis of the R2 coefficient confirms a very good fit of the proposed model. Therefore, the new non-linear normalization approach in MCDA presented in this work has great potential in adjusting decision preferences to non-monotonic criteria. Future research should continue on the accuracy of this approach in identifying non-linear decision models and apply it to other MCDA methods and real-world decision problems. [ABSTRACT FROM AUTHOR]

In the paper, we describe the procedure of determining reference spectra for medical diagnosis. In our research, we have used various clustering methods to extract spectra of a group of healthy patients participating in the determination of the final reference spectra. The results presented in the paper can be used in further research concerning the development of diagnostic algorithms based on comparing the spectra of the subjects, suspected by a specific disease unit, with developed reference spectra of healthy people. [ABSTRACT FROM AUTHOR]

Constrained machine learning (ML) models consists of incorporating a set of constraints into the ML model. The latter is generally used to incorporate domain knowledge, enhance performance, and define fair and robust ML models. Several papers reported that defining a full set of constraints is challenging. To address this issue, approaches for learning constraints were proposed. These approaches established general rules governing the data and dismissed any sample-related information. However, in user profiling tasks it is important to understand the position of each individual. In this paper, we define behavioral constraints as the set of numerical values that reflect the characteristics of each individual in the dataset. We propose an unsupervised approach for learning these behavioral constraints (ULBC). Applied to two datasets, the approach effectively provided behavioral constraints that unravel the similarities and dissimilarities between individuals. [ABSTRACT FROM AUTHOR]

Many research studies confirmed that the application of machine learning methods for predicting students at risk of academic failure can be beneficial for improving student performance in higher education institutions. Simultaneously, these studies emphasised the importance of the input data quality, its precise pre-processing and thoroughly selected features on final performance metrics of the applied classification algorithms. However, the stand-alone application of well-known classification algorithms still has performance limitations, which can be outperformed using ensembles of classifiers. Therefore, this paper aims to evaluate the overall contribution of the selected ensemble methods to the early prediction of students at risk of dropping out of learning courses. All phases of CRISP-DM methodology are described in this study to explain the complexity of the problem of the practical adoption of voting-based heterogeneous ensemble methods in the case of data obtained from the course of the university learning management system compared to traditional classification algorithms enhanced by ensemble learning with AdaBoost and XGBoost methods. As a result, the study confirmed that the features which characterise the students' performance and their interactions in the course can be considered the most significant for the identification of the students at risk of dropping out. Moreover, in the case of advanced ensemble algorithms, the overall accuracy, recall, precision, and f1 score were improved by 2-4%. The proposed method has demonstrated exceptional performance. Finally, the paper summarises the findings and their implications for effective prediction. [ABSTRACT FROM AUTHOR]

Recent studies have uncovered security issues with most of the federated learning models. One common false assumption in the federated learning model is that participants are the attacker and would not use polluted data. This vulnerability enables attackers to train their models using polluted data and then send the polluted updates to the training server for aggregation, potentially poisoning the overall model. In such a setting, it is challenging for an edge server to thoroughly inspect the data used for model training and supervise any edge device. This study evaluates the vulnerabilities present in federated learning and explores various types of attacks that can occur. This paper presents a robust prevention scheme to address these vulnerabilities. The proposed prevention scheme enables federated learning servers to monitor participants actively in real time and identify infected individuals by introducing an encrypted verification scheme. The paper outlines the protocol design of this prevention scheme and presents experimental results that demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Social media platforms generate vast amounts of data that can be mined to gain insights into user behavior, preferences, and opinions. With the widespread adoption of mobile devices and the Android platform, social media data mining on Android has become an important area of research. In this paper, we present a systematic review of existing research related to social media data mining on Android. We conducted a comprehensive literature search and identified relevant research papers published between 2015 and 2022. We analyzed the papers to identify key themes and trends, and we discuss the strengths and weaknesses of current approaches. Our review revealed that most of the research in this area focuses on text data mining, and that machine learning techniques are commonly used for data analysis. We also found that there is a lack of research on image and video data mining, and that privacy and ethical issues are important considerations in this field. Based on our review, we propose future research directions for social media data mining on Android, including the development of more efficient and accurate machine learning algorithms, and the integration of multimedia data analysis. Our paper provides a valuable overview of existing research in this field and offers insights for researchers and practitioners interested in social media data mining on Android. [ABSTRACT FROM AUTHOR]

In order to perform fault classification using machine learning algorithms, sufficient and balanced data is required. The paper presents a novel approach for data augmentation using a Generative Adversarial Network (GAN) with transient, time-dependent vibration data. The proposed approach aims to generate synthetic data in form of spectrograms that closely resemble the characteristics of the real data sets. Synthetic data generation can be used to improve the training performance of neural networks for vibration analysis if not enough or unbalanced real data is available. The authors demonstrate the effectiveness of this approach by training a vibration-based fault detection model using synthetic data and comparing its performance to a model trained on real data only as well as with a t-distributed stochastic neighbour embedding (t-SNE). Real data is acquired on a bearing test rig and measurements are carried out on bearings with four different system states. The results show that the model trained with the synthetic data set outperforms the model trained with real data only, indicating that the synthetic data generated by the proposed approach can improve the training performance and accuracy of the machine learning model. Overall, the paper highlights the potential of GAN-based data augmentation approach via spectrograms for vibration analysis and offers insights into its practical application to bearings. [ABSTRACT FROM AUTHOR]

Small data and big data are distinct approaches to data analysis and utilization in various applications. While big data has been the focus of many research and business efforts for more than ten years, small data is increasingly being recognized as having potential value in certain settings. We systematically review literature and conclude that small data can be indeed valuable in certain scenarios. This paper incorporates the data value perspective of small data within various application areas. For this, we apply the data-information-knowledge-wisdom (DIKW) hierarchy to categorize papers and findings, and discuss the papers from the view point of "data value". Our review identifies various contexts where small data can be used to create value, such as data pre-processing, classification tasks, anomaly detection, forecasting and decision support. We also highlight industries that may be particularly promising areas for practitioners and researchers focused on small data. In addition, we provide an overview of methods and tools for small data analysis, including statistical techniques, visualization, and machine learning algorithms. Finally, based on our results, we suggest, that further research should focus on small data analysis. [ABSTRACT FROM AUTHOR]

We review the existing literature on generating text from visual data under the cross-modal generation umbrella, which affords us to compare and contrast various approaches taking visual data as input and producing text outputs, while not limiting the analysis to narrowly-defined areas, such as image captioning. We provide a breakdown of image-to-text generation methods into generative and non-generative image captioning and visual dialogue, with further distinctions provided for relevant areas. We provide template methods and discuss the existing research in light of such template methods, highlighting both the salient commonalities between different approach and significant departures from the templates. Where it is of interest, we also provide comparisons between templates for distinct areas. To achieve a comprehensive review, we focus on research papers published at 8 leading machine learning conferences in the years 2016–2021 as well as a number of papers, which do not conform to our search criteria, but nonetheless come from leading venues. This is the first review we know of to provide a systematic description of the current state of image-to-text generation and tie distinct research areas together looking at them through the lens of cross-modal generation. • Comprehensive review of image-to-text cross-modal generation. • Succinct summarization of key research advances. • Presentation of relevant results on sets of metrics within the cross-modal area. • Description of common and distinct elements for easier comparison of methods. • Identification of potentially fruitful research lines in multimodal generation. [ABSTRACT FROM AUTHOR]

The critical importance of user privacy in the context of Machine Learning is a hot research topic because it hinders data collection. Consequently, Federated Learning (FL) has emerged as a solution to the privacy problem. Instead of collecting users' data to train smart models, FL exchanges models with clients, which are trained and sent back to the server for aggregation and global model updating. However, FL still faces some hurdles, such as vulnerability to inference and poisoning attacks. For this reason, this paper proposes PolyFLAG_SVM: Polymorphic Federated Learning Aggregation of Gradients Support Vector Machines Framework. PolyFLAG_SVM is a novel, secure, communication-efficient framework that provides several variants of Support Vector Machines models that follow the Gradient Descent Update technique. The confidence in the security of the proposed model is the result of the polymorphism of the encryption keys used, which guarantees that a cracked or leaked key is useless, since it is not used twice within the FL cycle. Moreover, the proposed framework is communication efficient due to the small size of messages exchanged between servers and clients. The proposed model is explained in detail and evaluated appropriately in this paper. [ABSTRACT FROM AUTHOR]

Spam is some unwanted material that may be put in the form of images. While many machine learning approaches are effective at detecting textual spam, this is not true for image spam. In this paper, a new framework for identifying image spams is proposed. The images are divided into two categories: undesirable material contained in the form of images which is referred to as a spam image, whereas anything else is referred to as a ham image. Our proposed methodology is based on applying different pre-trained deep learning models, including InceptionV3, Densely Connected Convolutional Networks 121(DenseNet121), Residual Networks (ResNet50), Visual Geometry Group (VGG16) and MobileNetV2, to filter out the unwanted spam images. Different standard test datasets such as Dredze Dataset, Image Spam Hunter (ISH) Dataset and Improved Dataset are utilized in this paper for performance testing. Furthermore, transfer learning and data augmentation are employed to address the issue of a shortage of labeled data. In our implementation, the fully connected (FC) layer in the aforementioned pre-trained models is replaced with a Support Vector Machine (SVM) classifier, resulting in an improved accuracy. The obtained results reveal that ResNet50 model yields the best performance achieving 99.87% accuracy, 99.88% area under the curve (AUC), 99.98% sensitivity, 99.79% precision, 98.99% F1 score and a computational testing time of in the order of one to two seconds for the ISH dataset. [ABSTRACT FROM AUTHOR]

The efficient and safe movement of goods and people require reliable railway systems. Quality assurance of manufactured and assembled systems and correct maintenance of such systems are required to keep rolling stock in good operational condition. Quality assurance and maintenance in the railway industry can be costly and time-consuming, but the expansive growth of data due to smart sensors and monitoring technologies makes it possible to leverage the potential of machine learning to reduce cost and labour. Improved reliability and safety, and reduced costs are benefits that the use of "Big Data" and machine learning techniques can realise. However, despite these potential benefits for manufacturers, rail operators, and passengers, the rail industry is still labelled for its lack of innovation, while in most other industries, data is regarded as a strategic asset for competitive advantage. This paper demonstrates how machine learning and data analysis can be used to benefit railway industry manufacturers and operators when applied to rolling stock data. It also illustrates the lost opportunity in the rail industry for not applying data-driven solutions to their full potential. The paper also discusses the current applications of machine learning in the railway industry and provides the requirements for the implementation of machine learning techniques. Machine learning is applied to pantograph data of a South African railway operator's rolling stock. Classification – a machine learning technique – is used to identify and categorise events within the dataset to discover whether pantograph bounce occurs due to faulty sensors, faulty pantographs, or defective infrastructure. In this paper it is demonstrated how machine learning can benefit rail manufacturers and operators to improve manufacturing and assembly processes, as well as maintenance practices. It is concluded that railways should treat data similarly to other railway assets, with suitable management and governance practices. [ABSTRACT FROM AUTHOR]

This paper studies the challenges encountered during the implementation of machine learning (ML) applications in production planning and control (PPC) functions of manufacturing companies. The application of ML in PPC promises numerous benefits, and several studies have revealed the positive impact of such an implementation on key performance indicators of production systems, such as lead time, due date reliability, and inventory levels. However, despite the theoretical potential and the increased research interest in the field over the last five years, practical applications remain rare and discourse on the existence of this gap between research and practice remains relatively sparse. In this paper, we identify general ML implementation barriers in the manufacturing domain based on a structured literature review and evaluate the relevance of these barriers within the PPC domain by interviewing ML and PPC experts. This research is beneficial for practitioners and researchers who aim to implement ML applications in PPC by enabling them to evaluate the possible barriers and challenges during implementation projects within their organizations. [ABSTRACT FROM AUTHOR]

Kanban systems are still the most popular method for pull control of material replenishment. In the Industry 4.0 era, the so-called eKanban represents a digital version of the classic Kanban. Thereby, in some eKanban variants, capturing actual stock-level by digital sensors would help or helps already to reduce capturing time and human failures. Currently, such digital sensors are not really beneficial because they are wired and expensive to install. In this paper, we investigate the potential of battery-powered stock-level sensors for eKanban systems using a simulation study. Battery- powered stock-level sensors are easier to install and, therefore, more beneficial than wired sensors if the battery´s lifetime is long enough. Because of the limited energy, it makes sense to trigger such sensor systems dynamically depending on the actual and predicted stock levels and on order points for minimum stock. For example, the sensor systems usually sleep and capture the stock-level only in a fixed interval for example every hour. Besides fixed intervals, it can be useful to trigger such sensors dynamically by reducing the activations in time with no or few material take-outs. To identify the best trigger strategy, this paper investigates how artificial intelligence (AI) methods, in this case, reinforcement learning, can be deployed. Thereby, the used material flow simulation maps the application scenario of a four-stage assembly line and compares the AI-based trigger with a manually operated demand notification. In this way, it can be shown that battery lifetime is increased fourteen times more compared to the manually operated demand notification, and battery runtimes of several months can be achieved by AI-based optimization of the sensor energy consumption. This approach leads to an eKanban solution becoming more economical, and thus smaller companies can also consider digitizing their material flow control [ABSTRACT FROM AUTHOR]

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning. Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning. In this paper, we propose two novel multi-view deep models to solve the multiclass classification problem, namely deep multi-view twin support vector machines (DMvTSVM) based on deep neural network (DNN) and auto-encoder (AE) network. They find two non-parallel hyperplanes such that each hyperplane is as close to its own class as possible while being as far away from the other class as possible. Meanwhile, we apply similarity regularization to the output of the Deep TSVM classifier for each view to learn consensus information between views, and use this to refine the joint weights of the deep model and TSVM. Finally, the novel models employ the o n e − v s − r e s t strategy to allow the DMvTSVM classifier to solve the multiclass classification problems. In the experiments, the proposed methods are compared with existing state-of-the-art algorithms to prove their effectiveness. • The first combination of twin support vector machines (TSVM) and deep neural networks. • Nesting of more advanced auto encoder networks. • Incorporating multi-view learning capabilities. • Mutual negotiation and simultaneous learning between deep neural networks and TSVM. • The experiments prove the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

The immense growth of Android mobile malware threats has pushed cybersecurity researchers to develop efficient systems that can detect new Android malware. In spite of the academic and industrial attempts to establish a robust, reliable, and efficient solution for Android, malware classification is considered an open problem with many challenges. This paper sheds light on the performance of several machine learning algorithms and analyzes their efficiency in detecting android malware. Moreover, it applies Synthetic Minority Oversampling Technique (SMOTE), normalizes the numerical features and PCA to reach the maximum accuracy. Furthermore, the paper develops a Light Gradient Boosting Model to identify Android malware and classify their families into five classes: Adware, Banking Malware, SMS Malware, Mobile Riskware, and Benign. The paper uses a large and recent dataset, which consists of 11,598 APK collected from several sources and provided by the Canadian Institute of Cybersecurity. [ABSTRACT FROM AUTHOR]

This paper aims to investigate whether increasing non-linear opportunities in a neural network-based scheduling model improves its predictive performance. More specifically, this paper experiments on a trip distribution model that is part of an activity-based scheduling model called Skyline-seqNN from the ongoing thesis A neural network scheduling model. The motivation behind that model's proposed structure is to lay the groundwork for a neural network discrete choice model (DCM) that achieves to model travel demand on a detailed level while also being suitable for experimental analysis. Similar to a four-step model framework in the sequential aspect, the model system from the referenced paper utilizes the three sub-models; trip generation, trip distribution, and mode choice using a utility-maximizing micro-simulation approach. The trip generation model first decides whether, at every 10-minute interval between 05:00 am and 11:00 pm, an individual in the next time step should stay and continue the current activity or take an activity-defined trip. The distribution and mode choice models are used whenever a trip is selected. The trip distribution model decides the trip's destination by evaluating travel times and land use descriptions of each zone. The mode choice model learns the probability distribution of modes given each mode's travel time to the selected destination zone. Tests performed in this paper show how successive non-linear opportunities between input features in the trip distribution model increase its predictive performance. The data used for training and evaluation comes from a travel questionnaire from 2015 performed in Stockholm containing 10819 individuals and days. [ABSTRACT FROM AUTHOR]

The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research. [ABSTRACT FROM AUTHOR]

This paper demonstrates a holistic approach for conducting multi-platform and multilingual sentiment analysis of a main stream football club in the United Arab Emirates. The paper reports first iterations of the research project which anticipates to build an automated sentiment analysis system for football clubs to detect and prevent a potential social media crisis at an early stage. The article illustrates a schematic of how state of the art machine learning algorithms, methods and techniques can be put together to provide a basis for automated data collection, classification, sentiment analysis and visual reporting. The article also presents that the time-consuming and costly process of human text labelling can be effectively replaced by lexicon-based sentiment analysis. This paper reports the complete process from data collection to visual reports to discuss challenges and opportunities associated with design and development of such artefacts. [ABSTRACT FROM AUTHOR]

Product recommendation systems are an important aspect of retailing because of the improved shopping experience provided for customers. Due to the wide range of products offered by retailers, recommendation systems provide an optimal approach for displaying only relevant products to customers by forming associations that exist between products. Still, it is also important to understand the characteristics of customers connected to different product associations. Conventional approaches for product recommendation systems apply association algorithms and unsupervised classification of customers based on product ratings. However, it is not clear what demographic properties of customers are linked to which different product associations. This paper applies a hybrid system of machine learning (ML) association and clustering algorithms to implement a product recommendation system that shows associations that exist in products and unique customer profiles linked to these associations. The method described in this paper is evaluated with a case of a hygiene product retailer in Austria. [ABSTRACT FROM AUTHOR]

This paper presents some initial results on the use of hyperspectral imaging technology and machine learning to characterise the surface composition of space objects and reconstruct their attitude motion. The paper provides a preliminary demonstration that hyperspectral and multispectral analysis of the light absorbed, emitted and reflected by space objects can be used to identify, with some degree of accuracy, the materials composing their surface. The paper introduces a high-fidelity simulation model, developed to test this concept, and a validation of the model against experimental tests in a laboratory environment. The paper shows how to unmix the spectra to provide an estimation of the materials composing the surface facing the sensor. A machine learning approach is then proposed to reconstruct the attitude motion from the time series of spectra. • Demonstration of characterisation of space object composition using hyperspectral technology. • Demonstration of attitude motion reconstruction from spectra with machine learning. • High-fidelity model of the spectra received by a sensor. • Validation of the simulation model in a laboratory environment. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) technology of mobile networks will establish new standards to fulfill unreachable performance requirements by fifth-generation (5G) mobile networks. This is due to the high requirements for more intelligent network, ultra-lower latency, extreme network communication speed, and supporting massive number of various connected applications. In the long term, the convergence of various business developments with communication platforms, as initiated by 5G, will exaggerate and highlight areas where 5G's capabilities will fall short of performance requirements. Motivated by the development of applications in massive connections, future networks, developments, and technological advancements for mobile communications that go beyond fifth-generation (B5G) networks are being developed. In this context, highly immersive applications are demanded, such as three-dimensional (3D) communications, digital twins, or massive extended reality (XR)/virtual reality (VR) applications, which will need 6G capabilities to be realized at scale to be commercially feasible. Mainly, we anticipate that only the upcoming 6G networks will be capable of running extremely high-performance connectivity with massive numbers of connected devices, even under laborious scenarios such as extreme density, diverse mobility, and energetic environments. In this article, we look at the most recent trends and future emerging trends that are possible to operate 6G network. Paper aims to provide more inclusive and brief review about 6G mobile communication technology in one survey paper. Initially, a comprehensive overview of the 6G system is introduced in terms of visions, drivers, requirements, architecture, and usage scenarios required to enable 6G applications. After that, the opportunities and advantages of 6G mobile technology has been discussed. Further, the promising new techniques that enable 6G technology has been highlighted. This is followed by a potential discussion of challenges and research directions. This article is envisioned to serve as an informative guideline to stimulate interest and further studies for subsequent research and development of 6G networks. Paper will enable the readers to briefly figure out the key requirements, targets, that will be need and the applications, advantages, and opportunities that can be offered as well as the challenges that need to be addressed before the implementation of this new technology. [ABSTRACT FROM AUTHOR]

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies. [ABSTRACT FROM AUTHOR]

Compressed Machine (ML) and Deep Learning(DL) techniques are the emerging areas in research, the use of compressed ML and DL algorithms is recommended on System on Chip(SoC) platforms as the latencies of ML or DL algorithms will be extremely high. This paper explores the need to use compressed ML/DL techniques by analyzing the various popular ML algorithms by implementing them on different platforms including SoCs, in particular Snapdragon 410c which will be used on the wearable biomedical device. This paper looks at fall prediction using wearables. The wearable device uses 410c as the platform and data is collected using IMU and heart rate sensors. A data set was created by collecting IMU and heart rate sensor values across different fall and non fall Activities of Daily Living(ADL) activities, over 70k data points was collected of which 70 percent was used to train the algorithm and 30 percent was used as test data. The same data set was used across varying algorithms and platforms and the performance as well as the latencies were analyzed.It was found that the latencies was found to be greater than 120 sec for even K Nearest Neighbours(KNN) when run on SoCs. This clearly indicates that we cannot use classical ML algorithms. When a public dataset SmartFall which had 92,780 data points was used, Support Vector Machine(SVM) algorithm did not converge even after several hours of computing. In case of Random Forest it took 2235 seconds for the algorithm to converge. As the size of the dataset increased obviously there was an increase in latency with complex ML algorithms are unable to converge, hence this research indicates the necessity of compressed ML algorithms. [ABSTRACT FROM AUTHOR]

The competitive airline sector has grown at a breakneck pace in the last two decades. A useful source for collecting consumer feedback and performing various forms of analysis on it is proper data collection. This collection of data can be used for sentiment analysis. Sentiment analysis is a type of analysis that involves extracting sentiment to find attitudes and emotions associated with the text or data supplied. It's a classification approach in which machine learning techniques are used to identify positive and negative words or reviews in text-driven databases. Further to explain the reasons for negative comments, a word cloud and a bar graph are used. Sentiment analysis is used to analyze the Airline reviews dataset in this paper. To test the performance of sentiment analysis, many Machine Learning (ML) algorithms have been utilized, such as Naive Bayes, Support Vector Machine, and Decision Tree (DT), and each of these approaches has produced distinct results. The performance of Google's BERT algorithm has been evaluated to that of other machine learning algorithms in our research. Furthermore, this paper explores the Bert architecture, which has been pre-trained on two NLP tasks: Masked language modeling and Sentence prediction. The "Random Forest" is used as a baseline against which the results of the "BERT Model" are compared because its performance is the best among the machine learning models. In terms of performance criteria such as accuracy, precision, recall, and F1-score, it is discovered that BERT outperformed the other ML techniques. [ABSTRACT FROM AUTHOR]

In recent years, predictive analysis has attracted a lot of interest due to advances in technology, particularly in the large data and machine learning fields. The advancement in technology has also scored the cases of cybercrimes. The majority of the time, a mathematical model that captures important trends is built using data that is historical. The predictive model is used to analyze data to forecast certain parameters or trends or to advise actions to achieve the best results. Artificial neural network (ANN) is an effective technique for such predictions. In this paper, ANN is used for prediction from a survey-based dataset that has been created from the students of universities of J&K Union Territory. The dataset that is used has been created with a dire need to understand the level of students in terms of their knowledge and understanding of cybercrimes. A very imperative way to prevent cyber against people is to make people aware of crimes and by training them for the same. For such a problem, a model is proposed to predict the need for cyber training among students using ANN. In this paper, ANN with back-propagation is proposed as a tool to predict whether there is a need for cyber training for individuals. Numerous cases have been reported in the cyber cell of J&K where cybercrimes have been registered by individuals. It is the need of the hour to train the student community on cybercrime schemes and behavior. [ABSTRACT FROM AUTHOR]

The method of recognizing faults in a software system is acknowledged as software fault prediction. Software faults predicted in prior stages help in the management of resources and time required during software testing and maintenance. The identified software module can be fixed ahead of time, saving time and money near the end of the software development process. Over the years, various supervised machine learning-based techniques for fault prediction have been suggested. These models' accuracy is based on the training datasets. The models are created and trained using a labeled dataset consisting of multiple independent variables like lines of codes, the complexity of the software, the size of the software, etc., and a dependent binary variable, either true or false. But the fault dataset may have some concerns like a class overlapping problem, class imbalance problem, null values, etc. Recent research in software fault prediction focuses on data quality. An imbalanced dataset is one in which one of the class data is present in the majority and another class data is present in the minority. Models built using imbalanced datasets are biased which results in inaccurate predictions. Therefore, balancing the dataset is important. In this paper, the most recent software fault prediction algorithms, which focus on class imbalance issues are discussed. A comparative presentation is presented in this paper, which would benefit the scholar in selecting the best techniques of fault prediction based on different datasets and algorithms. According to the survey, SMOTE is the most commonly used data sampling technique for dealing with data quality issues. [ABSTRACT FROM AUTHOR]

The paper presents a miniature crane prototype including a deep learning model especially designed for autistic children to assist them by teaching yoga. The objective of the paper is to propose a solution to help autistic kids overcome their difficulties faced in their local environment and adapt themselves to society for their well-being. Autistic kids find it hard to involve in social communications and feel insecure to talk with unknown people. Also, some of the kids have repetitive behaviors. To propose a solution, we introduce a miniature crane prototype to assist children in teaching yoga by which autistic kids can overcome their social difficulties. Play can create a positive impact on children during the learning phase and get rid of their difficulties. The play will take the input of the user and react accordingly. The miniature is mobile to get closer and playable with children by Responsive incorporation. The miniature can capture pictures depicting the behavior of the kid and respond to the kid through the LCD screen. The play enhances the activity of the kid and motivates them to learn yoga. The play is included with a yoga detection model which helps the play assist kids in learning yoga. The deep learning algorithm AlexNet is used which can detect and instruct while learning yoga. The model achieves higher accuracy of 98.61% which helps in assisting yoga postures the children with the proper instructions. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) is a high-resolution radar extensively used for aerial and terrestrial images in numerous applications. The SAR images are all weather and day/night capable. It is extensively used in defense/military systems for surveillance, target detection and target tracking. In current times, the need for deep learning algorithms has increased due to its capability of generating SAR images, as it is very intensive and expensive task for the acquisition and formation of SAR images through sensors. Thus, alternative methods are required to produce SAR image data. In this paper, generative adversarial networks (GAN) are used to generate SAR images from a limited pre-existing dataset. This paper presents a novel way of translating optical images to SAR images, for the generation of paired, unpaired data set. Due to the unavailability of an open-source paired, unpaired image dataset, a synthetic paired image dataset is created for proving the concepts of CycleGAN and Pix-to-Pix algorithm by fine tuning the algorithms using generated datasets. The paper tried tackling this problem in two ways, both paired and unpaired image-to-image translation. [ABSTRACT FROM AUTHOR]

Groundwater is best portrayed as the world's truly covered up treasure. It has had an effect in giving safe drinking water during dry season. It is the world's biggest open store of fresh water (barring ice sheets and icy masses) and comprises around 94% of all fresh water. Rajasthan witnesses water scarcity problem therefor, it aims at forecasting the rainfall in the state, based on different features provided by the state government. The motivation of the paper is to predict the groundwater level so that proper conservative methods can be applied to raise the level of ground water. In this paper, a comparative analysis of different individual models is used on the derived dataset and two new hybrid models are added to increase the efficiency and accuracy of the prediction. [ABSTRACT FROM AUTHOR]

Accuracy of a classifier is important for the success of any prediction model. The more accuracy a classifier possesses, the more robust the system is made on it. In this paper, a disease prediction model is developed in Python for the classification of diabetes in patients. In the research paper, study is performed to make a comparative analysis of the performance of machine learning classification algorithms. The classifier's performances are enhanced by of tuning the hyperparameters of classifiers and applied different dataset preprocessing methods. In this experimental analysis, four models have been created, and each model is based on a dataset, obtained by different preprocessing methods of PIMA dataset. For each model, K-Nearest Neighbors, Decision Tree, Random Forest, and Support vector machines classification algorithms, have been applied and classifier's hyperparameters are tuned to get better results from these models. A detail analysis has also performed to get the best prediction model, the best classifier and effective preprocessing methods for it. The prediction model use F1score as the main metric. The highest F1score and accuracy are 75.68 % and 88.61% respectively, which is achieved by Random Forest classifier for dataset model D3 obtained by removing the samples having missing or unknown values from PIMA dataset. [ABSTRACT FROM AUTHOR]

The stock market is highly volatile as it depends on political, financial, environmental, and various internal and external factors along with historical stock data. Such information is available to people through microblogs and news and predicting stock price merely on historical data is hard. The high volatility emphasizes the importance to check the effect of external factors on the stock market. In this paper, a machine learning model is proposed where the financial news is used along with historical stock price data to predict upcoming stock prices. The paper has used three algorithms to calculate various sentiment scores and used them in different combinations to understand the impact of financial news on stock price as well the impact of each sentiment scoring algorithm. Experiments have been conducted on ten-year historical stock price data as well as financial news of four different companies from different sectors to predict the next day and next week's stock trends and accuracy metrics were checked for a period of 10, 30, and 100 days. The proposed model can achieve the highest accuracy of 0.90 for both trend and future trends for a period of 10 days. Experiments have also been performed to check the difficulty in predicting some stocks. It was found that Tata Motors an automobile company stock prediction has maximum MAPE and hence deviates more from actual prediction as compared to others. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder affecting 60% of people over the age of 50 years. Patients with Parkinson's (PWP) face mobility challenges and speech difficulties, making physical visits for treatment and monitoring a hurdle. PD can be treated through early detection, thus enabling patients to lead a normal life. The rise of an aging population over the world emphasizes the need to detect PD early, remotely and accurately. This paper highlights the use of machine learning techniques in telemedicine to detect PD in its early stages. Research has been carried out on the MDVP audio data of 30 PWP and healthy people during training of 4 ML models. Comparison of results of classification by Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN) and Logistic Regression models, yield Random Forest classifier as the ideal Machine Learning (ML) technique for detection of PD. Random Forest classifier model has a detection accuracy of 91.83% and sensitivity of 0.95. Through the findings of this paper, we aim to promote the use of ML in telemedicine, thereby providing a new lease of life to patients suffering from Parkinson's disease. [ABSTRACT FROM AUTHOR]

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic. [ABSTRACT FROM AUTHOR]

Predicting faults in software aims to detect defects before the testing phase, allowing for better resource allocation and high-quality software development, which is a requisite for any organization. Machine learning techniques aid in the resolution of such issues and a variety of predictive models are being developed to categorize the software into defective modules and the one which is non-defective ones. Though applying these advanced machine learning techniques results in better utilization of time and other resources, there is still poor prediction as reported in many studies. This is because of several challenges that block defective software data, including redundancy, correlation, feature irrelevance, missing samples, and an imbalanced distribution between the faulty and non-faulty classes. Ensemble Machine learning has been adopted by practitioners and researchers globally to deal with such problems, and it is proven to demonstrate some improvement in defect prediction performance. In this review paper, all ensemble-based machine learning techniques developed for software defect prediction from 2018 to 2021 have been critically analyzed. The nucleus of this paper is to get a deep insight into why the various hybrid models still suffer from poor performance on the available datasets. A detailed review with a focus on multiple perspectives viz. faulty and non-defective datasets, performance evaluation criteria, and machine learning techniques have revealed certain gaps that can be addressed by developing more robust hyperparameter optimization algorithms, feature engineering, developing stacking and averaging models. [ABSTRACT FROM AUTHOR]

The growing complexity of the logistics chain, the need to take on an ever longer and distributed logistic chain, which in some cases even reaches the final stages of the production of the same goods, highlights the problem of managing the post-production phases of products and goods, but also of the personnel who work on these activities, charged to the companies that deal with logistics. We present in this paper the Integrated Logistic Platform (ILP 4.0), a software architectural model, whose purpose is to lead warehouse logistics to new levels of efficiency, not reachable as the sum of the operational functions within a single logistic company, but reachable only through a unique and cross-functional system, which surpasses the usual fragmented vision and consequent approaches, in favor a strategic coordination of all activities. In this paper it is described the general architecture of the realized platform, with its research contributions and innovations, in particular related to some typical problems of warehouse environments, solved with the help of machine learning and computer vision approaches, but also with the integration of augmented reality (AR) and virtual reality (VR) devices. The aim of the integration of these technologies in the project is the definition of a "smart" warehouse model environment, capable of mitigate problems of warehouse logistics such as: automation of the inventory process (through an UAV), control of warehouse movements, and management of logical / physical security of the premises. Based on the experiences made on the project, and focusing the attention on the safety and security aspects in logistics, that the platform covers, we discuss further a possible federated learning approach in order to mitigate the lack of data in order to solve some security and safety problems in the logistic area. [ABSTRACT FROM AUTHOR]

The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network. [ABSTRACT FROM AUTHOR]

In this paper, we considered a new probability distribution with new applications in the field of engineering, in particular, in music engineering. The new probability distribution is mainly based on the Weibull distribution and cosine function. We call this the weighted cosine flexible Weibull distribution. The point estimators for the new distribution are obtained. We evaluate these point estimators for three sets of parameter values. The illustration of the weighted cosine flexible Weibull distribution is provided for two practical data set which are drawn from the reliability and music engineering. In addition, to dual robust machine learning approaches, we implement the Lasso (Least Absolute Shrinkage and Selection Operator) and Elastic Net (ENet) to improve the predictive performance of the data. This was done in response to the existence of outliers in the same datasets. We provide comparative analysis to see how well these approaches performed in comparison to the Step Indicator Saturation (SIS) method. [ABSTRACT FROM AUTHOR]

Smart and connected health (SCH) accelerates the development and integration of information science and engineering approaches to support the digital transformation of health and medicine in populated societies. Sensor data based human activity recognition (HAR) as an effective means of SCH is promising for healthcare monitoring and ambient assisted living. This paper focuses on multi-position sensor data fusion enabled HAR, and proposes a multi-channel deep learning framework. The main contributions include: (1) A multi-channel hybrid deep learning model (1DCNN-Att-BiLSTM) that merges a one-dimensional convolutional neural network, a bidirectional long short-term memory model, and an attention mechanism is proposed to significantly improve the recognition performance of HAR models by extracting and selecting local and global behavioral features in the spatial and temporal domains; (2) Publicly accessible datasets Shoaib AR, Shoaib SA, and HAPT are collected and processed to build multi-position sensor data pool for model's evaluation; (3) Thoroughly evaluating the classification performance of seven sensor data fusion patterns from different body positions within the parallel network structure of the multi-channel framework; (4) Comparing the recognition performance metrics of traditional machine learning models, deep learning models, and hybrid models with our proposed model. Extensive experiments demonstrate that our approach achieves competitive performance. [ABSTRACT FROM AUTHOR]

This paper examines how business users can leverage machine learning and data analytics through dashboards to optimize their decision making in demand-side supply chain management. We present a case study of an Austrian B2B hygiene product retailer that needed to provide its top management, sales representatives, and marketing managers with more relevant information to improve business intelligence and to enhance customer acquisition and retention. To generate this information, we utilized various data analysis and machine learning methods, including RFM analysis, market basket analysis, TURF analysis, and demand forecasting, using real-life transaction data. To provide business users with easy access to this information, we developed dashboards that integrate these methods providing an interactive and visual tool for data exploration and understanding. We conclude that dashboards can enable, business users to make better informed and effective decisions on the demand side of supply chains leading to improved sales performance and increased customer satisfaction. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly. [ABSTRACT FROM AUTHOR]