Proposed Work Research Articles

Combined optimization strategy: CUBW for load balancing in software defined network

Software Defined Network (SDN) facilitates a centralized control management of devices in network, which solves many issues in the old network. However, as the modern era generates a vast amount of data, the controller in an SDN could become overloaded. Numerous investigators have offered their opinions on how to address the issue of controller overloading in order to resolve it. Mostly the traditional models consider two or three parameters to evenly distribute the load in SDN, which is not sufficient for precise load balancing strategy. Hence, an effective load balancing model is in need that considers different parameters. Considering this aspect, this paper presents a new load balancing model in SDN is introduced by following three major phases: (a) work load prediction, (b) optimal load balancing, and (c) switch migration. Initially, work load prediction is done via improved Deep Maxout Network. COA and BWO are conceptually combined in the proposed hybrid optimization technique known as Coati Updated Black Widow (CUBW). Then, the optimal load balancing is done via hybrid optimization named Coati Updated Black Widow (CUBW) Optimization Algorithm. The optimal load balancing is done by considering migration time, migration cost, distance and load balancing parameters like server load, response time and turnaround time. Finally, switch migration is carried out by considering the constraints like migration time, migration cost, and distance. The migration time of the proposed method achieves lower value, which is 27.3%, 40.8%, 24.40%, 41.8%, 42.8%, 42.2%, 40.0%, and 41.6% higher than the previous models like BMO, BES, AOA, TDO, CSO, GLSOM, HDD-PLB, BWO and COA respectively. Finally, the performance of proposed work is validated over the conventional methods in terms of different analysis.

Electrically tunable birefringence in nanophotonic waveguide with 2D electron gas in semiconductor heterojunction

An electrically tunable birefringence in a nanophotonic waveguide based on MgZnO-CdZnO semiconductor heterojunction is numerically proposed. Electrical control is realized with two-dimensional electron gas (2DEG) at the interface of ZnO based heterojunction and is used to control the polarization. The waveguide is engineered to enhance interaction of guided optical mode(s) with 2DEG providing us with a wide tuning in modal birefringence. We perform numerical analysis of phase and polarization characteristics of 2DEG based nanophotonic waveguide. Electrical tuning of 0.0112 in modal birefringence is reported by varying the applied voltage from 0 Vto −10 V. The proposed device also exhibits voltage variable optical phase modulation in both TE and TM polarizations. The proposed work can be utilized in applications where integrated photonic devices with variable polarization control are desirable.

Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images.

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

Deep learning-based approach for prediction of brain stroke from MR images for IoT in healthcare

<p class="CM12">This study develops a technique to predict brain strokes using magnetic resonance imaging (MRI). Worldwide, brain stroke is a leading factor in death and long-term impairment. The impact of stroke on the life of survivors is substantial, often resulting in disability. Stroke analysis performed manually takes a lot of time and is subject to intra- and inter-operator variability. Consequently, this work aims to create a computer-based system for the prediction of stroke utilizing deep learning techniques, which help in timely diagnosis. The MRI images are preferred as it provides images of good contrast and no ionizing radiations are used in this imaging method. The deep learning methods included in this proposed work are DenseNet-121, Xception, LeNet, ResNet-50 and VGG-16. The DenseNet-121 classifier outperformed other classifiers and achieved acccuracy of 96%. The outcomes of the proposed approach for stroke prediction in IOT healthcare systems show that improved performance is attained using deep learning methods.</p>

Towards efficiently solving the rubik’s cube with deep reinforcement learning and recursion

The Rubik’s cube is a prototypical combinatorial puzzle that has a large state space with a single goal state. The goal state is unlikely to be retrieved using orders of randomly generated moves, posing unique challenges for machine learning. The proposed work is above to solve the Rubik’s cube with recursion and DeepCubeA, a deep reinforcement learning approach that learns how to solve increasingly difficult states in reverse from the goal state without any specific domain knowledge. DeepCubeA solves 100% of all test patterns, finding a shortest path to the goal state 60.3% of the time. Deep Cube A generalizes to other combinatorial puzzles andis able to solve the 15 puzzle, 24 puzzle, 35 puzzle, 48 puzzle, Lights Out and Sokoban, finding a shortest path in the majority of verifiable cases. These models were trained with 1 4 GPUs and 20 30 CPUs. This varies throughout training as the training is often stopped and started again to make room for other processes. Further our experimentation compares the results of Rubik’s cube solving among both recursion and DeepCubeA and also with the state of art models. Later, we intend to develop a new deep learning model with an application.

Appliance Identification via Combinatorial Fusion Analysis-Assisted Bayesian-Optimized Classifier

Non-Intrusive Load Monitoring (NILM) aids in estimating the individual energy consumed by different appliances using aggregate signals measured at point of common connection. This paper explores various unexplored aspects concerning load classification in NILM. Rich feature sets are extracted from load signatures using auto-correlogram, Variational Mode Decomposition (VMD), and Wavelet Packet Tree (WPT) decomposition. Typically, previous works employ a single Feature Selection (FS) technique to earmark the relevant features for improved performance of NILM. However, each FS technique behaves uniquely, and the effects of using different FS techniques or their combinations have yet to be explored for NILM. Hence, Combinatorial Fusion Analysis (CFA), which surpasses the performance of a single FS technique by considering an optimal combination of multiple FS techniques, is used in this paper. The final selected features are fused and provided to a classifier. Rather than manually tuning or performing an exhaustive search for setting the classifier’s hyper-parameters, as prevalent in previous works, the present work utilizes Bayesian Optimization (BO) comprising surrogate and acquisition functions. The proposed work’s efficacy is validated on publicly available data-sets in a host computer and an embedded device, namely, R-Pi, via comparison with previous works.

AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users

Social networking websites are now considered to be the best platforms for the dissemination of news articles. However, information sharing in social media platforms leads to explosion of fake news. Traditional detection methods were focusing on content analysis, while the current researchers examining social features of the news. In this work, we proposed a novel artificial intelligence (AI)-assisted fake news detection with deep natural language processing (NLP) model. The proposed work is characterized in four layers: publisher layer, social media networking layer, enabled edge layer, and cloud layer. In this work, four steps were carried out: 1) data acquisition; 2) information retrieval (IR); 3) NLP-based data processing and feature extraction; and 4) deep learning-based classification model that classifies news articles as fake or real using credibility score of publishers, users, messages, headlines, and so on. Three datasets, such as Buzzface, FakeNewsNet, and Twitter, were used for evaluation of the proposed model, and simulation results were computed. This proposed model obtained an average accuracy of 99.72% and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$</tex-math> </inline-formula> score of 98.33%, which outperforms other existing methods.

Performance of stand-alone bifacial photovoltaic module using non-biodegradable waste as reflectors for tropical climatic region of southern India: An experimental approach

Bifacial photovoltaic modules (Bi-FM) generate more power compared to mono-facial modules (Mo-FM) even in diffused light conditions. Deployment of reflectors in Bi-FM increases the cost of implementation. The unique feature of this proposed work is that it utilizes waste material as reflectors. The cost of the reflective material is 10% of the total module cost, and the proposed work is economically viable due to waste reusability. In this work, three different non-biodegradable materials (polyvinyl chloride flex, thermocol, and aluminium foil) are reused as reflectors to improve their generation using Bi-FM. A detailed study is made using the waste materials as reflectors and the performance ratio is computed for different irradiation conditions observed across a day. The average performance ratio of thermocol and polyvinyl chloride flex is 20% and 8% respectively, higher than that of aluminium foil. Using a reflector made of PVC flex has a 3.59-year lifespan as compared to Bi-FM by employing a reflector made of aluminium foil. The deployment of non-biodegradable materials reduces the cost requirements. The proposed setup also increases the reusability of non-biodegradable waste materials as reflectors and improves the power generation of Bi-FM.

Fuzzy Heuristics for Detecting and Preventing Black Hole Attack

Mobile Ad-hoc Networks (MANET) is a set of computing nodes with there is no fixed infrastructure support. Every node in the network communicates with one another through wireless links. However, in MANET, the dynamic topology of the nodes is the vital demanding duty to produce security to the network and the black hole attacks get identified and prevented. In this paper, a novel fuzzy inference system is designed for black hole attack detection depending on the node authentication, trust value, Certificate Authority (CA), energy level, and message integrity. Before initiating the route discovery process in MANET, the proposed work mainly concentrates on node authentication. The simulation gets carried out using the Network Simulator (NS2), wherein the fuzzy inference system designed shows better performance by providing a certificate to only the trusted nodes. This helps the malicious nodes detection and prevents the black hole attack. The improvement in Packet Delivery Ratio (PDR) enhances throughput and the end to end delay gets reduced through better performance results. This proves that the system is more reliable and recovered to be used in military applications

Hardness determination by a semi-automatic testing system

The hardness testing is vital for metals for testing and production quality control. Hardness testing is a fine indicator to determine the mechanical characteristics of metals. The various types of hardness testing methods include Rockwell, Vicker, Brinell, and Knoop testing methods. The most prevalent manual method to determine hardness is indentation using dead weight technology. However, the said technology is quite ancient and it has its own limitations, like the kinetic effect applying excessive load at actual, man-to-man variations, variation over the period due to deterioration and friction effects of moving parts like lever, weight block hanger, etc. increases the uncertainty of this method and eventually demands for upgradation of the technology. Further, the methods currently in use are open-loop and manually operated testers. The main drawback of all these testers is that they provide zero feedback, and so the obtained result needs to be compared to the desired result. To overcome the above-stated problem, the paper presents some novel modifications for measuring hardness using existing methods of Rockwell, Vicker, and Brinell testing. Toward this objective, the paper proposes to modify existing testers and develop a semiautomatic hardness testing system. Firstly, for Rockwell testing, the work aims to design a closed-loop system for hardness testing. The proposed system comprises of Arduino as an open microcontroller, a load cell along with servo motor, and its driver to establish the desired closed loop. The need of obtaining a more accurate result, free from human interference gives rise to a closed-loop system for hardness testing. The key aspect of the closed-loop system lies in retaining the basic structure of the metal. Unlike traditional hardness tester, during testing, the weight increases gradually, due to which the basic structure of metal is not deformed. Secondly, the paper proposes a computational methodology that would estimate the Vickers and Brinell hardness value. The proposed system comprises of a camera for gathering hardness indentation images, thereby automating the system. Further, these images are analyzed based on image processing software, and the indentation depth is obtained. The proposed work thus replaces human intervention to calculate the hardness of the material. The results obtained based on the proposed systems provide a proof of concept to address the problems of traditional Rockwell, Vicker, and Brinell testing.

Bio-Inspired Algorithm Based Undersampling Approach and Ensemble Learning for Twitter Spam Detection

Currently, social media networks such as Facebook and Twitter have evolved into valuable platforms for global communication. However, due to their extensive user bases, Twitter is often misused by illegitimate users engaging in illicit activities. While there are numerous research papers available that delve into combating illegitimate users on Twitter, a common shortcoming in most of these works is the failure to address the issue of class imbalance, which significantly impacts the effectiveness of spam detection. Few other research works that have addressed class imbalance have not yet applied bio-inspired algorithms to balance the dataset. Therefore, we introduce PSOB-U, a particle swarm optimization-based undersampling technique designed to balance the Twitter dataset. In PSOB-U, various classifiers and metrics are employed to select majority samples and rank them. Furthermore, an ensemble learning approach is implemented to combine the base classifiers in three stages. During the training phase of the base classifiers, undersampling techniques and a cost-sensitive random forest (CS-RF) are utilized to address the imbalanced data at both the data and algorithmic levels. In the first stage, imbalanced datasets are balanced using random undersampling, particle swarm optimization-based undersampling, and random oversampling. In the second stage, a classifier is constructed for each of the balanced datasets obtained through these sampling techniques. In the third stage, a majority voting method is introduced to aggregate the predicted outputs from the three classifiers. The evaluation results demonstrate that our proposed method significantly enhances the detection of illegitimate users in the imbalanced Twitter dataset. Additionally, we compare our proposed work with existing models, and the predicted results highlight the superiority of our spam detection model over state-of-the-art spam detection models that address the class imbalance problem. The combination of particle swarm optimization-based undersampling and the ensemble learning approach using majority voting results in more accurate spam detection.

Green route synthesis of transition metal doped V2O5 nanoparticles, with emerging biomedical applications

In this proposed work we synthesized the pristine V2O5 nanoparticles and Ag (3.2 % (SV-1), 14.5 %, (SV-5) 23.4 % (SV-9), Cd (5.8 % (CV-1), 23.5 % (CV-5), 35.7 % (CV-9)) and Ru doped (4.9 % (RV-1), 20.7 % (RV-5), 32 % (RV-9)) V2O5 nanoparticles using vigna radiata extract. The nanoparticles were characterized by various techniques like UV-DRS, FT-IR, XRD, FE-SEM and HR-TEM. The antibacterial activities of nanoparticles were studied using bacterial strains such as Staphylococcus aures (S.aureus), Streptococcus pneumoniea (S. pneumoniea), Pseudomonas aeruginosae (P. aeruginosae), Klebsiella pneumoniea (K. pneumoniea) and Escherichia coli (E.coli). Among the nanoparticles the potential bacterium growth inhibition of 99 % for S.aureus was observed for CV-5. The binding efficiency of the nanoparticles with the biomolecule BSA was studied by using UV–visible absorption and emission spectral techniques. It was found that there is a binding interaction between them and the binding constant values are in the range from 104 to 105 M−1. The quenching rate constant (kq) value is bigger than the maximum diffusion constant of biomolecules 2.0 x 1010M−1s−1 suggesting that the interaction is static quenching type. In glucose oxidation studies lower potential with a high current density of 440 mA/cm2 occurred for pristine V2O5 NPs.

Lightweight Neural Network for Enhancing Imaging Performance of Under-Display Camera

Under-Display Camera (UDC) is an emerging feature of cellphone. This technology makes full-screen cellphones possible by hiding the front-facing camera below the display panel, which is in contrast to the conventional designs that place the camera in a bezel or punch-hole on the screen border. However, this novel imaging paradigm also causes degradation. The display panel attenuates and diffracts incoming light, so the images captured by UDC contain multiple artifacts, such as blurring, color shift, and low intensities. This paper proposes a lightweight deep learning approach to restore UDC images in a blind setting. The restoration network uses cross-scale modulation to exploit complementary information from multi-scale representations and capture the self-similarity across scales, aiming to find the cues for recovering distortion-free images. To facilitate the deployment of this scheme across mobile devices, especially on those with limited memory space and computing power, we compress the restoration network by reducing architectural redundancy. An adaptive distillation algorithm is designed to exploit knowledge from a pre-trained full-size model. The proposed work also interprets the behavior of the neural network in utilizing local and non-local information to restore UDC images. The proposed algorithm is evaluated over three datasets of the images captured by the cameras below different types of display panels. The results of comparative experiments demonstrate that our algorithm shows comparable or superior performance to the competing ones that are much heavier in parameter amount and computational complexities.

Optimalno podešavanje parametara simuliranog kaljenja

Introduction/purpose: Simulated annealing is a powerful technique widely used in optimization problems. One critical aspect of using simulated annealing effectively is a proper and optimal adjustment of its parameters. This paper presents a novel approach to efficiently adjust the parameters of simulated annealing to enhance its performance and convergence speed. Methods: Since the simulated algorithm is inspired by the cooling Metropolis process, the basic idea is to simulate and analyze this process using a mathematical model. The proposed work tends to properly imitate the Metropolis cooling process in the algorithmic field. By intelligently adjusting the temperature schedule, temperature reduction and cooling rate, the algorithm optimizes the balance between exploration and exploitation, leading to improved convergence and higher-quality solutions. Results: To evaluate the effectiveness of this approach, it was applied first on a chosen sample function to be minimized, and then on some usual known optimization functions. The results demonstrate that our approach, called Optimal Adjusting of Simulated Annealing parameters (OASA), achieves superior performance compared to traditional static parameter settings and other existing approaches, showing how to well adjust the parameters of the simulated annealing algorithm to improve its efficiency in terms of solution quality and processing time. Conclusion: Adjusting the algorithm parameters could have a significant contribution in the optimization field even for other metaheuristics.

IoT based Smart Trolley for Shopping using RFID and Node MCU

Customers are helped when shopping by the sophisticated shopping cart system. The goal of this proposed work is to create a hardware and software implementation of a modular, affordable, and user-friendly smart shopping system. This system may be tweaked, and dealers can begin using it right now. It tries to satisfy as numerous of the endconditions druggies as doable. Obviously, digital commerce is growing in vogue, but numerous consumers still need to see what they are purchasing and assimilate the various add-ons offered in the emporium. Still, in these situations, people are pressured to stay in lines that might grow rather long, and bearing in a line creates a variety of discomforts and pressure. Every item may be fixed with an accessible RFID label that, when inserted into a smart shopping cart, may be read automatically by a cart fitted with an RFID reader. This system can incorporate smart shelving, which has RFID scanners installed and can cover stock as well as update a central server. Another advantage of this technology is that it makes tally operations considerably simpler because an RFID reader can automatically read all effects and determine whether a product's expiration date has passed. The IoT website will be completely reconditioned.

THORIN: an Efficient Module for Federated Access and Threat Mitigation in Big Stream Cloud Architectures

In order to make cloud services attractive for several IT organizations, it is necessary to provide access control and to implement safe and reliable mechanisms of Identity and Access Management (IAM). In this work, we focus on security issues and challenges in the design and implementation of cloud architectures and, in particular, for the management of Big Stream applications in Internet of Things (IoT) scenarios. The proposed work introduces a new set of modules allowing a federated access control policy for cloud users. An analysis of possible threats and attacks against the proposed Big Stream platform is presented, investigating the system performance in terms of detection and elimination of malicious nodes. In particular, we propose a new module, denoted as Traffic Handler Orchestrator & Rapid Intervention (THORIN), which is very efficient in counteracting botnet-based threats.

A Dual-Objective Approach for Allocation of Virtual Machine with improved Job Scheduling in Cloud Computing

In Cloud Computing (CC) environment, requests of user are maintained via workloads that are allocated to Virtual Machines (VMs) using scheduling techniques which primarily focus on reducing the time for processing by generating efficient schedules of smaller lengths. The efficient processing of requests also needs larger usage of resources that incurs higher overhead in the form of utilization of energy and optimization of cost utilized by Physical Machines (PMs). Assignment of VMs optimally in the environment of CC for jobs submitted by users is a challenge. In order to obtain better solution involving scheduling of jobs to VMs, considering two parameters utilization of energy and cost, we present a dual-objective approach for VM allocation with improved scheduling of jobs in CC environment. The proposed work aimed to build a dual-objective scheduling model for improved job scheduling, focusing on minimization of cost and utilization of energy at a time. For evaluating performance of dual-objective approach, we utilized two types of benchmark datasets and compared with existing approaches such as Whale, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Metaheuristic Dynamic VM Allocation (MDVMA) techniques. The results obtained from simulation demonstrated that dual-objective approach performs better in the form of minimization of utilization of energy and cost

Nano-modified screen-printed electrode-based electrochemical immunosensors for oral cancer biomarker detection in undiluted human serum and saliva samples.

This proposed work reports the development of in-house made conductive ink-based screen-printed electrodes (SPEs) for label-free detection of oral cancer biomarkers. Carbon ink synthesis includes graphite powder, gum arabic, and water. The selectivity test of the fabricated SPE involves immobilizing antibodies specific to biomarkers and challenges with redox-active interference, other serum molecules, and non-target biomarkers. Three different biomarkers, cytokeratin-19 fragment (CYFRA 21-1), interleukin 8 (IL-8), and tumor protein p53 (TP-53), act as target entities for the detection of oral cancer in patients' samples (serum, N = 28, and saliva, N = 16) at an early stage. The standard technique enzyme-linked immunosorbent assay (ELISA) was employed to estimate the concentration of the biomarkers in serum and saliva samples. SPEs contain amine (-NH2) functional groups involved in covalent bonding with the carboxyl (-COOH) groups of antibody molecules. These immunosensors exhibited remarkably lower detection limits of 829.5 pg mL-1, 0.543 pg mL-1, and 1.165 pg mL-1, and excellent sensitivity of 0.935 μA mL pg-1 cm-1, 0.039 μA mL pg-1 cm-1, and 0.008 μA mL pg-1 cm-1 for CYFRA 21-1, IL-8, and TP-53 biomarkers, respectively. This sensing platform does not require any functionalization for biomolecule immobilization. Thus, it is a cost-effective, disposable, flexible, miniaturized, and sensitive strip to detect oral cancer biomarkers.

Design of Biogas Digester for Small Community of Salalah Countryside, Oman to Support the Lighting and Cooking Electrical Load

Bio-gas is a renewable energy source derived from the anaerobic digestion of organic materials such as agricultural, manure, and food waste. It emits methane and carbon dioxide, which reduce greenhouse gas emissions and promote circular economies by converting organic matter into valuable energy and nutrients. In this study, the biogas-based power generation possibilities are studied and reported based on the availability of biomass from animals like camels, sheep, goats, and cows. The data collection and analysis are carried out under different temperature conditions. The energy required is planned for eight people in each family. In the proposal work to design a digester, the location was chosen at the south-eastern part of the Arabian Peninsula (south of the Sultanate of Oman) for the place Madinat al Haqq. Afterwards, calculations are reported to estimate the size of the biogas digester and the output power generated from the digester. Here, the digester output is used for cooking and lighting for all members of the family. Further, the power output is viable based on the availability of biomass per day. The energy availability investigation is performed using the standard calorific value of the biomass per cubic meter. The proposed concept will allow the remote community to use a portal-sized biogas digester for their necessary electrical home load. Furthermore, the selection of the burner is reported to utilize the biogas efficiently.

A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification

Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.