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The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. Unlike traditional models, STID-Net has an improved ability to identify irregular patterns in dynamic datasets. This work is also equipped with an attention mechanism for enhancing the detection of long-term dependencies in intrusion patterns. The STID-Net is also experimented with the MBGD and SGD optimizers, and we are satisfied with the performance of the SGD optimizer in both the IoMT and IIoT datasets. The SGD optimized model provides a faster convergence and better weight adjustments for handling noisy datasets, making it robust and scalable for diverse IoT applications. This experimental work demonstrates an accuracy of 97.14% and 97.85% with the MBGD optimizer in the IoMT and IIoT datasets, while it attained 98.58% and 99.15% with SGD optimization, respectively. The proposed methodology also outperforms the standalone CNN and LSTM models incorporated with both optimizers, and the result indicates the robustness and scalability of STID-Net in medical and industrial applications.

Dementia, a progressively debilitating neurodegenerative disorder, poses a growing challenge to global healthcare due to its subtle early onset and lack of accurate temporal diagnostic models. Traditional machine learning (ML) approaches for dementia prediction, including SVMs, Random Forests, and CNNs, often rely on static, cross-sectional data that fail to capture the progressive cognitive decline observed in real-world patient trajectories. To address these limitations, this study introduces a novel deep learning-based framework TCBiNet (Temporal Convolutional Bidirectional Attention Network) designed to model both short- and long-term symptom evolution using sequential clinical data. The architecture strategically integrates Temporal Convolutional Networks (TCN) to identify localized trends, BiLSTM for bidirectional temporal analysis, and a Temporal Attention mechanism to highlight critical symptom intervals. Implemented in Python using TensorFlow 2.11, the model processes longitudinal data from 2,149 patients aged 60–90, with cognitive, behavioral, and functional metrics segmented into 30-day intervals. Compared to conventional models such as CNN-LSTM and BiLSTM-DRL, TCBiNet demonstrated superior performance, achieving an accuracy of 99.51%, F1-score of 99.35, and AUC-ROC of 0.990 reflecting an improvement of over 4.4% in accuracy and 6.2% in F1-score relative to the best-performing existing models. This significant gain is attributed to the model’s capacity for temporal pattern mining and attention-guided symptom weighting. The proposed system not only improves prediction accuracy but also enhances interpretability and clinical relevance. By offering a robust, sequence-aware diagnostic tool, this research paves the way for proactive interventions in dementia care, encouraging further exploration into longitudinal neurocognitive modeling for early disease detection.

Early detection of human activity is essential in domains including robotics, entertainment, surveillance, and healthcare. Early detection that is accurate enables prompt decision-making, enhancing system responsiveness and overall effectiveness. Conventional action recognition techniques can’t handle sequential and incomplete data well since they are usually built for offline analysis and concentrate on detecting entire actions. Early detection necessitates real-time result prediction and incomplete activity identification, which are difficult for many current models to do. In order to enhance early detection and prediction of human behaviors, this study proposes a unique method utilizing a Bi-Directional Convolutional Long Short-Term Memory (Bi-ConvLSTM) network. By incorporating both spatial and temporal connections, the model processes sequential data and makes it possible to identify activity initiation and continuing activities with greater accuracy. By examining the temporal sequence of input frames, the Bi-ConvLSTM network is intended to identify the beginning of an activity and forecast its course. The proposed approach utilizes a segment-based strategy in which the input sequence is broken down into smaller intervals, allowing the model to focus on specific temporal segments. This improves the network’s capacity to recognize tiny motion patterns and contextual signals indicating the start of an activity. The model is tested on a real-world dataset that includes a variety of human behaviors recorded in complicated contexts. Experimental findings show that the proposed Bi-ConvLSTM model outperforms current models such as CNN, InceptionV3, VGG19, and regular ConvLSTM networks, with an average accuracy of 89.54%. The findings show that the Bi-ConvLSTM model efficiently balances early detection accuracy with decision-making speed, making it appropriate for real-time applications. This study demonstrates the ability of Bi-ConvLSTM networks to improve early detection and prediction of human behaviors, opening the door for more responsive and intelligent systems.

A recommender system supports customers to find information, products, or services (such as music, books, movies, web sites, and digital contents), so it could help customers to make rapid routine decisions and save their time and money. However, most existing recommender systems do not recommend items that are already purchased by the target customer, so are not suitable for considering customers’ repetitive purchase behavior or purchasing order. In this research, we suggest a multi-period product recommender system, which can learn customers’ purchasing order and customers’ repetitive purchase pattern. For such a purpose we applied the Recurrent Neural Network (RNN), which is one of the artificial neural network structures specialized in time series data analysis, instead of collaborative filtering techniques. Recommendation periods are segmented as various time-steps, and the proposed RNN-based recommender system can recommend items by multiple periods in a time sequence. Several experiments with real online food market data show that the proposed system shows higher performance in accuracy and diversity in a multi-period perspective than the collaborative filtering-based system. From the experimental results, we conclude that the proposed system is suitable for multi-period product recommendation, which results in robust performance considering well customers’ purchasing orders and customers’ repetitive purchase patterns. Moreover, in terms of sustainability, we expect that our study contributes to the reduction of food wastes by inducing planned consumption, and the reduction of shopping time and effort.

Sequential data modeling and analysis have become indispensable tools for analyzing sequential data, such as time-series data, because larger amounts of sensed event data have become available. These methods capture the sequential structure of data of interest, such as input-output relations and correlation among datasets. However, because most studies in this area are specialized or limited to their respective applications, rigorous requirement analysis of such models has not been undertaken from a general perspective. Therefore, we particularly examine the structure of sequential data, and extract the necessity of “state duration” and “state interval” of events for efficient and rich representation of sequential data. Specifically addressing the hidden semi-Markov model (HSMM) that represents such state duration inside a model, we attempt to add representational capability of a state interval of events onto HSMM. To this end, we propose two extended models: an interval state hidden semi-Markov model (IS-HSMM) to express the length of a state interval with a special state node designated as “interval state node”; and an interval length probability hidden semi-Markov model (ILP-HSMM) which represents the length of the state interval with a new probabilistic parameter “interval length probability.” Exhaustive simulations have revealed superior performance of the proposed models in comparison with HSMM. These proposed models are the first reported extensions of HMM to support state interval representation as well as state duration representation.

The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank approximation. In practical data analysis, however, outliers and missing values maybe exist that restrict the performance of SVD, because SVD is a least squares method that is sensitive to errors in the data matrix. This paper proposes a robust SVD algorithm by applying an adjustable robust estimator. Through adjusting the tuning parameter in the algorithm, the method can be both robust and efficient. Moreover, a sequential robust SVD algorithm is proposed in order to decrease the computation volume in sequential and streaming data. The advantages of the proposed algorithms are proved with a financial application.

In military command and control, success relies on being able to perform key functions such as communicating intent. Most staff functions are carried out using standard means of text communication. Exactly how members of staff perform their duties, who they communicate with and how, and how they could perform better, is an area of active research. In command and control research, there is not yet a single model which explains all actions undertaken by members of staff well enough to prescribe a set of procedures for how to perform functions in command and control. In this context, we have studied whether automated classification approaches can be applied to textual communication to assist researchers who study command teams and analyze their actions. Specifically, we report the results from evaluating machine leaning with respect to two metrics of classification performance: (1) the precision of finding a known transition between two activities in a work process, and (2) the precision of classifying messages similarly to human researchers that search for critical episodes in a workflow. The results indicate that classification based on text only provides higher precision results with respect to both metrics when compared to other machine learning approaches, and that the precision of classifying messages using text-based classification in already classified datasets was approximately 50%. We present the implications that these results have for the design of support systems based on machine learning, and outline how to practically use text classification for analyzing team communications by demonstrating a specific prototype support tool for workflow analysis.

In military command and control, success relies on being able to perform key functions such as communicating intent. Most staff functions are carried out using standard means of text communication. Exactly how members of staff perform their duties, who they communicate with and how, and how they could perform better, is an area of active research. In command and control research, there is not yet a single model which explains all actions undertaken by members of staff well enough to prescribe a set of procedures for how to perform functions in command and control. In this context, we have studied whether automated classification approaches can be applied to textual communication to assist researchers who study command teams and analyze their actions. Specifically, we report the results from evaluating machine leaning with respect to two metrics of classification performance: (1) the precision of finding a known transition between two activities in a work process, and (2) the precision of classifying messages similarly to human researchers that search for critical episodes in a workflow. The results indicate that classification based on text only provides higher precision results with respect to both metrics when compared to other machine learning approaches, and that the precision of classifying messages using text-based classification in already classified datasets was approximately 50%. We present the implications that these results have for the design of support systems based on machine learning, and outline how to practically use text classification for analyzing team communications by demonstrating a specific prototype support tool for workflow analysis.

Our understanding of distributed decision making in professional teams and their performance comes in part from studies in which researchers gather and process information about the communications and actions of teams. In many cases, the data sets available for analysis are large, unwieldy and require methods for exploratory and dynamic management of data. In this paper, we report the results of interviewing eight researchers on their work process when conducting such analyses and their use of support tools in this process. Our aim with the study was to gain an understanding of their workflow when studying distributed decision making in teams, and specifically how automated pattern extraction tools could be of use in their work. Based on an analysis of the interviews, we elicited three issues of concern related to the use of support tools in analysis: focusing on a subset of data to study, drawing conclusions from data and understanding tool limitations . Together, these three issues point to two observations regarding tool use that are of specific relevance to the design of intelligent support tools based on pattern extraction: open-endedness and transparency .

We present results from a study on constructing and evaluating a support tool for the extraction of patterns in distributed decision -making processes, based on design criteria elicited from a study on the work process involved in studying such decision-making. Specifically, we devised and evaluated an analysis tool for C 2 researchers who study simulated decision-making scenarios for command teams. The analysis tool used text clustering as an underlying pattern extraction technique and was evaluated together with C 2 researchers in a workshop to establish whether the design criteria were valid and the approach taken with the analysis tool was sound. Design criteria elicited from an earlier study with researchers (open-endedness and transparency) were highly consistent with the results from the workshop. Specifically, evaluation results indicate that successful deployment of advanced analysis tools requires that tools can treat multiple data sources and offer rich opportunities for manipulation and interaction (open-endedness) and careful design of visual presentations and explanations of the techniques used (transparency). Finally, the results point to the high relevance and promise of using text clustering as a support for analysis of C 2 data.