In this study, a new deep learning-based model, namely, transfer learning-based granulated Bi-LSTM (TLG-LSTM) is developed for fall detection on roads. The TLG-LSTM can handle the uncertainty issue arising between various ‘Fall’ and ‘No Fall’ events in complex scenarios concerning both indoor (i.e., home) and outdoor (i.e., road) areas. The TLG-LSTM consists of four phases: (i) object detection and tracking, (ii) MoveNet-Lightening and object-level feature(s) computation for all the detected objects, (iii) granule formation using these features, and (iv) temporal self attention mechanism-based Bi-LSTM for granule classification as ‘Fall’ or ‘No Fall’. Unlike state-of-the-art models, TLG-LSTM uses both MoveNet-Lightning and object-level features, enabling better modeling of both indoor and outdoor falls. For each detected object, two MoveNet-Lightning features, namely head-hip distance and hip-ankle distance are defined and used for obtaining a granule, namely pose granule. Whereas, three object-level features, namely change in aspect ratio, speed variation, and change in area are used for obtaining another granule, namely object granule. The commonality between these two granules represents the approximate regions concerning fall scenarios. Instead of the entire frame, these common granules are fed to the Bi-LSTM network for fall classification, thereby increasing speed as well as accuracy. Moreover, temporal self attention mechanism-based transfer learning is used to re-train the Bi-LSTM network, enhances the training speed and accuracy. Characteristics of TLG-LSTM are demonstrated over several real-time traffic videos acquired from ‘YouTube8M’. The superiority of the developed TLG-LSTM is also claimed over several state-of-the-art models.

Convolutional neural networks (CNNs) are highly effective deep learning architectures for remote sensing (RS) image classification. However, the interpretability of CNN architecture remains challenging for further performance improvement. To address this issue, we propose an end-to-end interpretable CNN architecture called granulated interpretable CNN (GrI-CNN) within the granular computing (GrC) framework. The GrI-CNN uses fuzzy and rough sets to make each architecture component functionally interpretable. Fuzzy sets perform class-dependent (CD) granulation of the input feature space, while rough sets granulate the information with operations, such as reduct for dimension reduction, functional dependency (FD) of samples for the optimal selection of filters, and weighted membership of granules. The decision layer of GrI-CNN measures the roughness of overlapping granules, encodes the domain knowledge, and initializes the weights using weighted membership and roughness measures. We combined two classification networks at the decision layer to achieve the best possible performance: FD-based interpretable-extreme learning machine (I-ELM) and knowledge-encoded evolving granular neural network (e-GNN). E-GNN is a kind of GNN in which the shape and size of granules evolve based on the user's needs. Thus, GrI-CNN uses only the required weight parameters and reduces computational time. We have demonstrated the superiority of GrI-CNN over similar state-of-the-art models for classifying multispectral and hyperspectral RS images.

The eye usually located at the center of the tropical cyclone is connected to the rapid intensification of tropical cyclone and prediction of its track and intensity. The problem of its localization and detection from satellite imagery in deep learning framework is considered here, where the study conducted deals with 35 named tropical cyclones of different categories over the Bay of Bengal and the Arabian Sea of the North Indian Ocean (NIO). Mask region-based convolutional neural network (MRCNN), which is a variant of RCNN with an addition of a mask branch that produces a mask around the detected object, is considered as the backbone architecture. Since deep learning is time-consuming, we propose embedding the concept of granular computing into MRCNN to speed up its learning mechanism. The granulated mask region-based convolutional neural network (G-MRCNN), thus developed, provides better object(s) localization and increases eye detection accuracy, apart from speedy learning. Two novel indices for eye detection, viz, compactness and eye detection index (EDI), are defined incorporating the shape, area, and compactness (circular) of the predicted mask as well as the detection score. The larger the value of EDI, the more circular and compact the shape of the eye region, and the better the prediction. Different types of granulations ranging from regular to arbitrary shapes have been incorporated in G-MRCNN and the prediction accuracy of each model has been compared against a set of testing data as well as during the time of validation using the aforesaid two indices. EDI is seen to reflect well the eye detection performance, as also judged visually. In that sense it is unique. The results reveal that the performance of the G-MRCNN with k-means (k = 5) clustering-based granulation is better than other methods for the detection and localization of the eye of the tropical cyclone over NIO. The prediction skill of the model is then validated with 4 named tropical cyclones of extremely severe, very severe, and severe categories over NIO.

Transfer learning (TL) is a popular phrase in deep learning (DL) domain. It is one of the latest artificial intelligence (AI) technologies that has a significant impact on big data analysis. Methods of traditional machine learning (ML) require the availability of an adequate quantity of training data as well as similarity of characteristics among the feature spaces corresponding to training and test data while performing supervised learning tasks. However, in real-life analytical problems, data scarcity often arises. In such scenarios, the TL approach has shown effectiveness in transferring knowledge from the source tasks that had large training data to a target task that has less training data. Basically, in TL, a model that has been trained on one task is essentially applied to a second related (but not exact) task. In this way, the issue of distribution mismatch can also be addressed. TL is not like conventional machine learning algorithms that try to learn each task starting from the beginning. Meteorological research is such an example of big data analysis which often faces the data scarcity issue. The current study addresses the contemporary challenges in weather forecasting that can be solved (or better dealt with) using TL methods. It presents a brief review of earlier research with the evolution of various technologies used since 1990s, followed by potential applications of TL algorithms to several key challenges in weather prediction, which includes the prediction of air quality, thunderstorms, precipitation, visibility, and cyclones, among others. Special emphasis is given to high-impact weather (HIW) prediction. These high-impact events are extremely difficult to predict, and they can cause enormous property damage and fatalities around the world. TL techniques have shown advantages in predicting HIW. Various challenging issues in implementing TL technology are then discussed. Finally, we address various prospects associated with TL, propose new research directions, and more importantly mention some concerns for beginners in DL-TL research. An extensive list of references is also provided. Received: 12 March 2024 | Revised: 11 May 2024 | Accepted: 7 June 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Sankar Kumar Pal: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Debashree Dutta: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.

In image clustering applications, deep feature clustering has recently demonstrated impressive performance, which employs deep neural networks for feature learning that favors clustering exercises. In this context, density-based methods have emerged as the preferred choice for the clustering mechanism within the framework of deep feature clustering. However, as the performance of these clustering algorithms is primarily effective on the low-dimensional feature data, deep feature learning models play a crucial role here. With far infrared (FIR) thermal imaging systems working in real-world scenarios, the images captured are largely affected by blurred edges, background noise, thermal irregularities, few details, etc. In this work, we demonstrate the effectiveness of granular computing-based techniques in such scenarios, where the input data contains indiscernible image regions and vague boundary regions. We propose a novel adaptive non-homogeneous granulation (ANHG) technique here that can adaptively select the smallest possible size of granules within a purview of unequally-sized granulation, based on a segmentation assessment index. Proposed ANHG in combination with deep feature learning helps in extracting complex, indiscernible information from the image data and capturing the local intensity variation of the data. Experimental results show significant performance improvement of the density-based deep feature clustering method after the incorporation of the proposed granulation scheme.

Convolutional neural networks (CNNs) with the characteristics like spatial filtering, feed-forward mechanism, and back propagation-based learning are being widely used recently for remote sensing (RS) image classification. The fixed architecture of CNN with a large number of network parameters is managed by learning through a number of iterations, and, thereby increasing the computational burden. To deal with this issue, an adaptive granulation-based CNN (AGCNN) model is proposed in the present study. AGCNN works in the framework of fuzzy set theoretic data granulation and adaptive learning by upgrading the network architecture to accommodate the information of new samples, and avoids iterative training, unlike conventional CNN. Here, granulation is done both on the 2-D input image and its 1-D representative feature vector output, as obtained after a series of convolution and pooling layers. While the class-dependent fuzzy granulation on input image space exploits more domain knowledge for uncertainty modeling, rough set theoretic reducts computed on them select only the relevant features for input to CNN. During classification of unknown patterns, a new principle of roughness-minimization with weighted membership is adopted on overlapping granules to deal with the ambiguous cases. All these together improve the classification accuracy of AGCNN, while reducing the computational time significantly. The superiority of AGCNN over some state-of-the-art models in terms of different performance metrics is demonstrated for hyperspectral and multispectral images both quantitatively and visually.

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018–23 March 2020), complete lockdown (24 March 2020–31 May 2020), and partial lockdown (1 June 2020–30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May–15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96–120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

Human leg localization problems involving sonar sensing can be posed as a nonlinear regression problem, and, nonparametric Bayesian methods, such as the Gaussian process regression (GPR) model, are potential solution candidates. In this work, to overcome the problem of irrelevant input features from the sonar range data, an advanced automatic relevance determination kernel structure is proposed to be used in the GPR model instead of the commonly used standard isotropic kernel. It is able to extract high-relevance input features even from partially trained data, thus offering a better generalization ability while improving the prediction rates and robustness significantly.

In this article, a new systematic approach to sensor fusion and state estimation is proposed for extended target tracking in human–robot coexisting environments. The developed method, called human feature-based extended target tracking via multisensor information fusion (HFBETT-MSIF), can assimilate information from the onboard camera and sonar sensor of a mobile robot in a unified way, during tracking of a pair of human shoes. A novel generalized measurement model containing the complete information of the human target is formulated for both sensors, thus rendering the tracking system potentially robust to the failure of any one sensor. The study illustrates how heteroscedastic Gaussian process (HGP) regression can be used to derive the measurement model. It also develops an advanced HGP model, called bias-minimized most likely HGP , to interpret the real-world shoe-contour data subjected to heteroscedastic noise. Performance evaluations conducted for real-life shoe tracking demonstrate the supremacy of the HFBETT-MSIF.

Indian monsoon rainfall is an extremely important affair in socio-economic well-being of the country. Recently, climatic changes have introduced significant uncertainty and irregularity in the Indian Summer Monsoon (ISM) cycle. Such unpredictability has been evidenced in all the elements of monsoon, e.g., onset, intensity, and regularity. Rain amount has been the eye of all predictions for obvious reasons. But, in the recent scenario, other components of the monsoon process have also become extremely crucial to be forecasted. The current work has regionalized Indian subcontinent using a rough-fuzzy c-means algorithm and proposed five updated monsoon zones. Four deep learning networks using Bi-LSTM architecture for four of the resulting regions have been developed for prediction of the number of rainy days one month ahead of time with a spatial resolution of 10 × 10. The model accuracy is found to be 67.48

A new algorithm, namely Z−numbers-based deep feature thresholding (Z−DFT) is described for handling the issue concerning uncertainty that arises while classifying various fall and no-fall events in complex scenarios in both indoor (viz., home and hospital) and outdoor (viz., construction area and road). The Z−DFT consists of four phases: (i) object detection and tracking, (ii) feature extraction, (iii) feature thresholding and rule generation, and (iv) Z−numbers-based analysis for quantifying the reliability of the detected falls. Unlike state-of-the-art methods, Z−DFT uses both OpenPose and object-level features. This enables better modeling of both indoor and outdoor falls. New object-level features considered are change in area, aspect ratio, speed variation, and change in direction of the detected objects. The detection of fall locations involves two phases, viz., probable location and specific location. The probable location corresponding to each OpenPose and object-level feature is determined based on its statistical information. The commonality of these probable locations results in the specific location which is determined by framing linguistic rules using all the features. Z−numbers computed with features further reflect the reliability of detection. The characteristic features of Z−DFT are demonstrated over eighteen real-time videos acquired from YouTube8M and UR Fall data, along with its superiority claimed over ten state-of-the-art algorithms.

Thunderstorms are meso-scale systems that are characterised by deep convective cumulonimbus (Cb) clouds associated with torrential rain, lightning, hail, dust storms, strong winds, downbursts, and tornadoes. As Gangetic West Bengal is prone to thunderstorm, early forecasting is imperative in order to protect life and property, and prevent the damage caused by these intense storms. The present study comprises two issues on model evaluation and interpretability by applying popular machine learning algorithms, viz., Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR) with potentially predictive thermodynamic indices and parameters for short-term predictions of pre-monsoon thunderstorms over Kolkata. The thermodynamic indices and parameters employed include convective available potential energy (CAPE), convective inhibition (CIN), Bulk Richardson number (BRN), K-Index (KI), lifted index (LI), total totals Index (TT), Showalter index (SI),temperature (TEMP), relative humidity (RELH), dew point temperature (DWPT), wind direction (DRCT), wind Speed (SKNT), mixing ratio (MIXR), severe weather threat index (SWI), potential temperature (THTA), equivalent potential temperature (THTE), and virtual potential temperature (THTV). In the proposed approach, we place a greater emphasis on the concept of Explainable artificial intelligence (XAI) to apply SHapley Additive exPlanations (SHAP), a Shapley-value-based explanation method based on the coalitional game theory. The SHAP approach, as a primary interface, enables the identification and prioritization of features that determine the occurrences of pre-monsoon thunderstorms and compares the two different machine learning algorithms. SHAP can quantify the contribution of predictor variables to each data point and rank the importance of predictor variables in terms of their contributions to the model output. It also facilitates the computation of different plots on both global and local levels. Accordingly, it can help determine the validity of the model based on domain expertise by identifying the most important variables. The results indicate that both XGBoost and LR support the dominant positive influence of the convective available potential energy (CAPE), while the ranks and interpretations of the other predictor variables differ. Although, these two models perform well in predicting the pre-monsoon thunderstorms, they may favour different predictor variables due to their varying natures, thereby resulting in different explainability.

In this article, we have developed an improved failure mode and effect analysis (FMEA) model by leveraging the concepts of Z-number, rough number (RN), and probabilistic distance measure. Two new concepts, namely, double upper approximated rough number (DUARN) and granulized Z-number (gZN), a new scheme for measuring distance between two gZNs using weighted similarity and average linkage method, a new risk prioritization model, named, granulized Z-VIKOR, and a scheme for uncertainty assessment using box-plot are proposed. DUARN embodies the notion of double sided upper approximation of an ordinal decision class. gZN is developed using Z-number and DUARN. The distance between two gZNs is computed using the maximum entropy principle that captures the relationship between the A (opinion) and B (reliability of A) parts of gZN. The granulized Z-VIKOR involves synergistic integration of gZN and VIKOR. Both objective and subjective risk measures are computed and a combined risk measure is defined, which considers the interactions among the risk criteria using λ-Shapley index. Two case studies are conducted. Sensitivity and comparative analysis is carried out to demonstrate the applicability, effectiveness, and robustness of the proposed model, as well as its superiority to existing models.

Existing traffic video summarization algorithms are capable of detecting one-class (i.e., collision) anomaly and cannot handle uncertainty issues arising between two-class anomalies, such as collision and near-miss. To address the issues, a new video summarization algorithm, namely Z-number s-based spatio-temporal rough fuzzy granulation (Z-STRFG) is developed. In Z-STRFG, various spatio-temporal features are computed over the video frames and used for obtaining the approximate anomaly-prone regions in terms of granules. In these regions, uncertainty (i.e., fuzziness) may arise among three scenarios, namely collision, near-miss, and normal traffic. Therefore, two types of rough fuzzy granules (RFGs) along with their roughness scores are computed to distinguish the aforesaid three scenarios. For each RFG, Z-number is computed based on the membership value of its roughness score to ensure a higher degree of reliability in the detection of anomaly class. Aforesaid characteristics of Z-STRFG improve its speed and accuracy for traffic anomaly detection. The efficacy of Z-STRFG has been demonstrated over 130 real-time traffic videos containing collisions, near-misses, and normal traffics. The superiority of Z-STRFG over some state-of-the-art is also proved through extensive experiments.

Kolkata has a reputation for being one of the world’s most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to “very poor” (301 ≤ AQI ≤ 400) and “poor” (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM2.5 and PM10 that cause the AQI of Kolkata to deteriorate to “very poor” and “poor.” For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM10 and PM2.5. Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

The present article proposes a new human leg localization (HLL) algorithm using ultrasonic sensors in human–robot coexisting environments. The algorithm estimates the motion of a human leg pair between two successive sonar scans by using a new static cluster elimination (SCE) method, an edge feature-based leg recognition algorithm, and an advanced scan matching technique. We also propose a novel, robust approach to overcome bad initialization problem in sonar scan matching, by introducing a metaheuristic search (MHS)-based optimization algorithm for the sonar normal distributions transform (sNDT) method. The recently proposed dynamic neighborhood learning-based GSA (DNLGSA) has been successfully utilized in real-life scenario to solve this problem. The work also proposes a new chaos enhanced DNLGSA (CEDNLGSA) to further improve real-life performance and the proposed novel variant of the sNDT method based on CEDNLGSA, called chaotic MHS-based sNDT (CMHS-sNDT), has been demonstrated to achieve superior leg detection performance in various real-life case studies, compared to different contemporary state-of-the-art methods.

Changepoint detection is the problem of finding abrupt or gradual changes in time series data when the distribution of the time series changes significantly. There are many sophisticated statistical algorithms for solving changepoint detection problem, although there is not much work devoted towards gradual changepoints as compared to abrupt ones. Here we present a new approach to solve the changepoint detection problem using the fuzzy rough set theory which is able to detect such gradual changepoints. An expression for the rough-fuzzy estimate of changepoints is derived along with its mathematical properties concerning fast computation. In a statistical hypothesis testing framework, the asymptotic distribution of the proposed statistic on both single and multiple changepoints is derived under the null hypothesis enabling multiple changepoint detection. Extensive simulation studies have been performed to investigate how simple crude statistical measures of disparity can be subjected to improve their efficiency in the estimation of gradual changepoints. Also, the said rough-fuzzy estimate is robust to signal-to-noise ratio, a high degree of fuzziness in true changepoints, and also to hyperparameter values. Simulation studies reveal that the proposed method beats other methods of gradual changepoint detection (including MJPD, HSMUCE, fuzzy methods like FCP, FCMLCP etc) and also popular crisp methods like Binary Segmentation, PELT, and BOCD in detecting gradual changepoints. The applicability of the estimate is demonstrated using multiple real-life datasets including Covid-19. We have developed the python package roufcp for broader dissemination of the methods.

In this paper, the problem of ensuring reliable energy distribution in smart grid is studied, while considering that each customer is connected with multiple micro-grids. In the traditional smart grid, each customer is connected with a single micro-grid. Additionally, in the existing literature, some researchers proposed energy distribution schemes considering the presence of multiple micro-grids. However, none of these existing schemes consider that the customers can consume energy from multiple micro-grids simultaneously, which can essentially enhance the quality of service (QoS) in energy distribution, as it aids in reducing the transmission loss and increasing the profit of the micro-grids, while the customers pay less. To address the aforementioned problem, we design a sustainable energy distribution scheme, named SEED, to decide the distributed energy request vector, while ensuring high QoS in terms of energy availability and the price charged by the micro-grids in smart grid. We use an evolutionary game to ensure that the energy load is optimally distributed among the micro-grids and each micro-grid gets an equal opportunity to earn a profit. Through simulation, we observe that using SEED, renewable energy consumption per customer improves by 14.05 percent while reducing the cost by 29.87 percent. In other words, SEED ensures a sustainable environment by reducing the CO2 emission by 14.05 percent, while reducing non-renewable energy consumption from the main grid. Additionally, the profit of each micro-grid increases by 58.32 percent.

In this article, we present a study on the development in the theory and application of the Z-numbers since its inception in 2011. The review covers the formalization of Z-number-based mathematical operators, the role of Z-numbers in computing with words, decision-making, and trust modeling, application of Z-numbers in real-world problems such as multisensor data fusion, dynamic controller design, safety analytics, and natural language understanding, a brief comparison with conceptually similar paradigms, and some potential areas of future investigation. The paradigm currently has at least four extensions to its definition: multidimensional Z-numbers, parametric Z-numbers, hesitant-uncertain linguistic Z-numbers, and Z*-numbers. The Z-numbers have also been used in conjunction with rough sets and granular computing for enhanced uncertainty handling. While this decade has seen a plethora of theoretical initiatives toward its growth, there remains a major work scope in the direction of practical realization of the paradigm. Some challenges yet unresolved are automated translation of (imprecise, sarcastic, and metaphorical) linguistic expressions to their Z-number forms, discernment of probability–possibility distributions to map real-world situations under consideration, analysis of linguistic equivalents of Z-operator results to intuitive human responses, the endogenous arousal of belief in intelligent agents, and analysis of biases embedded in expert-belief values that are primary inputs to Z-number-based expert systems. After a decade of the Z-numbers, the paradigm has proved to be of use in expert-input-based decision-making systems and initial value problems. Its applicability in high-risk, high-precision areas, such as deep-sea exploration and space science, remains unexplored.

One of the most concerning safety hazards for elderly people is abnormal falls in public places. Vision-based fall detection using ambient cameras is a popular non-intrusive solution. Recent research uses Slow Feature Analysis (SFA), which can learn the slow invariant varying shape features obtained from input signals and is efficient. Another recent famous approach in motion detection is deep learning. However, the fall event in actual cases is diverse, resulting in complications in the detection task. Additionally, it is difficult to acquire fall-related data; hence, simulation is done on fall events to generate a training dataset, resulting in smaller data. Considering these complications, we have presented a novel method by combining SFA, deep learning models, namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), and rule-base. CNN is used to extract the object region, thereby reducing the region of interest (RoI). Two shape features, such as aspect ratio and area of RoI are considered as input to the LSTM for retrieving the temporal information which is further used for rule generation, thereby increasing the detection accuracy. The efficacy of the proposed method for various features, such as aspect ratio, area, and aspect r $a$ tio+area is demonstrated over the UR Fall data with an accuracy of 95.2%, 93.8%, and 96.36%, respectively.