In the highly competitive landscape of e-commerce advertising, maximizing return on advertising spend (ROAS) is crucial yet inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models struggle to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework. The architecture was motivated by initial exploratory analysis using a Bayesian Belief Network (BBN) to map structural dependencies, while the final deep learning model overcomes scalability limitations using self-attention mechanisms and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN captures dependencies among campaign variables, enhancing interpretability, while the hierarchical deep learning architecture leverages self-attention mechanisms to address scalability challenges in high-dimensional settings. Experimental results reveal that the proposed framework achieves 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback–Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R 2 of 98% with an inference speed of 5.2 ms per campaign, confirming its feasibility for real-time bidding applications which typically require sub-10ms latency, enabling a feasible real-time bidding. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising. • Proposed Hierarchical Bayesian Deep Learning model improves ROAS prediction accuracy. • Achieves 22.8% lower RMSE and 27.4% better Negative Log Likelihood than SOTA. • Combines Bayesian Belief and Mixture Density Networks for precise, uncertainty-aware predictions. • Enables real-time bidding with an inference speed of 5.2 ms per campaign. • Utilizes attention-driven feature selection for scalable and explainable predictions.

Abstract In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer’s, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.

Recently, unsupervised cross-dataset person reidentification (Re-ID) has attracted more and more attention, which aims to transfer knowledge of a labeled source domain to an unlabeled target domain. There are two common frameworks: one is pixel-alignment of transferring low-level knowledge, and the other is feature-alignment of transferring high-level knowledge. In this article, we propose a novel recurrent autoencoder (RAE) framework to unify these two kinds of methods and inherit their merits. Specifically, the proposed RAE includes three modules, i.e., a feature-transfer (FT) module, a pixel-transfer (PT) module, and a fusion module. The FT module utilizes an encoder to map source and target images to a shared feature space. In the space, not only features are identity-discriminative but also the gap between source and target features is reduced. The PT module takes a decoder to reconstruct original images with its features. Here, we hope that the images reconstructed from target features are in the source style. Thus, the low-level knowledge can be propagated to the target domain. After transferring both high-and low-level knowledge with the two proposed modules above, we design another bilinear pooling layer to fuse both kinds of knowledge. Extensive experiments on Market-1501, DukeMTMC-ReID, and MSMT17 datasets show that our method significantly outperforms either pixel-alignment or feature-alignment Re-ID methods and achieves new state-of-the-art results.

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done for immutability, privacy and security, mutual consensus, transparency, accountability, tracking and tracing, and temperature‒humidity control parameters.

Drone-camera based human activity recognition (HAR) has received significant attention from the computer vision research community in the past few years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is divided into two components. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability . In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H ∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model . Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H ∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics.

Abstract In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

This study discusses the new stochasticmaximum power point trackingcontrol approach toward thephotovoltaic cells(PCs). A PC generator is isolated from the grid, resulting in adirect currentmicrogrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposednetworked control systemsbeneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into theTakagi–Sugeno(T--S) fuzzy model is another option. The T--S fuzzy model is presented in a unified framework, for which 1) the fuzzy observer based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer-based controller can be created using the same premises being the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulation in the PC generator model for the real-time climate data obtained in China demonstrates the importance of our method. In addition, by using a newLyapunov–Krasovskii functionalfor combining with the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the tensor-product (T-P)transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators.

Group Activity Recognition requires spatiotemporal modeling of an exponential number of semantic and geometric relations among various individuals in a scene. Previous attempts model these relations by aggregating independently derived spatial and temporal features. This increases the modeling complexity and results in sparse information due to lack of feature correlation. In this paper, we propose Weighted Temporal Attention Mechanism (WTM), a representational mechanism that combines spatial and temporal features of a local subset of a visual sequence into a single 2D image representation, highlighting areas of a frame where actor motion is significant. Pairwise dense optical flow maps representing the temporal characteristic of individuals over a sequence are used as attention masks over raw RGB images through a multi-layer weighted aggregation. We demonstrate a strong correlation between spatial and temporal features, which helps localize actions effectively in a multi-person scenario. The simplicity of the input representation allows the model to be trained by 2D image classification architectures in a plug-and-play fashion, which outperforms its multi-stream and multi-dimensional counterparts. The proposed method achieves the lowest computational complexity in comparison to other works. We demonstrate the performance of WTM on two widely used public benchmark datasets, namely the Collective Activity Dataset (CAD) and the Volleyball Dataset. and achieve state-of-the-art accuracies of 95.1% and 94.6% respectively. We also discuss the application of this method to other datasets and general scenarios. The code is being made publicly available.

Classifying complex human motion sequences is a major research challenge in the domain of human activity recognition. Currently, most popular datasets lack a specialized set of classes pertaining to similar action sequences (in terms of spatial trajectories). To recognize such complex action sequences with high inter-class similarity, such as those in karate, multiple streams are required. To fulfill this need, we propose MS-KARD, a Multi-Stream Karate Action Recognition Dataset that uses multiple vision perspectives, as well as sensor data - accelerometer and gyroscope. It includes 1518 video clips along with their corresponding sensor data. Each video was shot at 30fps and lasts around one minute, equating to a total of 2,814,930 frames and 5,623,734 sensor data samples. The dataset has been collected for 23 classes like Jodan Zuki, Oi Zuki, etc. The data acquisition setting involves the combination of 2 orthogonal web cameras and 3 wearable inertial sensors recording both vision and inertial data respectively. The aim of this dataset is to aid research that deals with recognizing human actions that have similar spatial trajectories. The paper describes statistics of the dataset, acquisition setting, and provides baseline performance figures using popular action recognizers. We propose an ensemble-based method, KarateNet, that performs decision-level fusion on the two input modalities (vision and sensor data) to classify actions. For the first stream, the RGB frames are extracted from the videos and passed into action recognition networks like Temporal Segment Network (TSN) and Temporal Shift Module (TSM). For the second stream, the sensor data is converted into a 2-D image and fed into a Convolutional Neural Network (CNN). The results reported were obtained on performing a fusion of the 2 streams. We also report results on ablations that use fusion with various input settings. The dataset and code will be made publicly available.

Human Activity Recognition is an important task in Computer Vision that involves the utilization of spatio-temporal features of videos to classify human actions. The temporal portion of videos contains vital information needed for accurate classification. However, common Deep Learning methods simply average the temporal features, thereby giving all frames equal importance irrespective of their relevance, which negatively impacts the accuracy of the model. To combat this adverse effect, this paper proposes a novel Transformer Based Attention Consensus (TBAC) module. The TBAC module can be used in a plug-and-play manner as an alternate to the conventional consensus meth-ods of any existing video action recognition network. The TBAC module contains four components: (i) Query Sampling Unit, (ii) Attention Extraction Unit, (iii) Softening Unit, and (iv) Attention Consensus Unit. Our experiments demonstrate that the use of the TBAC module in place of classical consensus can improve the performance of the CNN-based action recognition models, such as Channel Separated Convolutional Network (CSN), Temporal Shift Module (TSM), and Temporal Segment Network (TSN). We also propose the Decision Consensus (DC) algorithm that utilizes multiple independent but related action recognizer models in order to improve upon the performance of most of these constituent models, using a novel fusion algorithm. Results have been obtained on two benchmark human action recognition datasets, HMDB51 and HAA500. The use of the proposed TBAC module along with Decision Consensus achieves state-of-the-art performances, with 85.23% and 83.73% classification accuracies on the two databases HMDB51 and HAA500, respectively. The code will be made publicly available.

Yoga can be seen as a set of fitness exercises involving various body postures. Most of the available pose and action recognition datasets are comprised of easy-to-moderate body pose orientations and do not offer much challenge to the learning algorithms in terms of the complexity of pose. In order to observe action recognition from a different perspective, we introduce YogaTube, a new large-scale video benchmark dataset for yoga action recognition. YogaTube aims at covering a wide range of complex yoga postures, which consist of 5484 videos belonging to a taxonomy of 82 classes of yoga asanas. Also, a three-stream architecture has been designed for yoga asanas pose recognition using two modules, feature extraction, and classification. Feature extraction comprises three parallel components. First, pose is estimated using the part affinity fields model to extract meaningful cues from the practitioner. Second, optical flow is used to extract temporal features. Third, raw RGB videos are used for extracting the spatiotemporal features. Finally in the classification module, pose, optical flow, and RGB streams are fused to get the final results of the yoga asanas. To the best of our knowledge, this is the first attempt to establish a video benchmark yoga recognition dataset. The code and dataset will be released soon.

As a mental disorder, depression has affected people's lives, works, and so on. Researchers have proposed various industrial intelligent systems in the pattern recognition field for audiovisual depression detection. This paper presents an end-to-end trainable intelligent system to generate high-level representations over the entire video clip. Specifically, a three-dimensional (3D) convolutional neural network equipped with a module spatiotemporal feature aggregation module (STFAM) is trained from scratch on audio/visual emotion challenge (AVEC)2013 and AVEC2014 data, which can model the discriminative patterns closely related to depression. In the STFAM, channel and spatial attention mechanism and an aggregation method, namely 3D DEP-NetVLAD, are integrated to learn the compact characteristic based on the feature maps. Extensive experiments on the two databases (i.e., AVEC2013 and AVEC2014) are illustrated that the proposed intelligent system can efficiently model the underlying depression patterns and obtain better performances over the most video-based depression recognition approaches. Case studies are presented to describes the applicability of the proposed intelligent system for industrial intelligence.

As a common mental disorder, depression has attracted many researchers from affective computing field to estimate the depression severity. However, existing approaches based on Deep Learning (DL) are mainly focused on single facial image without considering the sequence information for predicting the depression scale. In this paper, an integrated framework, termed DepNet, for automatic diagnosis of depression that adopts facial images sequence from videos is proposed. Specifically, several pretrained models are adopted to represent the low-level features, and Feature Aggregation Module is proposed to capture the high-level characteristic information for depression analysis. More importantly, the discriminative characteristic of depression on faces can be mined to assist the clinicians to diagnose the severity of the depressed subjects. Multiscale experiments carried out on AVEC2013 and AVEC2014 databases have shown the excellent performance of the intelligent approach. The root mean-square error between the predicted values and the Beck Depression Inventory-II scores is 9.17 and 9.01 on the two databases, respectively, which are lower than those of the state-of-the-art video-based depression recognition methods.

Abstract This paper presents a method of action location in three-dimensional motion teaching. The machine vision technology is used to solve the problems of low positioning accuracy and long positioning time in the traditional motion three-dimensional teaching method. The work of this method is as follows: (a) using machine vision method to determine the world coordinate system of the image; (b) using MRF algorithm to extract the features of 3D teaching action image; (c) determining the spatial correlation of 3D teaching action data. In the three-dimensional teaching action image, the smooth filtering technology is used to suppress and eliminate the noise. Then the convolution neural network (CNN) is used to reconstruct the three-dimensional teaching action image. The entropy of three-dimensional teaching behavior of physical education is determined by CNN. Through a large number of computer simulations, the effectiveness of the proposed system is confirmed. The results show that the system achieves 95% accuracy when the positioning time is 1.9 s.

Abstract Human activity recognition aims to determine actions performed by a human in an image or video. Examples of human activity include standing, running, sitting, sleeping,etc. These activities may involve intricate motion patterns and undesired events such as falling. This paper proposes a novel deep convolutional long short-term memory (ConvLSTM) network for skeletal-based activity recognition and fall detection. The proposed ConvLSTM network is a sequential fusion of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected layers. The acquisition system applies human detection and pose estimation to pre-calculate skeleton coordinates from the image/video sequence. The ConvLSTM model uses the raw skeleton coordinates along with their characteristic geometrical and kinematic features to construct the novel guided features. The geometrical and kinematic features are built upon raw skeleton coordinates using relative joint position values, differences between joints, spherical joint angles between selected joints, and their angular velocities. The novel spatiotemporal-guided features are obtained using a trained multi-player CNN-LSTM combination. Classification head including fully connected layers is subsequently applied. The proposed model has been evaluated on the KinectHAR dataset having 130,000 samples with 81 attribute values, collected with the help of a Kinect (v2) sensor. Experimental results are compared against the performance of isolated CNNs and LSTM networks. Proposed ConvLSTM have achieved an accuracy of 98.89% that is better than CNNs and LSTMs having an accuracy of 93.89 and 92.75%, respectively. The proposed system has been tested in realtime and is found to be independent of the pose, facing of the camera, individuals, clothing,etc. The code and dataset will be made publicly available.

DNA methylation is an epigenetic marker that plays an important role in the biological processes of regulating gene expression, maintaining chromatin structure, imprinting genes, inactivating X chromosomes, and developing embryos. The traditional detection method is time-consuming. Currently, researchers have used effective computational methods to improve the efficiency of methylation detection. This study proposes a fuzzy model with correntropy induced loss (C-loss) function to identify DNA N4-methylcytosine (4 mC) sites. To improve the robustness and performance of the model, we use kernel method and the C-loss function to build a higher order fuzzy inference systems. To test performance, our model is implemented on six 4 mC and eight University of California Irvine (UCI) datasets. The experimental results show that our model achieves better prediction performance.

Playing games always been interest in child and adolescent. Technology specially internet given new dimension to gaming and over the decade gaming over internet attracted major percentage of player. Excessive gaming when not controlled also lead to the behavioral dysfunction along with physiological issues. World Health Organization (WHO) has added Internet Gaming Disorder (IGD) as potential mental disorder recently in Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as research field to include in main manual. Research in this field gained interest, but about diagnostic criteria debate is still on in research group. For diagnosis of IGD, not only subjective symptoms but its underlying neurobiology needs to be included. Major behavioral addiction shares its symptoms with different addiction, which can be seen into IGD and Alcohol Use Disorder (AUD) also. In this paper, we aim to identify potential biomarkers to explore relationships between AUD and IGD symptoms using EEG signals recordings. Noise and other artifacts are removed during pre-processing and bands splitted in intermediate steps of proposed method. Spectral features are potential indicator in EEG data, for all five EEG bands power spectral density is computed and visible difference found in mean power on most of the channels for all bands. Across theta and beta bands, mean absolute power is greatly reduced in the frontal and prefrontal brain region. The results indicate symptoms are matching with impulsive behavior. Based on statistical analysis across we found similarities in EEG features in terms of emotional imbalance, high arousal, slow response inhibition and our study indicate Alcohol Use Disorder and IGD are correlated.

This paper explores relationship between depression and internet gaming disorder (IGD) symptoms using electroencephalography (EEG) signals recordings. With the advancement of technology and penetration of Internet in human's life, gaming community also has shifted to online platform like massively multiplayer online role-playing games (MMORPGs) instead of physical games, causing multiple health hazards including behavioral and physical anomalies. Diagnosis of IGD is an arduous task using subjective assessments and need neurological methods to be used in identification. The umbrella of symptoms for IGD overlap with other neurological and behavioral disorders like depression, attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), thus can share the methods for their identification. In this paper, we determine the power spectral density (SPD) of EEG recording for depression subjects. Rigorous experiments are conducted to analyze the proposed system. Experimental results reveal the coherence between the power for the beta band for depression and IGD subjects. The results suggest that due to the comorbidity of IGD and depression correlation are cohesively predictive.

Abstract Glaucoma is an ailment causing permanent vision loss but can be prevented through the early detection. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis. But accurate segmentation of disc and cup is still a challenge. To mitigate this challenge, an effective system for optic disc and cup segmentation using deep learning architecture is presented in this paper. Modified Groundtruth is utilized to train the proposed model. It works as fused segmentation marking by multiple experts that helps in improving the performance of the system. Extensive computer simulations are conducted to test the efficiency of the proposed system. For the implementation three standard benchmark datasets such as DRISHTI-GS, DRIONS-DB and RIM-ONE v3 are used. The performance of the proposed system is validated against the state-of-the-art methods. Results indicate an average overlapping score of 96.62%, 96.15% and 98.42% respectively for optic disc segmentation and an average overlapping score of 94.41% is achieved on DRISHTI-GS which is significant for optic cup segmentation.

Abstract A cosmetic product recognition system is proposed in this paper. For this recognition system, we have proposed a cosmetic product database that contains image samples of forty different cosmetic items. The purpose of this recognition system is to recognize Cosmetic products with there types, brands and retailers such that to analyze a customer experience what kind of products and brands they need. This system has various applications in such as brand recognition, product recognition and also the availability of the products to the vendors. The implementation of the proposed system is divided into three components: preprocessing, feature extraction and classification. During preprocessing we have scaled and transformed the color images into gray-scaled images to speed up the process. During feature extraction, several different feature representation schemes: transformed, structural and statistical texture analysis approaches have been employed and investigated by employing the global and local feature representation schemes. Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision Tree classifiers have been employed to perform the classification tasks. Apart from this, we have also performed some data analytic tasks for Brand Recognition as well as Retailer Recognition and for these experimentation, we have employed some datasets from the ‘Kaggle’ website and have obtained the performance due to the above-mentioned classifiers. Finally, the performance of the cosmetic product recognition system, Brand Recognition and Retailer Recognition have been aggregated for the customer decision process in the form of the state-of-the-art for the proposed system.

Summary A novel design and data analytics for an electronic human resource management (e‐HRM) system has been proposed in this article. E‐HRM software is being widely used in big industries and institutions. This e‐HRM is very cost‐effective, competence, congruence, and commitment for the organization. At present, Internet of Things (IoT) have great impact on e‐HRM, which gives various facilities and supports to e‐HRM functionalities such as securities, standards, privacy, and regulations. The combination of e‐HRM with IoT has wide applications for implementing policies, strategies, and practices within the organization. An e‐HRM has mainly five activities: e‐Selection, e‐Recruitment, e‐Performance, e‐Compensation, and e‐Learning. In this work, the proposed system has two parts. In the first part, the various e‐HRM activities have been discussed and elaborated with examples. In the second part, the description of data analytics based on IoT for each e‐HRM activity has been discussed and demonstrated. Here the data analytics part is divided into four components: (a) data preprocessing; (b) feature selection; (c) data classification; and (d) performance evaluation. Extensive experimentation has been performed for each e‐HRM activity using four HR analytic datasets from Kaggle site, and finally, the performance with proper justifications has been exquisitely done using each dataset respect to each e‐HRM activity.

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal - more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods.

Sarcasm detection in conversation, a theoretically and practically challenging artificial intelligence task, aims to discover elusively ironic, contemptuous, and metaphoric information implied in daily conversations. Most of the recent approaches in sarcasm detection have neglected the intrinsic vagueness and uncertainty of human language in emotional expression and understanding. To address this gap, we propose a complex-valued fuzzy network by leveraging the mathematical formalisms of quantum theory and fuzzy logic. In particular, the target utterance to be recognized is considered as a quantum superposition of a set of separate words. The contextual interaction between adjacent utterances is described as the interaction between a quantum system and its surrounding environment, constructing the quantum composite system, where the weight of interaction is determined by a fuzzy membership function. In order to model both the vagueness and uncertainty, the aforementioned superposition and composite systems are mathematically encapsulated in a density matrix. Finally, a quantum fuzzy measurement is performed on the density matrix of each utterance to yield the probabilistic outcomes of sarcasm recognition. Extensive experiments are conducted on the MUStARD and the 2020 sarcasm detection Reddit track datasets, and the results show that our model outperforms a wide range of strong baselines.

The Semantic Internet of Things (SIoT) is the extension of the Internet of Things (IoT) and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the IoT. However, the SIoT has the characteristics of both the IoT and the Semantic Web environment, and the corresponding semantic data present many new data features. In this article, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph (UKG), allowing us to be applied to the environment of the SIoT better. Here, we design a semantic collaboration method based on a UKG. It can take the UKG as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing the semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the SIoT. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes.

The Internet-of-Things (IoT) healthcare system monitors a patients' condition and takes preventive measures in case of an emergency. The electrocardiogram (ECG) that measures the electrical activity of the heart is one of the important health indicators. Thanks to the wearable technology, nowadays, we can even measure the ECG using smart portable devices and send via a wireless channel. However, this wireless transmission has to minimize both energy and memory consumption. In this article, we propose CULT-an ECG compression technique using unsupervised dictionary learning. Our method achieves a high compression rate due to the essence of dictionary learning and is immune to the noise by integrating discrete cosine transformation. Moreover, it continuously expands the dictionary when the unseen pattern occurs and refines the dictionary when new input arrives, by imposing the double dictionary scheme. We show that our method has a better performance by comparing it with the other existing approaches.

Internet of Things (IoT) refers to the complex systems generated by the interconnections among widely available objects. Such interactions generate large networks, whose complexity needs to be addressed to provide suitable computationally efficient approaches. In this article, we propose a distributed local community detection algorithm based on specific properties of community center expansions (DLCD-CCE) for large-scale complex networks. The algorithm is evaluated via a prototype system, based on Spark, to verify its accuracy and scalability. The results demonstrate that compared to the typical local community detection algorithms, DLCD-CCE has better accuracy, stability, and scalability, and effectively overcomes the problem that existing algorithms are sensitive to the location of initial seeds.

Essential genes are considered to be the genes required to sustain life of different organisms. These genes encode proteins that maintain central metabolism, DNA replications, translation of genes, and basic cellular structure, and mediate the transport process within and out of the cell. The identification of essential genes is one of the essential problems in computational genomics. In this present study, to discriminate essential genes from other genes from a non-biologists perspective, the purine and pyrimidine distribution over the essential genes of four exemplary species, namely Homo sapiens , Arabidopsis thaliana , Drosophila melanogaster , and Danio rerio are thoroughly experimented using some quantitative methods. Moreover, the Indigent classification method has also been deployed for classification on the essential genes of the said species. Based on Shannon entropy, fractal dimension, Hurst exponent, and purine and pyrimidine bases distribution, 10 different clusters have been generated for the essential genes of the four species. Some proximity results are also reported herewith for the clusters of the essential genes.