Real-time Scenarios Research Articles

Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective

Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios.

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) allows the complementary study of the brain's electrophysiology and hemodynamics with high temporal and spatial resolution. One application with great potential is neurofeedback training of targeted brain activity, based on the real-time analysis of the EEG and/or fMRI signals. This depends on the ability to reduce in real time the severe artifacts affecting the EEG signal acquired with fMRI, mainly the gradient and pulse artifacts. A few methods have been proposed for this purpose, but they are either slow, hardware-dependent, publicly unavailable, or proprietary software. Here, we present a fully open-source and publicly available tool for real-time EEG artifact reduction in simultaneous EEG-fMRI recordings that is fast and applicable to any hardware. Our tool is integrated in the Python toolbox NeuXus for real-time EEG processing and adapts to a real-time scenario well-established artifact average subtraction methods combined with a long short-term memory network for R peak detection. We benchmarked NeuXus on three different datasets, in terms of artifact power reduction and background signal preservation in resting state, alpha-band power reactivity to eyes closure, and event-related desynchronization during motor imagery. We showed that NeuXus performed at least as well as the only available real-time tool for conventional hardware setups (BrainVision's RecView) and a well-established offline tool (EEGLAB's FMRIB plugin). We also demonstrated NeuXus’ real-time ability by reporting execution times under 250 ms. In conclusion, we present and validate the first fully open-source and hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI studies.

SCHEISB: Design of a high efficiency IoMT security model based on sharded chains using bio-inspired optimizations

Securing Internet-of-Medical Things (IoMT) devices is a multidomain task that involves analysis of attack patterns from data & control packets. Existing security models either work on pre-emptive analysis, real-time analysis or post-processing analysis of different packet types. These operations add to the complexity of data processing, which reduces Quality of Service (QoS) levels of IoMT deployments. To overcome these issues, this paper proposes design of a sharded chain based high efficiency IoMT security model that uses bioinspired optimizations. The proposed model initially deploys a novel single chained Proof-of-Medical-Trust (PoMT) based consensus technique, which assists in identification of high-trust miners. The QoS levels are continuously monitored for each block request, and a Teacher–Learner based Optimization (TLbO) model is deployed to make sharding decisions. These decisions are reconfirmed by a Particle Swarm Optimization (PSO) model, which assists in estimation of shard length under different attack types. These attacks include Spoofing, Spying, and Flooding attacks, which affects the authenticity of data samples from IoT devices under real-time scenarios. The PSO Model also recommends splitting & merging decisions based on the context of IoMT deployments. The PoMT consensus is modified to support sharded chains, and assists in reducing mining delays without compromising miner trust levels. The modified PoMT consensus is capable of selecting overlapping miner nodes, while incorporating load balancing for optimal energy consumption during the mining process. Due to these enhancements, the proposed model is able to reduce mining delay by 8.5%, while reducing energy consumption by 3.2%, and reducing access delay by 4.9% under real-time clinical scenarios.

FastAiAlloc: A real-time multi-resources allocation framework proposal based on predictive model and multiple optimization strategies

In cloud platforms, a common task is to allocate computational resources, e.g., memory and CPU, requested by applications and users. The allocation of these resources, which is an optimal load-balancing task, is considered an NP-Hard problem, being a challenging research area. There are many works proposed in the literature to address this problem. They use several strategies to deal with this optimization problem such as evolutionary algorithms, exact programming and also heuristics. However, some steps of the allocation process are not considered by these works, which are sometimes treated separately and not in an integrated manner. These steps include the applications and users resource consumption request profile as part of the optimization process and defining adequate metrics to check the optimized allocation. To integrate these steps, this work proposes a framework based on the following strategies, widely used in the literature: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Linear Programming, besides our Heuristic approach. Furthermore, we restricted the resource allocation optimization to a real-time scenario, facilitating its use in an industrial process. In addition, Key Performance Indicators (KPIs) are proposed to carry out a comparative study in a public dataset. Our experiments showed that our proposed framework achieved better results than the baseline one, e.g., using a random allocation. In some cases, we observed an increase of almost 60% in performance compared with the baseline. In addition, when trying to balance memory and CPU consumption in the cluster, the linear approach performed the best, while the GA achieved the best result in allocating different user profiles across the cluster.

Application of Flexible Tools in Magnesia Sector: The Case of Grecian Magnesite

In this paper, two flexible model tools (CO2 emissions/cost tool and CFD tool) that simulate the production process of Grecian Magnesite (GM) and extract economic and technical conclusions regarding the substitution of fossil fuels with various types of biomass are presented and analyzed. According to the analysis, the higher the substitution, the higher the profit in both CO2 emissions and cost reduction. The reduction in CO2 emissions that can be achieved through biomass fuel substitution ranges from 15% for a 30% substitution to 35% for a 70% substitution. Accordingly, production costs are also reduced with the use of biomass. The initial results of this decision-making cost tool showed that the most profitable solution is a 70% substitution, for which production costs can be reduced by up to 38.7%, while the most beneficial type of biomass proved to be the olive kernel. A proposed and feasible solution is the substitution of 50% sunflower husk pellets, which will result in a reduction of 25% in CO2 emissions and almost 10% in production cost. From CFD simulation, a reduced order model (ROM) has been developed that allows the running of scenarios in real time, instead of the usual long times required by complex simulations. Comparative studies of fuel blend and biomass type can be carried out easily and rapidly, allowing one to choose the most suitable substitution.

Anomaly Pixel Detection via Dual-Branch Uncertainty Metrics

Anomaly detection aims to detect abnormal samples from normal data. Existing methods mainly use synthesis-based approach and uncertainty-based approach. Moreover, the above-mentioned methods need to re-train part/all of the deep network, which limits its application scenarios especially real-time scenarios. We propose a novel retraining-free anomaly detection method, which includes a dual-branch anomaly detection mechanism to detect abnormal pixels from two views. In the pseudo-supervised branch, we use the Copy post method to transform the unsupervised anomaly detection task into a supervised binary detection task. In the prototype detection branch, we use prototypes to improve the accuracy of abnormal pixel detection. The anomaly results of the two branches are fused by means of soft voting to produce anomaly detection results with higher recall. Our experiments show that our method significantly outperforms other methods and achieves excellent results on different datasets, which also shows that our method has good generalization ability. Therefore, our proposed method can effectively improve the effectiveness and safety of anomaly detection in real-time scenarios, such as autonomous driving.

Gas phase multicomponent detection and analysis combining broadband dual-frequency comb absorption spectroscopy and deep learning

In absorption spectroscopy, analysis of multicomponent gas mixtures becomes challenging when absorption features overlap (blended spectra). Here we propose a gas sensor which can accurately identify the species and retrieve the concentrations of components in a gaseous mixture in a broad spectrum. The sensor integrates a mid-infrared dual-frequency comb laser source for spectrum acquisition and a deep learning algorithm for spectral analysis. The sensor was tested on gas phase mixtures of methane, acetone and water vapor. A prototype sensor was assessed in realistic scenarios in real time. We also systematically analyzed and presented explicit visualizations to explain the underlying working mechanism of the algorithms.

Robust federated deep reinforcement learning for optimal control in multiple virtual power plants with electric vehicles

The deployment of virtual power plants (VPPs) with electric vehicles (EVs) is crucial for the successful integration of renewable energy sources and efficient management of EV charging and discharging while maintaining sustainability and cost-effectiveness. Deep reinforcement learning (DRL) is a highly promising method that uses historical data to learn optimal control strategies and adapts to a wide range of real-time scenarios. To address data privacy concerns in VPPs, federated DRL, which trains models across multiple VPPs, is necessary. However, existing federated DRL methods are prone to disturbance, which can severely impact system performance. This paper proposes a robust federated DRL method to ensure the robustness and reliability of VPP control strategies. Firstly, we formulate the control strategies of multiple VPPs as a Markov decision process that takes into account disturbances, aiming to achieve self-balance as much as possible. Secondly, we employ the stochastically controlled stochastic gradient method to increase training speed. Additionally, we introduce the robust gradient filter to develop a robust federated DRL method based on policy-based DRL. Finally, we validate the effectiveness and robustness of the proposed robust federated DRL method, which maintains balance in internal VPP power.

A Deep Learning based Scalable and Adaptive Feature Extraction Framework for Medical Images

Features extraction has a fundamental value in enhancing the scalability and adaptability n of medical image processing framework. The outcome of this stage has a tremendous effect on the reliability of the medical application being developed, particularly disease classification and prediction. The challenging side of features extraction frameworks, in relation to medical images, is influenced by the anatomical and morphological structure of the image which requires a powerful extraction system that highlights high- and low- level features. The complementary of both feature types reinforces the medical image content-based retrieval and allows to access visible structures as well as an in-depth understanding of related deep hidden components. Several existing techniques have been used towards extracting high- and low-level features separately, including Deep Learning based approaches. However, the fusion of these features remains a challenging task. Towards tackling the drawback caused by the lack of features combination and enhancing the reliability of features extraction methods, this paper proposes a new hybrid features extraction framework that focuses on the fusion and optimal selection of high- and low-level features. The scalability and reliability of the proposed method is achieved by the automated adjustment of the final optimal features based on real-time scenarios resulting an accurate and efficient medical images disease classification. The proposed framework has been tested on two different datasets to include BraTS and Retinal sets achieving an accuracy rate of 97% and 98.9%, respectively.

ProvLink-IoT: A novel provenance model for Link-Layer Forensics in IoT networks

Provenance has been instrumental in networked systems to solve issues related to data trustworthiness, network diagnostics, security, and forensic analysis. Existing provenance based solutions for the Internet of Things (IoT) are either device-centric or platform-centric, which address only application layer attacks. Whereas any stealthy attacks performed at MAC, network, and other sub-layers which alter the behavior of IoT devices often go undetected. Provenance based forensic solutions offer effective mechanisms for investigating link and network layer attacks in IoT networks. Realizing this potential, we propose ProvLink-IoT, a novel provenance model for link-layer forensic analysis in IoT networks. ProvLink-IoT employs PROV-DM and PROV-TEMPLATE standards to model the provenance of the network. Provenance graphs are generated under both benign and attack scenarios based on the provenance logs and network traffic collected from the network. ProvLink-IoT is implemented in a simulated environment with a case study on the 6TiSCH protocol stack. We implemented three link-layer attacks on TSCH and the 6top layers of the 6TiSCH network to study their impact and perform forensic analysis. Link-IoT, a comprehensive link-layer dataset, is generated from the network provenance, which can be used in the further incident and forensic analysis. The performance impact of ProvLink-IoT on IoT network is analyzed in terms of provenance growth rate and storage overhead. Experimental results showed the efficacy of the proposed solution in correlating evidence during incident analysis and its relevance to real-time scenarios.

Revolutionizing Small-Scale Retail: Introducing an Intelligent IoT-based Scale for Efficient Fruits and Vegetables Shops

In the bustling streets of Pakistan, small-scale fruits and vegetables shops stand as vital hubs of daily life. These humble establishments are where people flock to satisfy their everyday needs. However, the traditional methods employed by shopkeepers using manual weighing scales have proven to be time-consuming and limit the shopkeepers’ ability to serve multiple customers simultaneously. But in a world rapidly embracing automation and propelled by the wonders of artificial intelligence, a revolution is underway. In this visionary paper, we introduce the concept of an intelligent scale that will revolutionize the retail process. This remarkable scale possesses the power to automate numerous tasks, making the shopping experience seamless and efficient. Imagine a scale that not only determines the weight of the produce but also possesses the ability to detect and identify each fruit and vegetable placed upon it. By harnessing the potential of cutting-edge technology, we fine-tuned pre-trained models, such as YOLOv5n and YOLOv7, on our extensive dataset, consisting of 12 classes and 2622 images. The dataset was collected manually and it closely aligns with real-time scenarios, ensuring that the distribution in our training and validation sets were similar and that it reflected what our models will encounter during testing. As a result, our YOLOv5n and YOLOv7 models have achieved astonishing mean Average Precision (mAP) scores of 0.98 and 0.987, respectively. YOLOv5n demonstrates an impressive processing speed of 20 frames per second (fps) on a CPU, while it reaches an impressive 125 fps on a GPU. Similarly, YOLOv7 achieves a processing speed of 2 fps on a CPU, which escalates to 66.6 fps on a GPU. These extraordinary results testify to the remarkable accuracy and efficacy of our system when subjected to real-world testing scenarios. To ensure accurate weighing, we incorporated a load cell with an hx711 amplifier, providing precise measurements that customers can trust. However, our intelligent scale does not stop there. We understand that determining weight alone is insufficient when it comes to transactions. Hence, a meticulously crafted Python script was developed to map each specific item to its corresponding price based on its weight. With all these incredible features in place, the experience of purchasing from a fruits and vegetables shop is taken to new heights. The intelligent scale is accompanied by a user-friendly graphical user interface (GUI), where customers can conveniently view their order and prices. Once the order is complete, a simple click on the print button generates a neatly printed bill, ensuring a seamless transaction. The implications of this intelligent scale are profound. Shopkeepers can now serve customers faster and more efficiently, effortlessly managing multiple transactions simultaneously. The introduction of automation enhances the overall shopping experience, leaving customers delighted and eager to return. This amalgamation of technology and traditional commerce heralds a new era, where small-scale shops can thrive and adapt to the ever-evolving needs of the modern world.

A Fast Algorithm for Intra-Frame Versatile Video Coding Based on Edge Features.

Versatile Video Coding (VVC) introduces many new coding technologies, such as quadtree with nested multi-type tree (QTMT), which greatly improves the efficiency of VVC coding. However, its computational complexity is higher, which affects the application of VVC in real-time scenarios. Aiming to solve the problem of the high complexity of VVC intra coding, we propose a low-complexity partition algorithm based on edge features. Firstly, the Laplacian of Gaussian (LOG) operator was used to extract the edges in the coding frame, and the edges were divided into vertical and horizontal edges. Then, the coding unit (CU) was equally divided into four sub-blocks in the horizontal and vertical directions to calculate the feature values of the horizontal and vertical edges, respectively. Based on the feature values, we skipped unnecessary partition patterns in advance. Finally, for the CUs without edges, we decided to terminate the partition process according to the depth information of neighboring CUs. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 54.08% of the encoding time on average, and the BDBR (Bjøntegaard delta bit rate) only increases by 1.61%.

Path planning and collision avoidance based on the RRT*FN framework for a robotic manipulator in various scenarios

AbstractIn this article, we present a new path planning algorithm based on the rapidly exploring random tree-fixed node (RRT*FN) algorithm for manipulators. It addresses the problem that RRT*FN complex environment processing is not fast enough to meet real-time requirements, and RRT*FN is basically impossible to search for an effective manipulator path in a narrow-channel environment. In the new path planning algorithm, a heuristic sampling method is adopted and the leaf nodes outside the ellipsoids are preferentially deleted to address the node removal problem, resulting in better search paths and faster search speeds. When the nodes are expanded, new nodes are generated due to goal gravity and random point gravity, with the weight values dynamically adjusted via dichotomy, and the new nodes are expanded to the goal point twice to more rapidly obtain a tree extension direction that is closer to the goal point. For boundary points, the issues of narrow channels and stepped obstacles can be effectively solved by extending the local environment sampling boundaries. To optimize the paths, the redundant intermediate nodes are simplified based on the triangle inequality. Simulation analyses show that the proposed planner can adapt to a variety of scenarios in real time while satisfying optimal path conditions.

FACE SPOOF DETECTION USING CONVOLUTIONAL NEURAL NETWORK

The paper suggests a technique that uses convolutional neural network (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fake facial images. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, a standardized data set for identifying counterfeit or altered facial attributes was used. The suggested method presents various benefits compared to current techniques for detecting face spoofing. To start with, utilizing a deep CNN empowers the model to acquire complex and discerning characteristics from the input images, thus augmenting the precision of the categorization mission. Additionally, the suggested method is effective in terms of computational requirements, enabling its utilization in real-time scenarios. The proposed methodology is able to withstand a range of fraudulent tactics used on facial recognition systems, such as print and replay attacks. The findings from this study aid in the progression of face recognition technology by enhancing the accuracy and dependability of fraud detection systems. These improved systems have practical applications in security measures, biometric identification, and digital criminal investigations. The suggested method could substantially enhance the dependability and safety of facial recognition systems, consequently boosting their functional value and credibility.

HYBRID NEURO-FUZZY-GENETIC ALGORITHMS FOR OPTIMAL CONTROL OF AUTONOMOUS SYSTEMS

In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and genetic algorithms to achieve adaptive and optimal control in real-time scenarios. The neuro-fuzzy component provides the ability to model and handle complex and uncertain systems, while the genetic algorithm component facilitates the optimization of control parameters. The combination of these techniques enables autonomous systems to adapt and optimize their control strategies based on changing environments and objectives. The paper discusses the underlying principles of hybrid neuro-fuzzy-genetic algorithms, their advantages, and challenges. It also provides a review of the state-of-the-art research in this field, highlighting successful applications and potential future directions. Overall, the integration of neuro-fuzzy-genetic algorithms in autonomous systems holds great promise for achieving optimal control in various domains, including robotics, aerospace, and autonomous vehicles.

Modeling and Validation of a Lithium-Ion Battery Pack

Abstract Batteries are part of an extremely nonlinear electrochemical system, and their performance is determined by various key factors such as law of thermodynamics, electrode kinetics, ion transport, and diffusion. To develop an accurate model of a battery, it is important to take into consideration both the internal and external conditions that would determine both the battery dynamics and its performance. Electrochemical, mathematical, and electrical equivalent circuit models are three common ways to model the electrochemical battery system. However, using these models involves certain limitations such as increased computation, empirical equations, and components (both active and passive). A simple electrical battery model with a voltage source and a resistor is insufficient to attain the desired level of accuracy in the model. To overcome these limitations, this study presents an electrothermal model that has been designed by adding “n” number of resistance capacitance (RC) pairs connected parallelly to ensure both dynamics and accuracy. An electrothermal model of a lithium-ion cell with a one-RC model and two-RC model has been developed, and a performance comparison has also been made to check the voltage accuracy of all the models. The electrical and thermal parameters (Em (state of charge [SOC], T), RO (SOC, T), R1 (SOC, T), R2 (SOC, T), C1 (SOC, T), C2 (SOC, T)) required for an RC model have been extracted through the pulse charge-discharge test. A battery pack of 55 V, 50 Ah using the RC model based on the Indian driving cycle condition has also been developed. Pulse charge-discharge and constant current-constant voltage profiles have been applied to the developed battery pack model to simulate and validate the accuracy of voltage in a real-time scenario.

A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review.

Human gene sequences are considered a primary source of comprehensive information about different body conditions. A wide variety of diseases including cancer, heart issues, brain issues, genetic issues, etc. can be pre-empted via efficient analysis of genomic sequences. Researchers have proposed different configurations of machine learning models for processing genomic sequences, and each of these models varies in terms of their performance & applicability characteristics. Models that use bioinspired optimizations are generally slower, but have superior incremental-performance, while models that use one-shot learning achieve higher instantaneous accuracy but cannot be scaled for larger disease-sets. Due to such variations, it is difficult for genomic system designers to identify optimum models for their application-specific & performance-specific use cases. To overcome this issue, a detailed survey of different genomic processing models in terms of their functional nuances, application-specific advantages, deployment-specific limitations, and contextual future scopes is discussed in this text. Based on this discussion, researchers will be able to identify optimal models for their functional use cases. This text also compares the reviewed models in terms of their quantitative parameter sets, which include, the accuracy of classification, delay needed to classify large-length sequences, precision levels, scalability levels, and deployment cost, which will assist readers in selecting deployment-specific models for their contextual clinical scenarios. This text also evaluates a novel Genome Processing Efficiency Rank (GPER) for each of these models, which will allow readers to identify models with higher performance and low overheads under real-time scenarios.

VMLHST: Development of an Efficient Novel Virtual Reality ML Framework with Haptic Feedbacks for Improving Sports Training Scenarios

This paper presents the development of a novel virtual reality (VR) machine learning (ML) framework that incorporates haptic feedback to improve sports training scenarios. The framework uses You Look Only Once (YoLo) for object detection, and combines it with ensemble learning to analyze the performance of athletes in a simulated environment and provide real-time feedbacks. The system includes haptic feedback devices that are controlled via Grey Wolf Optimization (GWO) to simulate the physical sensation of a real-world sports scenario, allowing athletes to experience the sensation of force, impact, and movements. The proposed system was tested using a group of professional athletes who participated in various sports, including football, basketball, and tennis. The athletes were asked to perform various training scenarios in the virtual environment, and their performance was compared with their real-world performance levels. The results showed that the proposed system improved the athletes' performance significantly, as they were able to receive immediate and accurate feedback on their movements, and the haptic feedback provided a realistic sensory experience that enhanced their trainings. The proposed research has the potential to revolutionize sports training by providing athletes with an efficient and effective way to improve their performance in a set of safe and controlled environments. The system can be customized to suit various sports and training scenarios, and the ML algorithms can be trained on large datasets to improve their accuracy and effectiveness. The incorporation of haptic feedback provides a unique and realistic experience, making the training more engaging and effective under real-time scenarios. The proposed system showcased an accuracy 93.5%, with 3.5% higher precision, and 4.9% higher recall than existing models, which has the potential to enhance athletic performance and revolutionize the way athletes train for different sports.

IARMTS: Design of an Interference-Aware Routing Model with Time Synchronization Capabilities for Dense Wireless Sensor Network Deployments

Performance of dense wireless sensor networks is often degraded due to communication interference and time synchronization issues. Existing machine learning & deep learning models that propose bioinspired & pre-emptive packet-analysis solutions for these tasks either have high complexity, or high deployment costs. Moreover, these models cannot be scaled for heterogeneous node & traffic types, which limits their applicability when applied to real-time scenarios. To overcome these issues, this text proposes design of an interference-aware routing model with time synchronization capabilities for dense wireless sensor network deployments. The network initially collects temporal clock states & packet delivery performance of different nodes on heterogeneous traffic scenarios. These traffic patterns are converted into frequency, entropy, Gabor, and Wavelet components. The converted components are used to train an ensemble set of Naïve Bayes (NB), k Nearest Neighbour (kNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers. These classifiers assist in identification of optimal clock deviations and set of routing paths. These routing paths are further fine-tuned via use of a Bacterial Foraging Optimization (BFO) Model, which assists in identification of interference-aware paths. The BFO Model uses a temporal fitness function that fuses throughput, communication delay, energy levels, and packet delivery performance for different set of contextual communications. Due to which, the model is able to showcase lower end-to-end delay, higher throughput, lower energy consumption, and higher packet delivery performance when compared with existing routing methods under high density nodes & heterogeneous network scenarios. The model showcases 99% PDR, 18.3% lower delay, 19.5% higher energy efficiency and 10.4% lower delay levels when compared with existing methods.

A New Big Data Processing Framework for the Online Roadshow

The Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engagement process between the audience and the Online Roadshow (e.g., gameplay data and clickstream information). The high volume of data collected is valuable for more effective market segmentation in strategic business planning through data-driven processes such as web personalization and trend evaluation. However, the data storage and processing techniques used in conventional data analytic approaches are typically overloaded in such a computing environment. Hence, this paper proposed a new big data processing framework to improve the processing, handling, and storing of these large amounts of data. The proposed framework aims to provide a better dual-mode solution for processing the generated data for the Online Roadshow engagement process in both historical and real-time scenarios. Multiple functional modules, such as the Application Controller, the Message Broker, the Data Processing Module, and the Data Storage Module, were reformulated to provide a more efficient solution that matches the new needs of the Online Roadshow data analytics procedures. Some tests were conducted to compare the performance of the proposed frameworks against existing similar frameworks and verify the performance of the proposed framework in fulfilling the data processing requirements of the Online Roadshow. The experimental results evidenced multiple advantages of the proposed framework for Online Roadshow compared to similar existing big data processing frameworks.