The objective of this study is to analyze the energy consumption associated with modern methodologies utilized in wireless sensor networks and to conduct a comparative assessment with Reed Solomon (RS) codes. This paper presents three discrete techniques for wireless sensor networks. The strategies mentioned include the Self-Evolving Sensor System (SESS), the Secure and Adaptive Key Management utilizing Multipath Routing Protocol (SAKM-MRP), and the National Instruments Secure Reference-based Data Aggregation (NI-SRDA). A distinct algorithm was developed for each method to examine the energy use. Based on the experimental results, it has been shown that the RS-codes approach consumes a considerably greater quantity of energy compared to the SESS methods, which, in contrast, exhibit a significantly lower energy consumption. When comparing the efficiency of RS-codes and SESS methods, it is observed that the SAKN-MRP technique exhibits a more significant decrease in energy consumption. Compared to the RS-Codes system, the SESS scheme stands out with a significant 45.5% reduction in energy usage at the maximum delivery node. Similarly, the SAKM-MRP scheme showcases an average decrease of 35.7% in energy consumption. Notably, the NI-SRDA scheme achieves an impressive 60% reduction in energy consumption, underscoring its remarkable impact on energy efficiency. In a broader sense, it can be inferred that the NI-SDRA technique holds promise as an energy-efficient solution for wireless sensor networks in comparison to alternative strategies suggested in the current study.
{"title":"A Comparative Analysis of Energy Consumption in Various Wireless Sensor Network Techniques","authors":"Suresh Vellaiyan, Vijayarani N","doi":"10.54392/irjmt2428","DOIUrl":"https://doi.org/10.54392/irjmt2428","url":null,"abstract":"The objective of this study is to analyze the energy consumption associated with modern methodologies utilized in wireless sensor networks and to conduct a comparative assessment with Reed Solomon (RS) codes. This paper presents three discrete techniques for wireless sensor networks. The strategies mentioned include the Self-Evolving Sensor System (SESS), the Secure and Adaptive Key Management utilizing Multipath Routing Protocol (SAKM-MRP), and the National Instruments Secure Reference-based Data Aggregation (NI-SRDA). A distinct algorithm was developed for each method to examine the energy use. Based on the experimental results, it has been shown that the RS-codes approach consumes a considerably greater quantity of energy compared to the SESS methods, which, in contrast, exhibit a significantly lower energy consumption. When comparing the efficiency of RS-codes and SESS methods, it is observed that the SAKN-MRP technique exhibits a more significant decrease in energy consumption. Compared to the RS-Codes system, the SESS scheme stands out with a significant 45.5% reduction in energy usage at the maximum delivery node. Similarly, the SAKM-MRP scheme showcases an average decrease of 35.7% in energy consumption. Notably, the NI-SRDA scheme achieves an impressive 60% reduction in energy consumption, underscoring its remarkable impact on energy efficiency. In a broader sense, it can be inferred that the NI-SDRA technique holds promise as an energy-efficient solution for wireless sensor networks in comparison to alternative strategies suggested in the current study.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"248 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140449718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research presents an innovative deep learning approach for forecasting the Air Quality Index (AQI), a crucial public health concern in both developed and developing countries. The proposed methodology encompasses four stages: (a) Pre-processing, involving data cleaning and transformation; (b) Feature Extraction, capturing central tendency, dispersion, higher order statistics, and Spearman's rank correlation; (c) Feature Selection, using a novel hybrid optimization model, Particle Updated Grey Wolf Optimizer (PUGWO); and (d) an ensembled deep learning model for AQI prediction, integrating a Convolutional Neural Network (CNN), an optimized Bi-directional Long Short-Term Memory (Bi-LSTM), and an Auto-encoder. The CNN and Auto-encoder are trained on the extracted features, and their outputs are fed into the optimized Bi-LSTM for final AQI prediction. Implemented on the PYTHON platform, this model is evaluated through R^2, MAE, and RMSE error metrics. The proposed HRFKNN model demonstrates superior performance with an R-Square of 0.961, RMSE of 11.92, and MAE of 10.29, outperforming traditional models like Logistic Regression, HRFLM, and HRFDT. This underscores its effectiveness in delivering precise and reliable AQI predictions.
{"title":"A Novel Approach for Air Quality Index Prognostication using Hybrid Optimization Techniques","authors":"Krishnaraj Rajagopal, Kumar Narayanan","doi":"10.54392/irjmt2427","DOIUrl":"https://doi.org/10.54392/irjmt2427","url":null,"abstract":"This research presents an innovative deep learning approach for forecasting the Air Quality Index (AQI), a crucial public health concern in both developed and developing countries. The proposed methodology encompasses four stages: (a) Pre-processing, involving data cleaning and transformation; (b) Feature Extraction, capturing central tendency, dispersion, higher order statistics, and Spearman's rank correlation; (c) Feature Selection, using a novel hybrid optimization model, Particle Updated Grey Wolf Optimizer (PUGWO); and (d) an ensembled deep learning model for AQI prediction, integrating a Convolutional Neural Network (CNN), an optimized Bi-directional Long Short-Term Memory (Bi-LSTM), and an Auto-encoder. The CNN and Auto-encoder are trained on the extracted features, and their outputs are fed into the optimized Bi-LSTM for final AQI prediction. Implemented on the PYTHON platform, this model is evaluated through R^2, MAE, and RMSE error metrics. The proposed HRFKNN model demonstrates superior performance with an R-Square of 0.961, RMSE of 11.92, and MAE of 10.29, outperforming traditional models like Logistic Regression, HRFLM, and HRFDT. This underscores its effectiveness in delivering precise and reliable AQI predictions.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"185 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A revolutionary composite material, blending Glass Fiber Reinforced Polymer (GFRP) with advanced nanofillers like TiO2 and MgO, showcases remarkable versatility in various industries due to its unique properties. The process involves precise control of key factors, including fiber stacking sequence (F.S.S) and nanofiller integration (MgO and TiO2). The vacuum bagging process is employed in the production of nanocomposite laminates. Experimental studies have been conducted to assess the performance of composites with and without nanofillers, with a specific focus on crucial mechanical properties, namely ultimate tensile strength (U.T.S), flexural strength (F.S), impact strength (I.S), and hardness (H). The Taguchi L9 orthogonal array design optimizes parameters and enhances mechanical properties. Comparisons reveal significant improvements with nanofillers, including a 31.96% increase in ultimate tensile strength and a substantial 68.43% enhancement in flexural strength. ANOVA results highlight the critical impact of fiber stacking sequence on ultimate tensile strength (63.65%), flexural strength (65.70%), and impact strength (9.30%), while nanofillers play a lesser role, contributing 11.71% to ultimate tensile strength, 2.66% to flexural strength, and 3.61% to impact strength. Notably, in composite hardness, nanofillers play a more significant role, contributing 39.22%, while the influence of fiber stacking sequence is lower at 3.29%.
{"title":"Exploring the Impact of TiO2 and MgO Nanoparticles on the Mechanical and Topographical Characteristics of Glass Fiber Reinforced Polymer (GFRP) Composites with Varied Lay-up Sequences: A Taguchi Analysis","authors":"Somaiah A, Anjaneya Prasad B, Kishore Nath N","doi":"10.54392/irjmt2426","DOIUrl":"https://doi.org/10.54392/irjmt2426","url":null,"abstract":"A revolutionary composite material, blending Glass Fiber Reinforced Polymer (GFRP) with advanced nanofillers like TiO2 and MgO, showcases remarkable versatility in various industries due to its unique properties. The process involves precise control of key factors, including fiber stacking sequence (F.S.S) and nanofiller integration (MgO and TiO2). The vacuum bagging process is employed in the production of nanocomposite laminates. Experimental studies have been conducted to assess the performance of composites with and without nanofillers, with a specific focus on crucial mechanical properties, namely ultimate tensile strength (U.T.S), flexural strength (F.S), impact strength (I.S), and hardness (H). The Taguchi L9 orthogonal array design optimizes parameters and enhances mechanical properties. Comparisons reveal significant improvements with nanofillers, including a 31.96% increase in ultimate tensile strength and a substantial 68.43% enhancement in flexural strength. ANOVA results highlight the critical impact of fiber stacking sequence on ultimate tensile strength (63.65%), flexural strength (65.70%), and impact strength (9.30%), while nanofillers play a lesser role, contributing 11.71% to ultimate tensile strength, 2.66% to flexural strength, and 3.61% to impact strength. Notably, in composite hardness, nanofillers play a more significant role, contributing 39.22%, while the influence of fiber stacking sequence is lower at 3.29%.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"341 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Uppalapati Vamsi Krishna, Srinivasa Rao G, Lavanya Addepalli, Bhavsingh M, V. Sd, Lloret Mauri Jaime
Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway characteristics. The utility of these techniques was evident in their ability to yield a diverse array of synthetic images, each representing different airway scenarios with high fidelity. A notable outcome of our study was the effective categorization of the initial image as "Class II" under the Mallampati Classification system. The augmented images further enhanced our understanding by exhibiting a spectrum of airway parameters. Moreover, our approach included training a Recurrent Neural Network (RNN) model on a dataset of CT images. This model, fortified with pseudo-labels created via K-means clustering, showcased its proficiency by accurately predicting airway assessment categories in various test scenarios. These results underscore the model's potential as a tool for swift and precise airway evaluation in clinical settings, marking a significant advancement in medical imaging technologies.
{"title":"Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation","authors":"Uppalapati Vamsi Krishna, Srinivasa Rao G, Lavanya Addepalli, Bhavsingh M, V. Sd, Lloret Mauri Jaime","doi":"10.54392/irjmt2425","DOIUrl":"https://doi.org/10.54392/irjmt2425","url":null,"abstract":"Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway characteristics. The utility of these techniques was evident in their ability to yield a diverse array of synthetic images, each representing different airway scenarios with high fidelity. A notable outcome of our study was the effective categorization of the initial image as \"Class II\" under the Mallampati Classification system. The augmented images further enhanced our understanding by exhibiting a spectrum of airway parameters. Moreover, our approach included training a Recurrent Neural Network (RNN) model on a dataset of CT images. This model, fortified with pseudo-labels created via K-means clustering, showcased its proficiency by accurately predicting airway assessment categories in various test scenarios. These results underscore the model's potential as a tool for swift and precise airway evaluation in clinical settings, marking a significant advancement in medical imaging technologies.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"236 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study delved into the electronic structure of Pyridine derivative 3-Chloro-2,6-difluoropyridin-4-amine (3C26D4A) using quantum-chemical computational calculations and employing the DFT/B3LYP/6-311++G(d,p) method and basis set. Spectroscopic, electronic, Mulliken population analysis and molecular electrostatic potential surface (MESP) calculations were carried out to gain deeper insights, shedding light on their bonding characteristics and reactive sites. The simulated electronic and frontier molecular orbitals (FMO) energy gaps of 3C26D4A in both polar (aniline, DMSO and methanol) and nonpolar (CCl4, chloroform, cyclohexane and toluene) confirm the stability and chemical reactivity. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy gap of 3C26D4A in the gas phase is found to be 6.0214 eV and shows low reactivity and stability as compared to the solvent phase. In parallel, in silico molecular docking investigated their promise as antimicrobial agents by targeting key enzyme DNA gyrase. The obtained binding energy revealed a significant inhibitory potential docking score of -4.07 kcal/mol.
{"title":"Exploration of Solvent Effects, Structural and Spectroscopic Properties, Chemical Shifts, Bonding Nature, Reactive Sites and Molecular Docking Studies on 3-Chloro-2,6-Difluoropyridin-4-Amine as a Potent Antimicrobial Agent","authors":"Kavi Karunya S, Jagathy K, Anandaraj K, Pavithra C, Manjula R","doi":"10.54392/irjmt2419","DOIUrl":"https://doi.org/10.54392/irjmt2419","url":null,"abstract":"This study delved into the electronic structure of Pyridine derivative 3-Chloro-2,6-difluoropyridin-4-amine (3C26D4A) using quantum-chemical computational calculations and employing the DFT/B3LYP/6-311++G(d,p) method and basis set. Spectroscopic, electronic, Mulliken population analysis and molecular electrostatic potential surface (MESP) calculations were carried out to gain deeper insights, shedding light on their bonding characteristics and reactive sites. The simulated electronic and frontier molecular orbitals (FMO) energy gaps of 3C26D4A in both polar (aniline, DMSO and methanol) and nonpolar (CCl4, chloroform, cyclohexane and toluene) confirm the stability and chemical reactivity. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy gap of 3C26D4A in the gas phase is found to be 6.0214 eV and shows low reactivity and stability as compared to the solvent phase. In parallel, in silico molecular docking investigated their promise as antimicrobial agents by targeting key enzyme DNA gyrase. The obtained binding energy revealed a significant inhibitory potential docking score of -4.07 kcal/mol.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140482235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conventional security systems are often plagued by inherent flaws, leading to frequent security breaches. To address these vulnerabilities, automated biometric systems have emerged, leveraging individuals' physiological and behavioural traits for precise identification. Among these biometric modalities, iris-based authentication is a highly reliable, distinctive, and contactless method for user recognition. This research endeavours to enhance the accuracy of iris liveness detection by combining features extracted from the TSBTC n-Ary (Thepade’s Sorted Block Truncation Coding) method with those derived from the Triangle Thresholding method. Two distinct datasets, namely IIIT Delhi and Clarkson 2015, have been employed to evaluate the efficacy of these combined features. The study involves extracting features from three sources: TSBTC, TSBTC+Triangle, and Triangle methods. These features are subsequently input into the WEKA tool, which employs various classifiers to assess accuracy. The findings of this investigation reveal a notable increase in the accuracy of Iris Liveness Detection (ILD) by incorporating handcrafted techniques like TSBTC in conjunction with the Thresholding method. In essence, this research underscores the potential for improving the robustness of security systems by harnessing the synergy of distinct biometric methods, thereby mitigating the shortcomings of conventional security systems and fortifying the foundations of secure user authentication.
{"title":"Iris Liveness Detection using Fusion of Thepade SBTC and Triangle Thresholding Features with Machine Learning Algorithms","authors":"Sudeep D. Thepade, Lomesh R. Wagh","doi":"10.54392/irjmt24110","DOIUrl":"https://doi.org/10.54392/irjmt24110","url":null,"abstract":"Conventional security systems are often plagued by inherent flaws, leading to frequent security breaches. To address these vulnerabilities, automated biometric systems have emerged, leveraging individuals' physiological and behavioural traits for precise identification. Among these biometric modalities, iris-based authentication is a highly reliable, distinctive, and contactless method for user recognition. This research endeavours to enhance the accuracy of iris liveness detection by combining features extracted from the TSBTC n-Ary (Thepade’s Sorted Block Truncation Coding) method with those derived from the Triangle Thresholding method. Two distinct datasets, namely IIIT Delhi and Clarkson 2015, have been employed to evaluate the efficacy of these combined features. The study involves extracting features from three sources: TSBTC, TSBTC+Triangle, and Triangle methods. These features are subsequently input into the WEKA tool, which employs various classifiers to assess accuracy. The findings of this investigation reveal a notable increase in the accuracy of Iris Liveness Detection (ILD) by incorporating handcrafted techniques like TSBTC in conjunction with the Thresholding method. In essence, this research underscores the potential for improving the robustness of security systems by harnessing the synergy of distinct biometric methods, thereby mitigating the shortcomings of conventional security systems and fortifying the foundations of secure user authentication.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"63 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140482412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thayala Sanker S, Arunachalam S, Raju S, Velayutham Pillai M, Kumaresan R
Serotonin, a neurotransmitter known for promoting feelings of happiness and optimism, was the subject of theoretical studies conducted using Gaussian software. In these experiments, the 6-311++G/B3LYP basis set was employed. The finite-field-based B3LYP/6-311++G (d,p) approach was used to compute the first-order hyper polarizability and associated properties of this chemical system. Additionally, a Natural Bond Orbital (NBO) analysis was conducted to assess the molecule's stability, taking into account hyper conjugative interactions and charge delocalization. Additionally, HOMO-LUMO energy levels were computed to assess whether a chemical exhibits electrophilic or nucleophilic characteristic. TD-DFT simulations were conducted to examine the electrical and optical characteristics of the material, including absorption wavelengths and excitation energy. Subsequently, the chemical compound's electrophilic or nucleophilic nature was determined by calculating the molecular electrostatic potential (MEP).
{"title":"A Quantum Chemical Investigation on Structural, Spectroscopic and Nonlinear Optical properties of an Organic Molecule Serotonin","authors":"Thayala Sanker S, Arunachalam S, Raju S, Velayutham Pillai M, Kumaresan R","doi":"10.54392/irjmt24112","DOIUrl":"https://doi.org/10.54392/irjmt24112","url":null,"abstract":"Serotonin, a neurotransmitter known for promoting feelings of happiness and optimism, was the subject of theoretical studies conducted using Gaussian software. In these experiments, the 6-311++G/B3LYP basis set was employed. The finite-field-based B3LYP/6-311++G (d,p) approach was used to compute the first-order hyper polarizability and associated properties of this chemical system. Additionally, a Natural Bond Orbital (NBO) analysis was conducted to assess the molecule's stability, taking into account hyper conjugative interactions and charge delocalization. Additionally, HOMO-LUMO energy levels were computed to assess whether a chemical exhibits electrophilic or nucleophilic characteristic. TD-DFT simulations were conducted to examine the electrical and optical characteristics of the material, including absorption wavelengths and excitation energy. Subsequently, the chemical compound's electrophilic or nucleophilic nature was determined by calculating the molecular electrostatic potential (MEP).","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"180 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140484697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. G, J. Chohan, Abhilakshmi M, Harikaran S, Shakthi dharshini M.B, Sai Nithin C.H
The need to discover novel methods for creating sustainable materials is growing due to the depletion of the Earth's resources and increasing environmental concerns. Several studies have focused on the handling of agricultural waste in an attempt to mitigate the ecological issues associated with agricultural debris removal. Large volumes of agricultural waste are generated annually, posing a significant challenge from both ecological and financial perspectives. In alignment with the principles of a sustainable economy, such waste can be employed as supplementary ingredients to produce high-value goods. The utilization of organic waste from agriculture has become indispensable for the development of sustainable and lightweight biopolymer-based composites. This brief review delves into the expanding field of lightweight agronomic surplus biomass materials suitable for environmental applications. It places particular emphasis on the utilization of biopolymers in creating these materials. The study explores how agricultural waste biomass can be sustainably repurposed and transformed into eco-friendly composite materials. It examines the innovations, materials, and methods contributing to this ecological trend, with a focus on the potential environmental benefits. This review highlights the progress achieved in the development of these hybrids, drawing attention to the numerous ways in which environmentally friendly biopolymer-based materials can be utilized.
{"title":"A Short Review on the Growth of Lightweight Agronomic Surplus Biomass Composites for Ecological Applications Using Biopolymers","authors":"V. G, J. Chohan, Abhilakshmi M, Harikaran S, Shakthi dharshini M.B, Sai Nithin C.H","doi":"10.54392/irjmt24111","DOIUrl":"https://doi.org/10.54392/irjmt24111","url":null,"abstract":"The need to discover novel methods for creating sustainable materials is growing due to the depletion of the Earth's resources and increasing environmental concerns. Several studies have focused on the handling of agricultural waste in an attempt to mitigate the ecological issues associated with agricultural debris removal. Large volumes of agricultural waste are generated annually, posing a significant challenge from both ecological and financial perspectives. In alignment with the principles of a sustainable economy, such waste can be employed as supplementary ingredients to produce high-value goods. The utilization of organic waste from agriculture has become indispensable for the development of sustainable and lightweight biopolymer-based composites. This brief review delves into the expanding field of lightweight agronomic surplus biomass materials suitable for environmental applications. It places particular emphasis on the utilization of biopolymers in creating these materials. The study explores how agricultural waste biomass can be sustainably repurposed and transformed into eco-friendly composite materials. It examines the innovations, materials, and methods contributing to this ecological trend, with a focus on the potential environmental benefits. This review highlights the progress achieved in the development of these hybrids, drawing attention to the numerous ways in which environmentally friendly biopolymer-based materials can be utilized.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"72 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140484119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sri Vijaya K, Gokula Krishnan V, Arul Kumar D, Prathusha Laxmi B, Yasaswi B
Uneven illumination has a significant impact on vision-based automatic parking systems, making it impossible to conduct a correct assessment of parking places in the presence of complicated picture data. In to address this issue, this work provides a deep learning-based system for visual recognition of parking spaces and picture processing. Artificial intelligence (AI) approaches can be used to identify a less expensive and easier-to-implement solution to the parking spot identification challenge, especially since the discipline of deep learning is reshaping the world. Using deep learning techniques, this study offers a dynamic, straightforward, and cost-effective algorithm for the detection of parking spots. In order to determine which parking spots are available and which are occupied, this method employs a Masked Region Based Convolutional Neural Network (MR-CNN) and the intersection over union approach. Cars in the training dataset were spaced more apart than those actually seen, which increased the accuracy of the identification between cars and parking spots. The AOA mechanism enhances the model's ability to focus on relevant regions within an image, improving accuracy in detecting parking spaces. This leads to precise identification of parking slots, reducing false positives and negatives. The sequence and quantity of parking spots, as well as the capacity to predict empty spots, were tested in a case study and found to be accurate. In the experimental results as the AOA based MR-CNN model stretched the accuracy as 98.50 and the recall value as 40.59 then the precision as 96.34 F1-measure as 57.95 correspondingly.
{"title":"AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment","authors":"Sri Vijaya K, Gokula Krishnan V, Arul Kumar D, Prathusha Laxmi B, Yasaswi B","doi":"10.54392/irjmt2418","DOIUrl":"https://doi.org/10.54392/irjmt2418","url":null,"abstract":"Uneven illumination has a significant impact on vision-based automatic parking systems, making it impossible to conduct a correct assessment of parking places in the presence of complicated picture data. In to address this issue, this work provides a deep learning-based system for visual recognition of parking spaces and picture processing. Artificial intelligence (AI) approaches can be used to identify a less expensive and easier-to-implement solution to the parking spot identification challenge, especially since the discipline of deep learning is reshaping the world. Using deep learning techniques, this study offers a dynamic, straightforward, and cost-effective algorithm for the detection of parking spots. In order to determine which parking spots are available and which are occupied, this method employs a Masked Region Based Convolutional Neural Network (MR-CNN) and the intersection over union approach. Cars in the training dataset were spaced more apart than those actually seen, which increased the accuracy of the identification between cars and parking spots. The AOA mechanism enhances the model's ability to focus on relevant regions within an image, improving accuracy in detecting parking spaces. This leads to precise identification of parking slots, reducing false positives and negatives. The sequence and quantity of parking spots, as well as the capacity to predict empty spots, were tested in a case study and found to be accurate. In the experimental results as the AOA based MR-CNN model stretched the accuracy as 98.50 and the recall value as 40.59 then the precision as 96.34 F1-measure as 57.95 correspondingly.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"174 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140492744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the dissociation behavior of water-soluble salts of Li and La and the unique behavior of Zr sources, resulting in the generation of Li+, La3+, and Zr4+ ions in aqueous solutions. The specific conductivity of calcined SG1 and SG2 displays temperature-dependent variations, with SG1 consistently exhibiting higher conductivity (2.08 x 10-4 S/cm) across the temperature range. The closed-packed structure facilitates the controllable mass transfer of lithium, enhancing ionic conductivity. The constructed LiFePO4/LLZO/AC device using these electrolytes demonstrates an impressive energy density of 1.95 Wh/kg and a power density of 144.92 W/kg, showcasing an excellent solid electrode-electrolyte interphase. Over 10,000 cycles, cyclic stability, with an average performance of 86%, underscores the potential of LLZO as a solid electrolyte for advanced energy storage devices. The sol-gel synthesis and densification strategy is a simple and effective method for obtaining lithium-rich LLZO electrolytes. The enhanced ionic conductivity and electrochemical performance of the solid-state device emphasize the practical viability of this approach, contributing to the sustainable development of advanced energy storage technologies.
{"title":"Effect Temperature for improving the Li-ion conductivity of Li7La3Zr2O12","authors":"Agnes Lakshmanan, Sabarinathan Venkatachalam","doi":"10.54392/irjmt2417","DOIUrl":"https://doi.org/10.54392/irjmt2417","url":null,"abstract":"This study investigates the dissociation behavior of water-soluble salts of Li and La and the unique behavior of Zr sources, resulting in the generation of Li+, La3+, and Zr4+ ions in aqueous solutions. The specific conductivity of calcined SG1 and SG2 displays temperature-dependent variations, with SG1 consistently exhibiting higher conductivity (2.08 x 10-4 S/cm) across the temperature range. The closed-packed structure facilitates the controllable mass transfer of lithium, enhancing ionic conductivity. The constructed LiFePO4/LLZO/AC device using these electrolytes demonstrates an impressive energy density of 1.95 Wh/kg and a power density of 144.92 W/kg, showcasing an excellent solid electrode-electrolyte interphase. Over 10,000 cycles, cyclic stability, with an average performance of 86%, underscores the potential of LLZO as a solid electrolyte for advanced energy storage devices. The sol-gel synthesis and densification strategy is a simple and effective method for obtaining lithium-rich LLZO electrolytes. The enhanced ionic conductivity and electrochemical performance of the solid-state device emphasize the practical viability of this approach, contributing to the sustainable development of advanced energy storage technologies.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"53 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140494229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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