Showing papers by "Al Ain University of Science and Technology published in 2021"

Privacy-Preserving Multiobjective Sanitization Model in 6G IoT Environments

Abstract: The next revolution of the smart industry relies on the emergence of the Industrial Internet of Things (IoT) and 5G/6G technology. The properties of such sophisticated communication technologies will change our perspective of information and communication by enabling seamless connectivity and bring closer entities, data, and “things.” Terahertz-based 6G networks promise the best speed and reliability, but they will face new man-in-the-middle attacks. In such critical and high-sensitive environments, the security of data and privacy of information still a big challenge. Without privacy-preserving considerations, the configuration state may be attacked or modified, thus causing security problems and damage to data. In this article, motivated by the need to secure 6G IoT networks, an ant colony optimization (ACO) approach is presented by adopting multiple objectives as well as using transaction deletion to secure confidential and sensitive information. Each ant in the population is represented as a set of possible deletion transactions for hiding sensitive information. We utilize the use of a prelarge concept to assist in the reduction of multiple database scans in the evaluation progress. We then also adopt external solutions to maintain discovered Pareto solutions, thus improving effectiveness to find optimized solutions. Experiments are conducted comparing our methodology to state-of-the-art bioinspired particle swarm optimization (PSO) as well as genetic algorithm (GA). Our strong results clearly show that the designed approach achieves fewer side effects while maintaining low computational cost overall (Chen et al. , 2020).

Federated Learning in Vehicular Networks: Opportunities and Solutions

Abstract: The emerging advances in personal devices and privacy concerns have given the rise to the concept of Federated Learning. Federated Learning proves its effectiveness and privacy preservation through collaborative local training and updating a shared machine learning model while protecting the individual data-sets. This article investigates a new type of vehicular network concept, namely a Federated Vehicular Network (FVN), which can be viewed as a robust distributed vehicular network. Compared to traditional vehicular networks, an FVN has centralized components and utilizes both DSRC and mmWave communication to achieve more scalable and stable performance. As a result, FVN can be used to support data-/computation-intensive applications such as distributed machine learning and Federated Learning. The article first outlines the enabling technologies of FVN. Then, we briefly discuss the high-level architecture of FVN and explain why such an architecture is adequate for Federated Learning. In addition, we use auxiliary Blockchain-based systems to facilitate transactions and mitigate malicious behaviors. Next, we discuss in detail one key component of FVN, a federated vehicular cloud (FVC), that is used for sharing data and models in FVN. In particular, we focus on the routing inside FVCs and present our solutions and preliminary evaluation results. Finally, we point out open problems and future research directions of this disruptive technology.

A Blockchain-empowered Access Control Framework for Smart Devices in Green Internet of Things

Abstract: Green Internet of things (GIoT) generally refers to a new generation of Internet of things design concept. It can save energy and reduce emissions, reduce environmental pollution, waste of resource...

A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load

Abstract: COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained $\mathcal{R}_{0} \approx 1.50$ . Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning.

Abstract: The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system’s performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system’s improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

Design Guidelines for Blockchain-Assisted 5G-UAV Networks

Abstract: Fifth generation (5G) wireless networks are designed to meet various end-user quality of service (QoS) requirements through high data rates (typically of gigabits per second) and low latencies. Coupled with fog and mobile edge computing, 5G can achieve high data rates, enabling complex autonomous smart city services such as the large deployment of self-driving vehicles and large-scale artificial-intelligence-enabled industrial manufacturing. However, to meet the exponentially growing number of connected IoT devices and irregular data and service requests in both low- and high-density locations, the process of enacting traditional cells supported through fixed and costly base stations requires rethought to enable on-demand mobile access points in the form of unmanned aerial vehicles (UAV) for diversified smart city scenarios. This article envisions a 5G network environment that is supported by blockchain-enabled UAVs to meet dynamic user demands with network access supply. The solution enables decentralized service delivery (drones as a service) and routing to and from end users in a reliable and secure manner. Both public and private blockchains are deployed within the UAVs, supported by fog and cloud computing devices and data centers to provide a wide range of complex authenticated service and data availability. Particular attention is paid to comparing data delivery success rates and message exchange in the proposed solution against traditional UAV-supported cellular networks. Challenges and future research are also discussed with highlights on emerging technologies such as federated learning.

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Abstract: Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. In this paper, state-of-the-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. This is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis in one place. It is observed that there is a dire need for low energy and low cost but accurate auto diagnosis solutions for the proactive management of AF.

QMSIW-Based Single and Triple Band Bandpass Filters

Abstract: This brief proposes compact and planar substrate integrated waveguide (SIW)-based filters for single and triple band operations using two quarter-mode SIW (QMSIW) cavities Two miniaturized QMSIW cavity resonators (QMSIWCRs) are capacitively coupled Miniaturization in the QMSIWCR is achieved by adding an arc-shaped slot and a rectangular open-ended stub at the closed-end of the QMSIWCR In the triple band bandpass filter (BPF), a capacitor is added to the open-ended side of the stub to enhance impedance matching at the higher modes It has the centre frequencies of 21, 398 and 66 GHz with an insertion losses (ILs) of 093, 098, and 12 dB at the lower, middle and upper passband, respectively Moreover, the measured fractional bandwidths (FBWs) are 1697, 877, and 842% at the lower, middle and upper passband, respectively Two rectangular slots are etched on the upper layer of the cavity to suppress the second mode and reduce coupling of the third mode; this realizes a single-band filter with an improved upper stopband response It has the centre frequency of 21 GHz with a wide upper stop-band response, having a rejection level of 20 dB up to $319f_{c}$ The operation of filter is additionally discussed and verified by means of a lumped element equivalent circuit model and experimental results The proposed filters are suitable for integration with other planar radio-frequency (RF) components due to their compact sizes, low ILs, planar structures, multiple transmission zeros (TZs) and adequate bandwidths

Generalizing AI: Challenges and Opportunities for Plug and Play AI Solutions

Abstract: Artificial Intelligence (AI) has revolutionized today's Internet of Things (IoT) applications and services by introducing significant technological enhancements across a multitude of domains. With the deployment of the fifth generation (5G) mobile communication network, smart city visions of fast, on-demand, intelligent user-specific services are now becoming a reality. The concept of connected IoT is evolving into connected intelligent things. The advancements of both AI techniques, coupled with the sophistication of edge devices, is now leading to a new era of connected intelligence. Moving the intelligence toward end devices must account for latency demands and simplicity of selecting the type of AI technique to be used. Moreover, since most AI techniques require learning from big data sets and reasoning using a multitude of classification patterns, new simplified and collaborative solutions are now necessary more than ever. As such, the concept of introducing decentralized and distributed ‘Plug and Play’ (PnP) AI tools is now becoming more attractive given the vast numbers in edge devices, data volume and AI techniques. To this end, this article envisions a novel general AI solution that can be adapted to autonomously select the type of machine learning (ML) algorithm, the data set to be used, and provide reasoning in regards to data selection for optimal features extraction. Moreover, the solution performs the necessary training and all the necessary parameter fine-tunings to achieve the highest level of generality and simplicity for AI at the edge. We explore several aspects related to PnP-AI and its impact in the smart city ecosystem.

Does globalization affect the green economy and environment? The relationship between energy consumption, carbon dioxide emissions, and economic growth

Abstract: This study analyzes the relationship between globalization, energy consumption, and economic growth among selected South Asian countries to promote the green economy and environment. This study also finds causal association between energy growth and nexus of CO2 emissions and employed the premises of the EKC framework. The study used annual time series analysis, starting from 1985 to 2019. The data set has been collected from the World Development Indicator (WDI). The result of a fully modified ordinary least square (FMOLS) method describes a significantly worse quality environment in the South Asian region. The individual country as Bangladesh shows a positively significant impact on the CO2 emissions and destroys the level of environment regarding non-renewable energy and globalization index. However, negative and positive growth levels (GDP) and square of GDP confirm the EKC hypothesis in this region. This study has identified the causality between GDP growth and carbon emission and found bidirectional causality between economic growth and energy use.

SynergyChain: Blockchain-Assisted Adaptive Cyber-Physical P2P Energy Trading

Abstract: Industrial investments into distributed energy resource technologies are increasing and playing a pivotal role in the global transactive energy, as part of a wider drive to provide a clean and stable source of energy. The management of prosumers, which consume and as well as generate energy, with heterogeneous energy sources is critical for sustainable and efficient energy trading procedures. This article proposes a blockchain-assisted adaptive model, namely SynergyChain, for improving the scalability and decentralization of the prosumer grouping mechanism in the context of peer-to-peer energy trading. Smart contracts are used for storing the transaction information and for the creation of the prosumer groups. SynergyChain integrates a reinforcement learning module to further improve the overall system performance and profitability by creating a self-adaptive grouping technique. The proposed SynergyChain is developed using Python and Solidity and has been tested using Ethereum test nets. The comprehensive analysis using the hourly energy consumption dataset shows a 39.7% improvement in the performance and scalability of the system as compared to the centralized systems. The evaluation results confirm that SynergyChain can reduce the request completion time along with an 18.3% improvement in the overall profitability of the system as compared to its counterparts.

The Impact of Social Media Marketing Communications on Consumer Response During the COVID-19: Does the Brand Equity of a University Matter?

Abstract: The main objective of the current study is to investigate the impact of social media marketing communication on the consumer response to University in UAE during COVID-19. In addition to that, the study has also examined the mediating role of brand equity of university in the relationship between social media marketing communication and consumer response during the COVID-19. The schedule for the academic session of an academic year, their starting and ending dates, schedule for vacations all vary from country to country; as a result, the conditions were not similar and varied due to the same reason. However, few countries suspended their classic room lectures from March or April 2020 up to further notification. We employed a combination of inferential and descriptive analyses to carry out the data analysis. For this purpose, the PLS-SEM approach was integrated, which is a second-generation technique for structural equation modeling. PLS-SEM is a relatively new approach and provides reliable results when coupled with SEM models. The response rate is above 50%. Customer-Based Brand Equity illustrated the significant indirect impacts of these significant indirect effects on the association among Customer-Based Brand Equity (CBBE) and social media marketing communications. As a result, this research further added the perception of Customer-Based Brand Equity (CBBE) in the perception of UNIVERSITY perception through endorsing the Customer-Based Brand Equity (CBBE), which estimates the brand sustainability, explicit consumers associations, functional brand image, perception, hedonic brand image and experiences with UNIVERSITY brands by brand awareness. This research work also positioned the two levels of social media communication, such as UGC and FCC, into marketing communication through endorsing the degrees of social media advertising, social media promotions, and social media interactive marketing. Hence, the research was capable of discussing the various roles of these social media marketing communications on the consumer response and the growth of Customer-Based Brand Equity (CBBE). Moreover, Customer-Based Brand Equity was illustrated to have significant influences on consumer responses.

Emotional development in preschoolers and socialization

Abstract: This work aims to determine the relationship between the emotional development variables and later adaptation in society for preschoolers living in Omdurman, Sudan. The secondary objective is to st...

Distance learning during COVID-19 pandemic: satisfaction, opportunities and challenges as perceived by faculty members and students

Abstract: Purpose: This study aims to investigate and assess the first experience of faculty members and students with distance learning implemented at Al Ain University (AAU) to contain the spread of Coronavirus or COVID-19 The paper attempted to understand faculty and students’ satisfaction with institutional readiness for distance learning and perception towards opportunities and challenges of distance learning Design/methodology/approach: The study is based on data collected in March 2020 through an online survey questionnaire from the participants (students = 445, faculty members = 139) The unified theory of acceptance and use of technology (UTAUT) was used in formulating a conceptual framework The collected data were analysed using several statistical techniques and partial least square structural equation modelling, to test and verify hypotheses Findings: The study found that, although faculty members and students expressed high satisfaction with the institutional readiness for distance learning and believed in its opportunities and advantages, they expressed concerns about the challenges facing distance learning Findings of the study indicated a relationship between the status or college of the participant and perceived opportunities and advantages of distance learning Hypotheses testing supported the study framework and UTAUT theory by identifying and confirming the impact of perceived opportunities of distance learning on satisfaction with the institutional readiness for distance learning Originality/value: The study suggested that non-distance learning institutions should keep offering courses through distance learning to prevent any shortcomings in the future © 2020, Emerald Publishing Limited

How the Business Intelligence in the New Startup Performance in UAE During COVID-19: The Mediating Role of Innovativeness

Abstract: The current study aims at empirically investigating the impact of the business intelligence on the new start-up performance of UAE during Covid-19. The study also examines the mediating role of innovativeness in the relationship between business intelligence and new start-up performance in UAE. The sample size included distributing the questionnaires to 250 respondents to get the required information for further analyses. 210 questionnaires out of 250 were received, so the response rate of the study was 84%. The data analysis involved the path modeling technique because of the explorative nature of the study. The results indicated that all the paths are significant at a p-value of less than 0.05. The findings of the study will be helpful for policymakers and researchers in formulating the policy concerning the business intelligence, innovation, and start-up performance in UAE.

Do renewable energy sources improve clean environmental-economic growth? Empirical investigation from South Asian economies:

Abstract: This study investigates the causal relationship between renewable energy sources and clean environmental economic growth among South Asian economies. This study comprises the panel data sets for ei...

Biotelemetry and Wireless Powering of Biomedical Implants Using a Rectifier Integrated Self-Diplexing Implantable Antenna

Abstract: This article proposes an efficient and complete wireless power transfer (WPT) system (WPTS) for multipurpose biomedical implants The WPTS is composed of a self-diplexing implantable antenna, efficient rectifier, and WPT transmitter (WPT Tx) The proposed system is capable of simultaneously transmitting recorded data and recharging the batteries of the devices (so as to elongate the implant life) The WPT Tx occupies dimensions of $50 \times 50 \times 16$ mm3 and is optimized to effectively transfer power at 1470 MHz to a 55-mm deep implantable device An efficient and compact ( $34 \times 67$ mm2) rectifier is used at 1470 MHz to convert the harvested RF power into a useful direct current (dc) power The proposed rectifier circuit exhibits a high conversion efficiency of 50% even at an input power of −14 dBm and maximum efficiency of 761% at 2 dBm The proposed self-diplexing implantable antenna occupies small dimensions (94 mm3) and operates at 915 and 1470 MHz by exciting ports 1 and 2, respectively The biotelemetry operation is performed using a 915 MHz band (port 1), and the rectifier circuit is connected to port 2 (1470 MHz) to perform wireless powering The simulated results are validated by examining the individual elements (WPT Tx, rectifier, and self-diplexing antenna) and overall WPTS in a saline solution and minced pork The results prove that the proposed scheme is suitable for biotelemetry and wireless powering of biomedical implants

E-proctored exams during the COVID-19 pandemic: A close understanding.

Abstract: Researchers have focused on evaluating and exploring the online examination experience during the COVID-19 pandemic. However, understanding the perceptions of using an e-proctoring tool within the online examination experience is still limited. This study explores the first unique experience for students’ attitudes and concerns using an e-proctoring tool in their final exams during the COVID-19 pandemic. It also highlights the e-tools’ impact on students’ performances to guide educational institutions towards appropriate practices going forward, especially as the pandemic is expected to have far-reaching consequences. A mixed-methods analysis was used to examine heterogeneous sources of data including self-reported data and officially documented data. The data was analyzed by a qualitative analysis of the focus group and quantitative analyses of the survey questions and exam attempts. In June 2020, students participated in a focus group to elaborate on their attitudes and concerns pertaining to their e-proctoring experience. Based on the preliminary outcomes, a survey was developed and distributed to a purposive sample (n = 106) of students from information technology majors who had taken at least one e-proctored exam during the COVID-19 pandemic. Finally, 21 online exams with 815 total attempts were analyzed to assess how well students performed under an e-proctored test. The study’s findings shed light on students’ perceptions of their e-proctoring experience, including their predominant concerns over privacy and various environmental and psychological factors. The research also highlights challenges in implementing the e-proctoring tool as well as its impact on students’ performance.

The effects of subjective norm on the intention to use social media networks : an exploratory study using PLS-SEM and machine learning approach

Abstract: Several research has been conducted on social media application's acceptance, but factors that impact educational purposes are completely ignored in this research. Therefore, the research has been conducted with the purpose of developing a conceptual model, which is derived from the Technology Acceptance Model (TAM). The subjective norm of the study is to find out social media's acceptance in education by students. To find out the exact conclusion, the research follows the questionnaire survey method in which 310 questionnaires were distributed to the students of the United Arab Emirates' well-reputed university. In this questionnaire survey, two famous approaches were used to examine the collected data that is the partial least squares-structural equation modeling (PLS-SEM) and Machine Learning approach (ML). From the above-stated study, it has been observed that perceived usefulness, subjective norms, and perceived ease of use are proven to be significant measures of student's intention that motivates them to use social media networks for their educational purpose.

Investigation of Fractal-Fractional Order Model of COVID-19 in Pakistan under Atangana-Baleanu Caputo (ABC) derivative

Abstract: This manuscript addressing the dynamics of fractal-fractional type modified SEIR model under Atangana-Baleanu Caputo (ABC) derivative of fractional order y and fractal dimension p for the available data in Pakistan. The proposed model has been investigated for qualitative analysis by applying the theory of non-linear functional analysis along with fixed point theory. The fractional Adams–bashforth iterative techniques have been applied for the numerical solution of the said model. The Ulam-Hyers (UH) stability techniques have been derived for the stability of the considered model. The simulation of all compartments has been drawn against the available data of covid-19 in Pakistan. The whole study of this manuscript illustrates that control of the effective transmission rate is necessary for stoping the transmission of the outbreak. This means that everyone in the society must change their behavior towards self-protection by keeping most of the precautionary measures sufficient for controlling covid-19.

Evaluation of Telepharmacy Services in Light of COVID-19.

Abstract: Background: Telepharmacy services are expected to have an important role in increasing access of patients to pharmaceutical care and reducing potential dispensing errors in community pharmacies. Objective: To assess the predictors for effective telepharmacy services on increasing access of patients to care and reducing dispensing errors in community pharmacies. Method: This is a prospective study carried out for 4 months in 52 community pharmacies across the United Arab Emirates (UAE) using disguised direct observation. Multivariable logistic regression was used as a tool to predict factors associated with effective telepharmacy services in improving dispensing safety and increasing access of patients to pharmaceutical care. Data were entered and analyzed using the Statistical Package for Social Science (SPSS) software version 26. Results: Pharmacist recommendations related to COVID-19 at pharmacies with telepharmacy (n = 63,714) versus those without remote services (n = 15,539) were significantly more likely to be (1) contact the nearest testing center (adjusted odds ratio [AOR] = 7.93), (2) maintain home quarantine (AOR = 5.64), and (3) take paracetamol for fever (AOR = 3.53), all were significant results (p 0.05), respectively. However, pharmacies with telepharmacy were more likely to include wrong patient errors (AOR = 5.38, p < 0.05). Conclusions: Telepharmacy can be used as a tool to reduce the burden on the health care system and improve drug dispensing safety in community pharmacies.

Gender Discrimination at Workplace: Do Artificial Intelligence (AI) and Machine Learning (ML) Have Opinions About It

Abstract: The gender discrimination problem started from day one when they entered professional offices, factories, businesses, institutions, and other organizations. Despite strict regulations and laws, gender-based discrimination can be seen in almost all working places. However, its types and gravity may change with the place, sectors, or development level of a country. The complaints and protests of affected women roar severely about humanity’s failure to solve this alarming problem consistently. This issue may remain invisible or limelight through discriminated actions during job recruitment, assigning duties, salary packages, benefits, performance assessments, promotions, communications, behavior, trusts, and responsibilities. The women promoted to managerial level always remained lesser in number. Even the fellow women at senior positions discriminate their gender. This reveals the peak of the problem. Therefore, women’s discrimination is a prioritized issue that concerted efforts of all stakeholders must tackle. Also, the link between Artificial Intelligence, Machine Learning, and Gender discrimination has been discussed in this paper. Finally, a set of gender discrimination mitigating strategies has been suggested in this article.

The Impact of Game-Based Learning (GBL) on Students' Motivation, Engagement and Academic Performance on an Arabic Language Grammar Course in Higher Education.

Abstract: The objective of this study was to evaluate the impact of game-based learning (GBL) on students’ motivation, engagement and academic performance on an Arabic language grammar course at Ajman University. The study was carried out utilizing a case study of quasi-empirical design. The respondents were 107 learners, grouped into two groups: one empiric group (n = 54) that used the game-based classroom response system; and the other a control group (n = 53) which was instructed using non-game-based methods. The game-based online assessment tool Kahoot! was used as a formative assessment method in the lectures, and a questionnaire was designed to measure motivation and engagement. The findings indicated that there were statistical differences in the benefit of the empiric group between the empiric and the control groups between the groups. Empirical group students showed more improved knowledge of the concepts taught on the Arabic language grammar course, and higher motivation than the students taught using the traditional strategy.

Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

Abstract: Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher’s Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.