Showing papers by "Birla Institute of Technology, Mesra published in 2021"

Unification of Blockchain and Internet of Things (BIoT): requirements, working model, challenges and future directions

Abstract: The Internet of Things (IoTs) enables coupling of digital and physical objects using worthy communication technologies and introduces a future vision where computing systems, users and objects cooperate for convenience and economic benefits. Such a vision requires seamless security, data privacy, authentication and robustness against attacks. These attributes can be introduced by blockchain, a distributed ledger that maintains an immutable log of network transactions. In this paper, we present a comprehensive review on how to remodel blockchain to the specific IoT needs in order to develop Blockchain based IoT (BIoT) applications and aim to shape a coherent picture of the current state-of-the-art efforts in this direction. After describing the basic characteristics and requirements of IoT, evolution of blockchain is presented. In this regard, we start with the fundamental working principles of blockchain and how such systems achieve auditability, security and decentralization. Further, we describe the most relevant BIoT applications, its architecture design and security aspects. From there, we build our narrative on the centralized IoT challenges followed by recent advances towards solving them. Finally, some future directions are enumerated with the aim to guide future BIoT researchers on challenges that needs to be considered ahead of deploying the next generation of BIoT applications.

A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques

Abstract: Stress is an escalated psycho-physiological state of the human body emerging in response to a challenging event or a demanding condition. Environmental factors that trigger stress are called stressors. In case of prolonged exposure to multiple stressors impacting simultaneously, a person’s mental and physical health can be adversely affected which can further lead to chronic health issues. To prevent stress-related issues, it is necessary to detect them in the nascent stages which are possible only by continuous monitoring of stress. Wearable devices promise real-time and continuous data collection, which helps in personal stress monitoring. In this paper, a comprehensive review has been presented, which focuses on stress detection using wearable sensors and applied machine learning techniques. This paper investigates the stress detection approaches adopted in accordance with the sensory devices such as wearable sensors, Electrocardiogram (ECG), Electroencephalography (EEG), and Photoplethysmography (PPG), and also depending on various environments like during driving, studying, and working. The stressors, techniques, results, advantages, limitations, and issues for each study are highlighted and expected to provide a path for future research studies. Also, a multimodal stress detection system using a wearable sensor-based deep learning technique has been proposed at the end.

Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System

Abstract: Artificial Intelligence (AI) is widely implemented in healthcare 4.0 for producing early and accurate results. The early predictions of disease help doctors to make early decisions to save the life of patients. Internet of things (IoT) is working as a catalyst to enhance the power of AI applications in healthcare. The patients' data are captured by IoT_sensor and analysis of the patient data is performed by machine learning techniques. The main aim of the work is to propose a Machine learning-based healthcare model to early and accurately predict the different diseases. In this work, seven machine learning classification algorithms such as decision tree, support vector machine, Naive Bayes, adaptive boosting, Random Forest (RF), artificial neural network, and K-nearest neighbor are used to predict the nine fatal diseases such as heart disease, diabetics breast cancer, hepatitis, liver disorder, dermatology, surgery data, thyroid, and spect heart. To evaluate the performance of the proposed model, four performance metrics (such as accuracy, sensitivity, specificity, and area under the curve) are used. The RF classifier observes the maximum accuracy of 97.62%, the sensitivity of 99.67%, specificity of 97.81%, and AUC of 99.32% for different diseases. The developed healthcare model will help doctors to diagnose the disease early.

Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection

Abstract: The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

Reinforcement learning for medical information processing over heterogeneous networks

Abstract: Fog computing is an emerging trend in the healthcare sector for the care of patients in emergencies. Fog computing provides better results in healthcare by improving the quality of services in the heterogeneous network. The transmission of critical multimedia healthcare data is required to be transferred in real-time for saving the lives of patients using better quality networks. The main objective is to improve the quality of service over a heterogeneous network by reinforcement learning-based multimedia data segregation (RLMDS) algorithm and Computing QoS in Medical Information system using Fuzzy (CQMISF) algorithm in fog computing. The proposed algorithms works in three phase’s such as classification of healthcare data, selection of optimal gateways for data transmission and improving the transmission quality with the consideration of parameters such as throughput, end-to-end delay and jitter. Proposed algorithms used to classify the healthcare data and transfer the classified high-risk data to end-user with by selecting the optimal gateway. To performance validation, extensive simulations were conducted on MATLAB R2018b on different parameters like throughput, end-to-end delay, and jitter. The performance of the proposed work is compared with FLQoS and AQCA algorithms. The proposed CQMISF algorithm achieves 81.7% overall accuracy and in comparison to FLQoS and AQCA algorithm, the proposed algorithms achieves the significant improvement of 6.195% and 2.01%.

Task Offloading in Fog Computing for Using Smart Ant Colony Optimization

Abstract: In the current scenario, Cloud computing is providing services to IoT-sensor based applications in task offloading. In time-sensitive real-time applications, latency is a major problem in cloud computing. Due to exponential growth in IoT-sensor applications huge amount of multimedia data is produced and only the use of cloud computing decreases the efficiency of quality of service (QoS) in IoT-sensor applications. Fog computing uses to resolve the aforementioned issues in cloud computing. Fog computing accomplishes the low-latency requirement of QoS in time-sensitive real-time IoT-sensor applications. Thus the tasks of IoT-sensor applications are computed by various fog nodes. In this paper, a meta-heuristic scheduler Smart Ant Colony Optimization (SACO) task offloading algorithm inspired by nature is proposed to offload the IoT-sensor applications tasks in a fog environment. The proposed algorithm results are compared with Round Robin (RR), throttled scheduler algorithm and two bio-inspired algorithms such as modified particle swarm optimization (MPSO) and Bee life algorithm (BLA). Numerical result shows the significant improvement in latency by the proposed Smart Ant Colony Optimization (SACO) algorithm in task offloading of IoT-sensor applications comparison to Round Robin (RR), throttled, and MPSO and BLA. Proposed technique reduces the task offloading time by 12.88, 6.98, 5.91 and 3.53% in comparison to Round Robin (RR), throttled, MPSO, and BLA.

ElStream: An Ensemble Learning Approach for Concept Drift Detection in Dynamic Social Big Data Stream Learning

Abstract: With the rapid increase in communication technologies and smart devices, an enormous surge in data traffic has been observed. A huge amount of data gets generated every second by different applications, users, and devices. This rapid generation of data has created the need for solutions to analyze the change in data over time in unforeseen ways despite resource constraints. These unforeseeable changes in the underlying distribution of streaming data over time are identified as concept drifts. This paper presents a novel approach named ElStream that detects concept drift using ensemble and conventional machine learning techniques using both real and artificial data. ElStream utilizes the majority voting technique making only optimum classifier to vote for decision. Experiments were conducted to evaluate the performance of the proposed approach. According to experimental analysis, the ensemble learning approach provides a consistent performance for both artificial and real-world data sets. Experiments prove that the ElStream provides better accuracy of 12.49%, 11.98%, 10.06%, 1.2%, and 0.33% for PokerHand, LED, Random RBF, Electricity, and SEA dataset respectively, which is better as compared to previous state-of-the-art studies and conventional machine learning algorithms.

Stable silver nanoparticle doped mesoporous biochar-based nanocomposite for efficient removal of toxic dyes

Abstract: In this present study, biochar-based silver nanocomposite (Ag-nBC) has been synthesized by combining (i) AgNPs, synthesized biochemically using Shorea robusta leaf extract, and (ii) S. robusta leaf biochar, produced via mild thermal pyrolysis (300 °C) and tested for toxic dye removal efficiency. The Ag-nBC was characterized by FESEM-EDX, BET, XRD, XPS, TGA, and FTIR, found to be irregular, heterogeneous, and mesoporous, containing a significant amount of surface functional groups. The Ag-nBC showed > 90% removal for Congo red (CR) and Rhodamine B (RhB), where the dye removal experiments were found to be spontaneous exothermic, followed Freundlich isotherm and pseudo-second order reaction kinetic model. The adsorption mechanism of CR involved surface complexation through specific electrostatic attraction and H-bonding, while RhB exhibited only surface complexation. Notably, this heterogeneous Ag-nBC system showed excellent stability at wide ranges of pH and promising reusability, suggesting great potential for waste biomass minimization and dye effluent treatment.

SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

Abstract: Internet of Things (IoT) is a domain where the transfer of big data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality, and payload capacity in comparison with the existing state of art methods.

UIDS: a unified intrusion detection system for IoT environment

Abstract: Intrusion detection system (IDS) using machine learning approach is getting popularity as it has an advantage of getting updated by itself to defend against any new type of attack. Another emerging technology, called internet of things (IoT) is taking the responsibility to make automated system by communicating the devices without human intervention. In IoT based systems, the wireless communication between several devices through the internet causes vulnerability for different security threats. This paper proposes a novel unified intrusion detection system for IoT environment (UIDS) to defend the network from four types of attacks such as: exploit, DoS, probe, and generic. The system is also able to detect normal category of network traffic. Most of the related works on IDS are based on KDD99 or NSL-KDD 99 data sets which are unable to detect new type of attacks. In this paper, UNSW-NB15 data set is considered as the benchmark dataset to design UIDS for detecting malicious activities in the network. The performance analysis proves that the attack detection rate of the proposed model is higher compared to two existing approaches ENADS and DENDRON which also worked on UNSW-NB15 data set.

The multidimensional role of the Wnt/β-catenin signaling pathway in human malignancies.

Abstract: Several signaling pathways have been identified as important for developmental processes. One of such important cascades is the Wnt/β-catenin signaling pathway, which can regulate various physiological processes such as embryonic development, tissue homeostasis, and tissue regeneration; while its dysregulation is implicated in several pathological conditions especially cancers. Interestingly, deregulation of the Wnt/β-catenin pathway has been reported to be closely associated with initiation, progression, metastasis, maintenance of cancer stem cells, and drug resistance in human malignancies. Moreover, several genetic and experimental models support the inhibition of the Wnt/β-catenin pathway to answer the key issues related to cancer development. The present review focuses on different regulators of Wnt pathway and how distinct mutations, deletion, and amplification in these regulators could possibly play an essential role in the development of several cancers such as colorectal, melanoma, breast, lung, and leukemia. Additionally, we also provide insights on diverse classes of inhibitors of the Wnt/β-catenin pathway, which are currently in preclinical and clinical trial against different cancers.

Green banking initiatives: a qualitative study on Indian banking sector.

Abstract: The environmental concern is on rise in all types of business; however, banking assumes a special niche due to its ability to influence the economic growth and development of the country. The present study proposes conceptual model of Green banking initiatives and studies the impact of three Green banking initiatives, viz. green products development, green corporate social responsibility and green internal process on two possible outcomes, viz. Green brand image and Green trust. The study is qualitative in nature comprising of semistructured in-depth interviews conducted with 36 middle- to senior-level managers of twelve public and private Indian banks. Banking sector can play a crucial role in greening the banking system by enhancing the availability of finance and serve the needs of a “green economy”. The findings of the study revealed that 63% of the total respondents were of view that their bank indulges in development of several green banking products and services, 53% of the bankers said that their bank incorporates green internal processes in their daily activities, and 78% respondents said that their bank undertakes several green corporate social responsibility initiatives. This investigation further highlights that more than 60% respondents believed that Green banking initiatives have positive role in restoring customer trust through enhanced Green brand image. With dearth of studies on green banking in India, the present qualitative study contributes to the body of knowledge and paves way for future research in green banking for sustainable development.

SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic.

Abstract: The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network.

Abstract: COVID-19 is a novel virus that affects the upper respiratory tract, as well as the lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are increasing regularly. Computed tomography (CT) scans can be used carefully to detect and analyze COVID-19 cases. In CT images/scans, ground-glass opacity (GGO) is found in the early stages of infection. While in later stages, there is a superimposed pulmonary consolidation. This research investigates the quantum machine learning (QML) and classical machine learning (CML) approaches for the analysis of COVID-19 images. The recent developments in quantum computing have led researchers to explore new ideas and approaches using QML. The proposed approach consists of two phases: in phase I, synthetic CT images are generated through the conditional adversarial network (CGAN) to increase the size of the dataset for accurate training and testing. In phase II, the classification of COVID-19/healthy images is performed, in which two models are proposed: CML and QML. The proposed model achieved 0.94 precision (Pn), 0.94 accuracy (Ac), 0.94 recall (Rl), and 0.94 F1-score (Fe) on POF Hospital dataset while 0.96 Pn, 0.96 Ac, 0.95 Rl, and 0.96 Fe on UCSD-AI4H dataset. The proposed method achieved better results when compared to the latest published work in this domain.

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach.

Abstract: The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-a-vis other states in India. The study uses Pearson's correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

Experimental investigation and study of HVOF sprayed WC-12Co, WC-10Co-4Cr and Cr3C2-25NiCr coating on its sliding wear behaviour

Abstract: An experimental investigation followed by a comparative study of HVOF sprayed WC-12Co, WC- 10Co 4Cr and Cr3C2-25NiCr coatings were conducted at three different loads of 20, 40 and 60 N. Sliding wear of coated specimens against hardened EN-32 disc was performed as per the G 99–5 standard at room temperature. The feedstock powders and corresponding coatings were characterized for microstructural studies along with porosity, microhardness, and adhesive bond strength. The experimental results suggest that the stability of the transfer layer plays a significant role in stable wear. The WC-12Co coating shows the best sliding wear resistance, maximum microhardness which was found to be 56.6% and 9.6% higher than WC-10Co-4Cr and Cr3C2-25NiCr coatings. The maximum adhesive bond strength was found to be 2.03% and 10.5% higher in WC-12Co than WC-10Co-4Cr and Cr3C2-25NiCr. Tribo oxide layer counterbalances to stabilize the wear during high heat generation at higher loads. Various aspects and mechanisms of these improvements are discussed in this paper.

BIFM: Big-Data Driven Intelligent Forecasting Model for COVID-19

Abstract: Ever since the pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China, it has been recognized as a global threat and several studies have been carried out nationally and globally to predict the outbreak with varying levels of dependability and accuracy. Also, the mobility restrictions have had a widespread impact on people’s behavior such as fear of using public transportation (traveling with unknown passengers in the closed area). Securing an appropriate level of safety during the pandemic situation is a highly problematic issue that resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The autoregressive integrated moving average (ARIMA) machine learning model is used to develop the best model for twenty-one worst-affected states of India and six worst-hit countries of the world including India. The best ARIMA models are used for predicting the daily-confirmed cases for 90 days future values of six worst-hit countries of the world and six high incidence states of India. The goodness-of-fit measures for the model achieved 85% MAPE for all the countries and all states of India. The above computational analysis will be able to throw some light on the planning and management of healthcare systems and infrastructure.

TLCoV- An automated Covid-19 screening model using Transfer Learning from Chest X-ray images

Abstract: The Coronavirus disease (Covid-19) has been declared a pandemic by World Health Organisation (WHO) and till date caused 585,727 numbers of deaths all over the world. The only way to minimize the number of death is to quarantine the patients tested Corona positive. The quick spread of this disease can be reduced by automatic screening to cover the lack of radiologists. Though the researchers already have done extremely well to design pioneering deep learning models for the screening of Covid-19, most of them results in low accuracy rate. In addition, over-fitting problem increases difficulties for those models to learn on existing Covid-19 datasets. In this paper, an automated Covid-19 screening model is designed to identify the patients suffering from this disease by using their chest X-ray images. The model classifies the images in three categories - Covid-19 positive, other pneumonia infection and no infection. Three learning schemes such as CNN, VGG-16 and ResNet-50 are separately used to learn the model. A standard Covid-19 radiography dataset from the repository of Kaggle is used to get the chest X-ray images. The performance of the model with all the three learning schemes has been evaluated and it shows VGG-16 performed better as compared to CNN and ResNet-50. The model with VGG-16 gives the accuracy of 97.67%, precision of 96.65%, recall of 96.54% and F1 score of 96.59%. The performance evaluation also shows that our model outperforms two existing models to screen the Covid-19.

Removal of Arsenic by Wheat Straw Biochar from Soil.

Abstract: Biochar prepared from wheat straw (Triticum aestivum) at different pyrolysis temperatures was screened, followed by its application to soil for arsenic removal in the present study. Characterization of biochar by Field emission scanning electron microscope studies and Fourier thermal Infrared imaging showed smooth and porous biochar surface and abundance of surface functional groups. A low value of H/C was obtained by CHNS analyzer, indicating high stability of biochar. The surface area was 15.86 m2/g on an average. Batch sorption experiments were carried out to optimize conditions for arsenic sorption. Maximum arsenic removal of 83.7% was obtained when applied at a 7.5% dose for a contact time of 60 min at 25 °C. Isotherm, kinetic and thermodynamic studies revealed the feasibility of sorption and removal of arsenic through physisorption, chemisorption, ion exchange, and diffusion.