Proposed Research Work Research Articles

Detection of Autism Spectrum Disorder (ASD) Symptoms using LSTM Model

Autistic children will often exhibit certain behaviors that are unique to them and that are not typical of neurotypical children. Parents will become familiar with these patterns over time and will be able to use this knowledge to answer questions about their child's behavior. Deep learning models are very useful to solve critical problems in the healthcare domain. Detection of ASD at the early age of a child is a challenging task. Recent research reveals that there is an increasing trend of ASD among children. Communication, eye contact, social behavior, and education are very poor for those who suffer from ASD. The proposed research work has been done to detect ASD symptoms in a child. Data has been collected from the various autism groups from social sites and organizations that are working on special children. A Deep learning model like the Long-Short Term Memory (LSTM) model has been used to detect the sentiment of parents’ dialog. LSTM is the most popular deep learning model that can able to solve complex natural language problems. The proposed LSTM model has been trained with prepared data and accuracy is 97% according to the prepared data.

Dynamic fuzzy learning based hybrid GWO-CSA for optimal planning of PV, BESS and DSTATCOM with network reconfiguration

The proposed approach is on the optimal planning issue of photovoltaic distributed generation (PV-DG) and DSTATCOM device with integrated battery energy storage systems (BESS) considering network reconfiguration by applying hybrid Grey-Wolf crow-search algorithm (GWO-CSA) encompassing dynamic fuzzy learning (DFL) optimization technique. Considering the stochastic nature of solar irradiance and variations, when solar energy is inadequate, BESS acts as backup energy storage device to meet essential load requirements. Network reconfiguration is to reduce power loss by changing the network tie switches using optimization algorithm. The principal objective of the proposed research work is to reduce the total power loss, enhance the voltage profile, improve the voltage stability index, and perform network reliability analysis. The beneficial effect of the proposed method is validated on Standard IEEE 69 and Standard 118 bus systems. It found that for the IEEE 69 bus system, the overall Real power loss values decreased to 12.82% and overall Reactive power loss increased to 78.18%. The total voltage deviation index (TTVDI), and total voltage stability index (TTVSI) values decreased to 17.94% and − 0.75% respectively. The reliability indices like SAIDI, SAIFI, CAIDI, CAIFI, and AENS values decreased by 56%, 3.54%, − 3.09%, 0.55% and 30% respectively. Similarly, for IEEE 118 bus system overall Real power loss, overall Reactive power loss, TTVDI, and TTVSI decreased to 9.53%, − 0.11%, 24.64%, and 3.87%. The reliability indices like SAIDI, SAIFI, CAIDI, CAIFI, and AENS values decreased by 2.4%, 1.0%, 1.54%, 0.94%, and 10.1% respectively. Further, the proposed approach of DFL-based hybrid GWO-CSA considers the reconfiguration with PV-DG, DSTATCOM and BESS enhances the overall performance compared to other scenarios.

Decision-making for solar panel selection using Sugeno-Weber triangular norm-based on q-rung orthopair fuzzy information

Solar power is an alternative energy derived from the sun. Solar power is more environmentally friendly and sustainable than burning fossil fuels which releases harmful greenhouse gas emissions. Therefore, this study aims to evaluate a reliable solar panel based on certain characteristics by incorporating the theory of the decision-making process. To serve this goal, this study discusses a well-known aggregation model of the q-rung orthopair fuzzy set, which is a broader and flexible environment of fuzzy sets and intuitionistic fuzzy sets used to handle unpredictable information of human opinions. The key components of this article are to demonstrate some realistic operations of Sugeno–Weber triangular norms considering q-rung orthopair fuzzy information. These operations provide authentic estimated information during the decision-making process. We developed a class of new aggregation operators using the q-rung orthopair fuzzy information system, including q-rung orthopair fuzzy Sugeno–Weber power weighted average and q-rung orthopair fuzzy Sugeno–Weber power weighted geometric operators. Some realistic characteristics and special cases are also demonstrated to show the compatibility of the proposed methodologies. An innovative approach to the multi-attribute decision-making problem is utilized to resolve different real-life applications considering various criteria or attributes. To show the intensity and applicability of the proposed approaches, we explored a numerical example for efficient solar panel selection based on the proposed methodologies. Furthermore, we presented a comprehensive comparison technique to compare the findings of the existing methods with the proposed aggregation approaches. Finally, the proposed research work is summarized, and the future prospects are discussed.

Shear behavior of low-cost and sustainable bio-fiber based engineered cementitious composite beams –experimental and theoretical studies

The design of reinforced concrete (RC) members subjected to shear dominant loading is complex due to their quasi-brittle failure nature. Hence, a highly ductile material as a replacement for conventional concrete is essential to perform well under shear loading. Engineered Cementitious Composite (ECC) is one such material designed based on micromechanical theory for achieving a high strain capacity of about 3%. The objective of the proposed research work is to develop a cost-effective ECC using bio-based natural fibers such as flax, hemp, kenaf, and pineapple for achieving a large strain levels under shear dominant loading. The test program consists of different ECC specimens with (a) 2.0% flax fiber, (b) 2.0% hemp fiber, (c) 2.0% kenaf fiber, and (d) 2.0% pineapple fiber. In addition, the ultimate shear capacity of natural fiber-based ECC beams are estimated theoretically using the AIJ method and the results are compared with the tests and FE predictions. Test results revealed that the use of flax fibers in ECC beams was highly effective in enhancing the ultimate load through an excellent crack-bridging mechanism when compared to the other natural fiber types. Also, the ultimate load predictions from analytical calculations matched well with the test results.

Digital image noise removal towards soybean and cotton plant disease using image processing filters

The processing of noisy images does not provide accurate outcomes. In most existing techniques, many filters have been employed to eradicate the noises and enhance the image quality. However, most of the works fail to generate enhanced outcomes because of degraded capability and increased time consumption. Hence, in the proposed research work, a novel pre-processing method called the optimization assisted cascaded filtering approach (OptCFA) is introduced. In the proposed Opt_CFA model, the Gaussian Amended Bilateral filter (GABF) is designed where the multilevel thresholds are calculated for the GABF outcomes. The filter parameters are optimized using the Amended Pelican optimization algorithm (AmPel) for first stage noise removal. The outcomes are then cascaded with an Extended Savitzky-Golay filter (E_SGF) for second stage noise removal. The proposed Opt_CFA model is a single approach with the integration of GABF, AmPel and E_SGF methods. In the results section, some existing pre-processing filters, like the median, bilateral, and Gaussian filters, are analyzed for comparison. The individual performance of GABF and E_SGF methods used in the proposed model are also analyzed. Through the proposed technique, the noises can be extensively removed, and the image quality can be greatly enhanced. Several existing filters are compared with the proposed model, whereas the results are analyzed using PYTHON.

Formulation and Evaluation of Herbal Emulgel Containing Syzigium Cumini Extract

Traditional healers use Syzygium cumini bark to treat inflammation. The main aim of this work is to formulate an emulgel containing extract of Syzigium cumini. Syzygium cumini bark was extracted using hydro-alcoholic solvent. Phytochemical evaluation of extract was done. In-vitro anti-inflammatory activity of the extract was evaluated. 4 batches of emulgel were formulated by varying the concentrations of the gelling agents. The emulgel was evaluated for pH, spreadability, viscosity, consistency, appearance, color, washability and appearance. Best results were shown by F2 formulation. Stability studies if F2 formulation was carried. The formulation was found to be stable. It was considered better for treatment of skin inflammation. The proposed research work concludes that the newly formulated herbal emulgel loaded with extract of Syzygium cumini can be used in future for anti-inflammatory effect.

Micro aneurysm detection using optimized residual-based temporal attention Convolutional Neural Network with Inception-V3 transfer learning.

In this manuscript micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) with Inception-V3 transfer learning optimized with equilibrium optimization algorithm (MA-RTCNN-Inception V3-EOA) is proposed. The proposed research work contains four phases: (1) pre-processing, (2) segmentation, (3) post-processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA-RTCNN-Inception V3-EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f-measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non-proliferative diabetic retinopathy depending on deep learning approaches (DRD-CNN-NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN-AMD-MAFP-Net) respectively. RESEARCH HIGHLIGHTS: Micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT-CNN algorithm successfully identifies the micro aneurysms using EOA.

A variegated GWO algorithm implementation in emerging power systems optimization problems

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm.

Orchard Mapping with Deep Learning Semantic Segmentation

Abstract: The goal of orchard mapping with deep learning semantic segmentation is to automatic detection and localization of the orchard tree canopy under varied situations like multiple seasons, various tree ages, and varying levels of weed covering. The accuracy of segmentation depends on many factors like season, canopy size, and presence of weeds, background soil condition, and untreated soil. The proposed research have several combinations of training & test data based on orchard conditions The proposed research work contains deep learning convolutional neural network variant. The algorithm U- net network will be used. This helps to automatic detect and localize the tree canopy size using UAV images. Image segmentation will help all farmers by automatically segmenting tree canopies from aerial images for the development of integrated tools. After measurement of these parameters, tree canopy volume can be detected of targeted tree

Algal organic matter induced photodegradation of tinidazole

Antibiotic pollution has become one of the most emerging problems of the modern era. Tinidazole (TDZ) is one the most important nitroimidazole derivative drugs whose use has tremendously increased in the last few years. The proposed research work provides a good alternative cost-effective method for wastewater treatment. In the present investigation, algae were used as a photosensitizer in the treatment of the wastewater that was contaminated with antibiotic residue. The proposed research also provides the probable mechanism involved in the photodegradation of tinidazole. The different factors like concentration and pH of the test solution which play a key role in the photodegradation of drug molecules are also discussed in the present investigation. The result of this study established that the maximum degradation of drug molecules was observed at the algal concentration of 1.6 × 108 Cell/L and approximately 58% of drug molecules were degraded. This study also established that in an acidic medium ie at pH 5 the degradation occurs more efficiently. Results of the current study indicated that the use of algae-induced photodegradation of drug residue became one of the most promising routes for wastewater treatment. The results of the present study provide a new way to treat wastewater contaminated with antibiotics residue.

Formulation and Evaluation of Emulgel loaded with leaf extract of Artemisia princeps for its Anti-Inflammatory potential

Indian Systems of Medicine have their roots in ayurveda. The potential of naturally occurring bioactive components is of tremendous interest to researchers. Different plant extracts have remedial properties.North India, Japan, China, Korea and Nepal are among the countries where Artemisia princeps can be found.Apigenin, diosmetin, eriiodicytol, eupafolin, kaempferol, vitex, jaceosidine, and quercetins have all been discovered in plant extracts of Artemisia princeps. It is used to treat fever, apoptosis, pain, ulcers, inflammation, and other conditions as well as Diarrhoea and Asthma.Recent in-vivo and in-vitro research have shown that it has anti-inflammatory and analgesic properties. The objectives of proposed research work are to formulate, evaluate and optimize the Emulgel loaded with optimised extract of Artemisia princepsand assess its In-Vitro Anti-Inflammatory Activity. Collected leaves of Artemisia princeps was extracted using hydro-alcoholic solvent. On the basis of their Phytochemical evaluation, the extract was optimized. Additional 6 batches of Emulgel were created utilizing a chosen extract. The prepared Emulgel was assessed for pH, spreadability, viscosity, consistency, appearance, colour, infrared spectral assignment, and appearance. Using the HRBC Membrane stabilisation assay, extract and formulation were tested for in vitro anti-inflammatory efficacy. The suggested study project's findings support the potential for future topical application of the newly developed Emulgel, which contains Artemisia princeps leaf extract, to reduce inflammation.

Influence of Ultracapacitor and Plug-In Electric Vehicle for Frequency Regulation of Hybrid Power System Utilizing Artificial Gorilla Troops Optimizer Algorithm

The proposed research work considers the effect of ultracapacitor (UC) and plug-in electric vehicle (PEV) inside a distributed energy sources based on three-area hybrid power system. Recently developed artificial gorilla troops optimizer (GTO) algorithm-based fractional order tilt integral derivative (FOTID) controller is suggested for load frequency control (LFC) of a three-area hybrid power system. By setting up a TID controller for the hybrid system’s frequency regulation, the algorithms’ realistic use is attempted via showcasing its immense potential. The results of the GTO-based TID regulator are observed better over GTO-based PID. Next, by considering a modification of system settings and under existence of intermittent behavior of renewable energy sources, it is evident that the influence of UC and PEV is more ethical compared to the conventional regulator-based frequency control. Additionally, it is seen that the suggested GTO-based FOTID controller along with the UC and PEV outperforms GTO-based TID controller in terms of lesser overshoots/settling times and fast responses.

A Novel Hybrid Protocol and Code Related Information Reconciliation Scheme for Physical Layer Secret Key Generation

Wireless networks are vulnerable to various attacks due to their open nature, making them susceptible to eavesdropping and other security threats. Eavesdropping attack takes place at the physical layer. Traditional wireless network security relies on cryptographic techniques to secure data transmissions. However, these techniques may not be suitable for all scenarios, especially in resource-constrained environments such as wireless sensor networks and adhoc networks. In these networks having limited power resources, generating cryptographic keys between mobile entities can be challenging. Also, the cryptographic keys are computationally complex and require key management infrastructure. Physical Layer Key Generation (PLKG) is an emerging solution to address these challenges. It establishes secure communication between two users by taking advantage of the wireless channel's inherent features. PLKG process involves channel probing, quantization, information reconciliation (IR) and privacy amplification to generate symmetric secret key. The researchers have used various PLKG techniques to get the secret key, sTop of Form
 till they face problems in the IR scheme to obtain symmetric keys between the users who share the same channel for communication. Both the code based and protocol based methods proposed in the literature have advantages and limitations related to their performance parameters such as information leakage, interaction delay and computation complexity. This research work proposes a novel IR mechanism that combines the protocol and code-based error correction methods to obtain reduced Bit Mismatch Rate (BMR), reduced information leakage, reduced interaction delay, and reduced computational time to enhance physical layer secret key's quality. In the proposed research work, the channel samples are generated using the Received Signal Strength (RSS) and Channel Impulse Response (CIR) parameters. These samples are quantized using Vector Quantization with Affinity Propagation Clustering (VQAPC) method to generate the preliminary key. The samples collected by the two users who wish to communicate, (for example Alice and Bob) will be different due to noise in the channel and hardware limitations. Hence their preliminary keys will be different. Removing this discrepancy between Bob's and Alice's initial keys, using novel Hybrid Protocol and Code related Information Reconciliation (HPC-IR) scheme to generate error corrected key, is the most important contribution of this research work. This key is further encoded by the MD5 hash function to generate a final secret key for exchanging information between two users over the wireless channel. It is observed that the proposed HPC-IR scheme achieves BMR of 19.4%, information leakage is 0.002, interaction delay is 0.001 seconds and computation time is 0.02 seconds.

Unsteady flow of a ternary nanofluid over a slow-rotating disk subject to uniform suction and backpropagated neural network

The purpose of the proposed research work is to explore the heat source or sink impact on the unsteady three-dimensional flow of ternary-hybrid nanofluid through a rotating disk. The magnetohydrodynamic flow of ternary-hybrid nanofluid under the impact of radiative heat transfer and uniform suction is also discussed in this study. The partial differential equations of the flow problem are reduced into ordinary differential equations by employing apt similarity transformation and solved numerically using the Runge–Kutta Fehlberg fourth–fifth order method. The various nondimensional parameters’ effects on velocity and thermal profiles are illustrated using graphs. In addition, a Levenberg Marquardt backpropagated neural network is employed for determining the Nusselt number and skin friction model. The outcomes of the developed Levenberg Marquardt backpropagated neural network models are indicated through the performance metrics. Result reveals that a rise in the suction parameter decreases the velocity profiles. The thermal profile increases with higher values of thermal radiation and heat source/sink parameters. In addition, the presented Levenberg Marquardt backpropagated neural network models’ scheme is found to be a perfect tool for estimating heat transfer and surface drag force models.

Evaluating the Performance of Blockchain-Based Land Registration Management Using Hyperledger Caliper

Abstract: The land is a fixed property. Finding the details of property and involvement of third-party is a challenging task. Fraudsters may forge documents and mislead thepurchaser and if it's a disputed land it takes many years in the court battle, a waste of time and resources. Problems escalate in this current land registration process which is notclear. Such calls were made to integrate technologies and lead to the growth of blockchain technology. Blockchain isa decentralized and transparent ledger. The smart contract is an empirical and permanent document between two parties. This proposed model is used to develop a land registration application system in which the new buyer hasto register and continue with the further process. The proposed research work is implemented using the SHA256algorithm which provides unique hash values for the messages getting stored in blocks and Ethereum blockchaintechnology is used to store both smart contract and transaction details.

Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.

Evaluation of the Genetic Stability of Amaranthus viridis L. Species in Selected Regions of Western Ghats using Random Amplified Polymorphic DNA (RAPD)

Random Amplified Polymorphic DNA is cheap and fast molecular technique to identify the genetic resemblances or variations in DNA in various plants. The proposed research work wasintendedto compare the genetic diversification of Amaranthus viridis species in selected regions of Western Ghats in Tamilnadu and Kerala by RAPD analysis.The biometric morphological data among the five A. viridis accessions were compared. Their genomic DNA was isolated and finger prints were obtained using three RAPD markers. The percentage polymorphism, polymorphism information content, effective multiplex ration and resolving power were calculated. UPGMA dendrogram was constructed and their genetic relatedness was compared using Jaccard coefficient.Upon finger printing, 66 bands were counted for the three RAPD primers used,among which 46 bands were polymorphic band numbers from 8 to 21. Percentage of polymorphic bands ranged between 44.44% and 80.77%. EMRvalues for 66 polymorphic loci ranged between 3.56 and 17.01. The UPGMA dendrogram comparing the genomic profiles of the 5 A. viridis accessions using Jaccard coefficient revealed their genetic distances where the maximum Jaccard’s coefficient value observed was 0.96 between AVKATN and AVNEK. Among the 5 selections the peak similarity index (0.9565) was witnessed between AVKATN and AVNEK. The cuurent study revealed that AVKATN and AVNEK had the highest their genetic distances, whereas AVKATN and AVNEK were genetically similar among the 5 A. viridis accessions. Several polymorphic bands which ranged up to 80.77% have proved their genetic variations.

Oral Cancer Prediction Using a Probability Neural Network (PNN).

In India, usually, oral cancer is mostly identified at a progressive stage of malignancy. Hence, we are motivated to identify oral cancer in its early stages, which helps to increase the lifetime of the patient, but this early detection is also more challenging. The proposed research work uses a probabilistic neural network (PNN) for the prediction of oral malignancy. The recommended work uses PNN along with the discrete wavelet transform to predict the cancer cells accurately. The classification accuracy of the PNN model is 80%, and hence this technique is best for the prediction of oral cancer. Due to heterogeneity in the appearance of oral lesions, it is difficult to identify the cancer region. This research work explores the different computer vision techniques that help in the prediction of oral cancer. Oral screening is important in making a decision about oral lesions and also in avoiding delayed referrals, which reduces mortality rates.

Features of Genetic Sigma Factors Learning Model Simulating Neural Network

Sigma factors play a crucial role in the gene regulation process, which bind with RNA polymerase to unwind the genesequence by identifying the recognition of transcription starting motif pattern, a combination of nucleobases (Adenine(A),Cytosine(C), Guanine(G), Thymine(T)). The advancements in DNA analysis are useful for the geneticist to learn differentpatterns from different perspectives for the identification of mutations in genetic structures, new organisms ranging fromunicellular to multicellular, and useful for creating new gene patterns to get relief from hereditary diseases. To meet all thesechallenging needs, the proposed research work aimed to predict the DNA motif patterns of various sigma factors. Thus themain objective is to create a novel method named "Features of Genetic Sigma Factors Learning Model simulating NeuralNetwork" to predict the prefix motif patterns of major sigma factors such as sigma 70, sigma 32, sigma 24, sigma 19, and sigma38(σ70, σ32, σ24, σ19, and σ38). Each of the sigma factors possesses significant functionality, like vegetative growth for thedevelopment of nutrients. In the proposed model, the novel idea is a generation of a dictionary of DNA motifs that mimics then-gram of natural language processing. The proposed model is trained to feed the DNA motif dictionary, which consists ofpositive and negative motif patterns. The model is tested by an array of K-mer motif patterns taken from the whole E.Colibacterial genome, downloaded from the NCBI website (Escherichia coli str. K-12 substr. MG1655, complete genomeACCESSION: NC_000913). The experimental results of the proposed model yielded 100% accuracy. The model's outcome isa set of patterns that are highly helpful to experts in the biological fields to identify new gene patterns.

Data-Mining Model-Based Enhanced Differential Relaying Scheme for Microgrids

The article presents a data-mining model-based enhanced differential relaying scheme for fault detection and classification task in the microgrids. The proposed research work aims to provide a fixed setting, fast, and reliable scheme for accurately detecting and classifying faults in the microgrid. The presented scheme fetches the three-phase current signals from both ends of the distribution lines and computes the differential current phasor, including the distributed generation (DG) in the fault model. The deep neural network (DNN)-based data-mining models for fault detection and classification are built using differential current phasor datasets generated by wide variations in fault and operating conditions of the microgrid, including different types of faults with the change in fault location, fault resistance, DG penetration in grid-connected and islanding mode of operation with types of DG units. The developed DNN models are validated for their ability to detect the faults in a low-voltage CERTS microgrid and subsequently for a medium-voltage IEEE-34 bus microgrid on a MATLAB/SIMULINK platform. Furthermore, the proposed intelligent scheme is also tested on the Typhoon hardware-in-loop platform for real-time performance evaluation. The accuracy and speed in detecting and classifying faults indicate that the proposed DNN-based intelligent differential relaying scheme can be a potential candidate for providing reliable protection measures for the microgrid.