Browse Items (11810 total)
Sort by:
-
Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant's Jaundice Symptoms
One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive. 2021 Debabrata Samanta et al. -
Optimized task group aggregation-based overflow handling on fog computing environment using neural computing
It is a non-deterministic challenge on a fog computing network to schedule resources or jobs in a manner that increases device efficacy and throughput, diminishes reply period, and maintains the system well-adjusted. Using Machine Learning as a component of neural computing, we developed an improved Task Group Aggregation (TGA) overflow handling system for fog computing environments. As a result of TGA usage in conjunction with an Artificial Neural Network (ANN), we may assess the models QoS characteristics to detect an overloaded server and then move the models data to virtual machines (VMs). Overloaded and underloaded virtual machines will be balanced according to parameters, such as CPU, memory, and bandwidth to control fog computing overflow concerns with the help of ANN and the machine learning concept. Additionally, the Artificial Bee Colony (ABC) algorithm, which is a neural computing system, is employed as an optimization technique to separate the services and users depending on their individual qualities. The response time and success rate were both enhanced using the newly proposed optimized ANN-based TGA algorithm. Compared to the present works minimal reaction time, the total improvement in average success rate is about 3.6189 percent, and Resource Scheduling Efficiency has improved by 3.9832 percent. In terms of virtual machine efficiency for resource scheduling, average success rate, average task completion success rate, and virtual machine response time are improved. The proposed TGA-based overflow handling on a fog computing domain enhances response time compared to the current approaches. Fog computing, for example, demonstrates how artificial intelligence-based systems can be made more efficient. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Optimized score card for mentoring students using artificial intelligence and methods thereof /
Patent Number: 202011040658, Applicant: Dr Priti Verma.
The invention discloses a mentoring system capable of improving student's performance in the field of Learning in Theoretical, practical, behavioral, sports, cultural activities and life skills for the betterment of the life of an individual. The feedback system has the capability of generating feedback with better accuracy and hence easily identifying the areas of weaknesses and strengths of the students. -
Optimized score card for mentoring student using artificial intelligence and methods thereof /
Patent Number: 202011040658, Applicant: Dr Priti Verma.
The invention discloses a mentoring system capable of improving student™s performance in the field of Learning in Theoretical, practical, behavioral, sports, cultural activities and life skills for the betterment of the life of an individual. -
Optimized production of keratinolytic proteases from Bacillus tropicus LS27 and its application as a sustainable alternative for dehairing, destaining and metal recovery
The present study describes the isolation and characterization of Bacillus tropicus LS27 capable of keratinolytic protease production from Russell Market, Shivajinagar, Bangalore, Karnataka, with its diverse application. The ability of this strain to hydrolyze chicken feathers and skim milk was used to assess its keratinolytic and proteolytic properties. The strain identification was done using biochemical and molecular characterization using the 16S rRNA sequencing method. Further a sequential and systematic optimization of the factors affecting the keratinase production was done by initially sorting out the most influential factors (NaCl concentration, pH, inoculum level and incubation period in this study) through one factor at a time approach followed by central composite design based response surface methodology to enhance the keratinase production. Under optimized levels of NaCl (0.55 g/L), pH (7.35), inoculum level (5%) and incubation period (84 h), the keratinase production was enhanced from 41.62 U/mL to 401.67 9.23 U/mL (9.65 fold increase) that corresponds to a feather degradation of 32.67 1.36% was achieved. With regard to the cost effectiveness of application studies, the crude enzyme extracted from the optimized medium was tested for its potential dehairing, destaining and metal recovery properties. Complete dehairing was achieved within 48 h of treatment with crude enzyme without any visible damage to the collagen layer of goat skin. In destaining studies, combination of crude enzyme and detergent solution [1 mL detergent solution (5 mg/mL) and 1 mL crude enzyme] was found to be most effective in removing blood stains from cotton cloth. Silver recovery from used X-ray films was achieved within 6 min of treatment with crude enzyme maintained at 40 C. 2023 Elsevier Inc. -
Optimized Multi-Scale Attention Convolutional Neural Network for Micro-Grid Energy Management System Employing in Internet of Things
The combination of micro-grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi-scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN-MGEMS-IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias-aware Ensemble Kalman Filter (RBAEKF) for pre-processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN-MGEMS-IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi-micro grid systems (IoT-PEM-MMS), a micro-grid system infrastructure implementing IoT for efficient energy management in buildings (MSII-IoT-EEM) and a hybrid deep learning-based online energy management scheme for industrial microgrids (HDL-OEM-IM). The findings highlight the impact of the proposed OMACNN-MGEMS-IoT method in enhancing energy efficiency and cost-effectiveness in microgrid systems. 2025 John Wiley & Sons Ltd. -
Optimized Metamaterial Loaded Square Fractal Antenna for Gain and Bandwidth Enhancement
This paper presents a report on the enhanced performance of an optimized metamaterial loaded square fractal antenna (OMSFA). The design and simulation of the antenna was carried out using Electronic Desk Top HFSS version 18.2 software. The antenna layer spreads over an area of 23 square millimeter on a FR4 substrate whose dielectric permittivity is 4.4. The substrate size measures an area of 46 mm X 28 mm, with 1.6 mm thickness. Also the design includes a microstrip feed and truncated ground. The antenna resonates well with a deep return loss of-38.9 dB in a broad bandwidth of 3.2 GHz (128 %) between 2 GHz and 5.2 GHz. The OMSFA produces enhanced gain of 9.8 dB at 2.5 GHz. The radiation is more focused due to the effect of metamaterial loading. The proposed antenna is recommended for wireless application in the lower region (S band) of the microwave spectrum. 2018 IEEE. -
Optimized Load Balancing Technique for Software Defined Network
Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission media loads. These loads also contribute to decreasing the performance of Software- Defined Networks. This work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the load. We proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network load. The simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques. 2022 Tech Science Press. All rights reserved. -
Optimized heat transport in Marangoni boundary layer flow of a magneto nanomaterial driven by an exponential interfacial temperature distribution
In a small boundary layer of the fluid interface, the temperature distribution deviates from being linear with the spatial coordinate and exhibits an exponential form. Hence, the Marangoni convective flow of a nanoliquid driven by an exponential interfacial temperature distribution is modeled in this study. Due to practical applicability, the working fluid is chosen to be ethylene glycol-based magnesium oxide nanoliquid, which is modeled using experimentally estimated properties. In the system, the external effects of an inclined magnetism, thermal radiation, and an internal heat source are considered. Heat transport is rigorously analyzed using an empirical model, which is estimated using the robust response surface methodology (RSM) to find the optimal working conditions and to estimate the sensitivity. The modeled problem is simulated numerically using the finite difference-based scheme and a parametric analysis is conducted to study the effect of magnetic field, inclination of magnetic field, radiation, and internal heat source parameters. The internal heat generation (increase of 0.94%) factor dominates the augmentation in the thermal field but at some distance, the thermal radiation factor has a predominant impact (58.99%). The inclination angle of the magnetic field has a prominent decremental impact on the velocity profile. Also, the radiative heat flux enhances the temperature profile. Optimal working conditions are estimated to be with a magnetic inclination of 10 and using a liquid with 0.25% volume fraction of 100 nm. This study finds applicability in crystal growth, drying silicon wafers, and heat exchangers. 2022 Wiley-VCH GmbH. -
Optimized handwritten character recognition using artificial neural network
Handwritten character recognition (HCR) plays important role in the modern world and is one of the focused area of research in the field of image processing and pattern recognition. Handwritten character recognition refers to the process of conversion of hand-written character into printed/word file character which can immensely improve the interface between man and machine in numerous application. It is difficult to process with great variations in writing styles, different size and orientation angle of the character that are existing. Also segmentation of cursive handwritten text is difficult as the edges cant be detected easily. There are numerous approaches to recognize handwritten data. The images are acquired using a digital camera or scanner and stored in standard format like JPG, PNG etc. The second stages include pre-processing techniques like Binarization, Skeletonization, thinning, resizing the image and segmentation. In our work we mainly concentrated on extracting statistical features of alphabets like mean, variance, standard deviation, Skewness and kurtosis, which differentiates a character from another. We used feed forward algorithm to train Artificial Neural Network (ANN). The features of input character after pre-processing are fed into ANN. A database of 650 samples is created to test input samples for recognition of character by neural net-work. The Experimental results that we have achieved show 88.46 % accuracy rate with minimum time taken for training. IJSTR 2020. -
Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication
The dissertation titled ???Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication??? is the compilation of all efforts taken and tasks completed in order to implement an optimal handoff method in Vehicular Ad-hoc Network communication.Wireless communication technologies have been improving exponentially. Ad-hoc networks can form a network of wireless nodes anywhere and they are not bound by the limitations of a static infrastructure. This enhances the ability of mobile nodes to communicate with each other even in situations where a defined architecture is absent. Vehicular Ad-hoc Networks (VANETs) has its applications in dynamic environments that involve nodes with high mobility. The nodes frequently move between the coverage areas of different access points. This increases the chance of link breakage and new link formation in communication network. Handoff is a process that helps in transferring the session details between one access point to another whenever the node is about to move away from a currently serving access point. Many handoff methods have been proposed but a majority of them utilize just a particular attribute of a network to employ the channel selection process. This process of network selection would be skewed as other attributes of a network play important roles in improving its overall efficiency. Multiple Attributes Decision Making (MADM) methods make use of different attributes in order to perform the network selection process. Use of MADM methods help in selecting optimal access points that can provide services to the nodes for a longer duration. In the proposed system, MADM methods have been utilized to modify existing protocols in order to optimize their approach for handoff operations. Various scenarios involving vehicular nodes and different access points have been considered in order to improve the efficiency of the proposed system across applications. The proposed handoff mechanism follows a proactive approach where the target access points are selected before the mobile node reaches the edge of its coverage area. This leads to a seamless transition of the communication channels. Based on the client/access point information stored in the data log, optimal access points which are situated along the direction of the node???s movement can be selected. NS2 and SUMO have been implemented to simulate mobile environments that accommodate handoff operations. -
Optimized green synthesis of ZnO nanoparticles: evaluation of structural, morphological, vibrational and optical properties
In this study, leaf extracts of Aloe vera (AV), Azadirachta indica (AI), and Amaranthus dubius (AD) were used to synthesize zinc oxide nanoparticles utilizing a simple green synthesis process. The structural, optical, band energy, size, surface area, and shape of as-prepared nanoparticles were studied using analytical techniques. The hexagonal phase was revealed by XRD studies for all three samples: AV-ZnO, AI-ZnO, and AD-ZnO, with crystallite sizes of 35.8nm, 30.83nm, and 33.1nm, respectively. The UVVisible spectra of AV-ZnO, AI-ZnO, and AD-ZnO exhibit the characteristic absorption in the range of 200 to 450nm, and the band gap energy was found to be 3.10eV, 3.12eV, and 3.07eV, respectively. FESEM and TEM studies revealed that the ZnO NPs are rod-shaped with a roughly spherical appearance. EDAX analysis confirmed the presence of zinc and oxygen and indicates that the formed product is a pure phase of ZnO NPs. Increased antibacterial activity was noted for AV-ZnO, AI-ZnO, and AD-ZnO against gram-negative (Klebsiella pneumonia, Shigella dysenteriae), gram positive (Staphylococcus aureus, and Bacillus) bacterial strain. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Optimized gateway oriented unicast and multicast routing for multi hop communication network
Networking and communication in an infrastructure less environment had brought interest in the development of mobile ad hoc networks (MANET). Growth of technologies in years has increased the size of the network and its applications. In larger MANET, it becomes important to maintain the quality of performance for increased overhead scenarios. Grouping of network optimizes the workload with reduced overhead for routing and maintenance in larger MANET. Mobile nodes are grouped by the use of clustering algorithms. Once the MANET environment is formed, the network needs appropriate architecture and methods to have efficient and effective transactions. Mobility, energy, selection of cluster head and gateway are parallel related with efficiency metrics of the network optimizing these parameter helps to increase performance of network in terms of higher packet delivery ratio, less energy consumption and jitter. This work proposes a routing architectural algorithm especially for very large network to get high-quality performance. The proposed method uses unicasting and multicasting approaches in an optimized way for routing and maintenance of the network. Analysis results prove that the proposed model has performed with higher packet delivery ration of 1.17% with a reduced jitter of 0.0014 s. Springer Nature Singapore Pte Ltd 2017. -
Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect
Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Optimization-Based Cash Management Model for Microfinance Applications Using GSA and PSO
Banks and businesses use cash as a means for exchange in finance on a regular basis to please customers. Making decisions about cash management can be challenging because banks must keep significant sums of cash in order to sustain high levels of client satisfaction. In this paper, linear PSO and GSA models are given for estimating the daily cash demand of a bank by taking into account the variables Year of Reference (RY), Years Month (My), Months Day (Dm), Days Week (Dw), Payday Effect Salary (Se), and Holiday Effect (He). Using PSO and GSA in MATLAB, the algorithms for estimating both the model coefficients for short term are implemented from the real data of a specific bank branch. The proposed system's overall cost is minimized using a fitness function. It was discovered that the results are in good accord with the observed data and that the PSO-based cash management model outperformed other models with superior accuracy. The models are then used for future cash management for validation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Optimization of multiple responses using overlaid contour plot and steepest methods analysis on hydroxyapatite coated magnesium via cold spray deposition
In this work, sequential optimization strategy based statistical design was employed to enhance the mechanical properties of hydroxyapatite coatings onto a pure magnesium substrate using a cold spray technique. A fractional factorial design (24-1) was applied to elucidate the process parameters that significantly affected the mechanical properties of the coating samples. Standoff distance, surface roughness, and substrate heating temperature were identified as important process parameters affecting thickness, nanohardness, and the elastic modulus of the coating sample. The overlaid method analysis was employed to determine tradeoff optimal values from multiple regressive equations. Then, finally, steepest method analysis was used to reconfirm and relocate the optimal domain from which the factor levels for maximum mechanical properties of the coating were determined at 49.77mm standoff distance, 926.4grit surface roughness, and 456C substrate heating temperature, which can accommodate the optimum requirements for the cold spray process with a coating of 49.77?m thickness, 462.61MPa nanohardness, and 45.69GPa elastic modulus. Scanning electron microscopy revealed that a short standoff distance, high surface roughness, and high substrate temperatures improved the bond between the coated layers and substrates. 2015 Elsevier B.V. -
Optimization of heat transfer in the thermal Marangoni convective flow of a hybrid nanomaterial with sensitivity analysis
The heat transfer rate of the thermal Marangoni convective flow of a hybrid nanomaterial is optimized by using the response surface methodology (RSM). The thermal phenomenon is modeled in the presence of a variable inclined magnetic field, thermal radiation, and an exponential heat source. Experimentally estimated values of the thermal conductivity and viscosity of the hybrid nanomaterial are utilized in the calculation. The governing intricate nonlinear problem is treated numerically, and a parametric analysis is carried out by using graphical visualizations. A finite difference-based numerical scheme is utilized in conjunction with the 4-stage Lobatto IIIa formula to solve the nonlinear governing problem. The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The mono and hybrid nanomaterial flow fields are compared. The hybrid nanomaterial possesses enhanced thermal fields for nanoparticle volume fractions less than 2%. The irregular heat source and the thermal radiation enhance the temperature profiles. The high level of the thermal radiation and the low levels of the exponential heat source and the angle of inclination (of the magnetic field) lead to the optimized heat transfer rate (Nux = 7.462 75). 2021, Shanghai University. -
Optimization of graded catalyst layer to enhance uniformity of current density and performance of high temperature-polymer electrolyte membrane fuel cell
The optimal use of catalyst materials is essential to improve the performance, durability and reduce the overall cost of the fuel cell. The present study is related to spatial distributions of current and overpotential for various graded catalyst structures in a high temperature-polymer electrolyte membrane fuel cell (HT-PEMFC). The effect of catalyst gradient across the catalytic layer (CL) thickness and along the channel and their combination on cell performance and catalyst utilization is investigated. The graded catalytic structure comprises two, three, or multiple layers of catalyst distribution. For a total cathode catalyst loading of 0.35 mg/cm2, higher loading near the membrane presents improved cell performance and catalyst utilization due to reduced limitations caused by oxygen and ion diffusions. However, non-uniformity in the current distribution is significantly increased. The increase in the catalyst loading along the reactant flow provides a substantially uniform current density but lower cell performance. The synergy of varying catalytic profiles across the CL thickness and along the cathode flow direction is investigated. The results emphasize the importance of a rational design of cathode structure and mathematical functions as a strategic tool for functional grading of a CL towards improved uniform current distribution and catalyst utilization. 2021 Hydrogen Energy Publications LLC -
Optimization of Friction Stir Welding Parameters Using Taguchi Method for Aerospace Applications
The current research work investigated the optimization of the input parameters for the friction stir welding of AA3103 and AA7075 aluminum alloys for its applications in aerospace components. Friction stir welding is rapidly growing welding process which is being widely used in aerospace industries due to the added advantage of strong strengths without any residual stresses and minimal weld defects, in addition to its flexibility with respect to the position and direction of welding. Thus, the demand for this type of welding is very high; however, the welding of aluminum alloys is a key aspect for its use in aircraft components, particularly with respect to bracket mounting frames, braces and wing components. Henceforth in the current work, research is focused on optimization of welding of aluminum alloys, viz. AA 3103 and AA 7075; AA 3103 is a non-heat treatable alloy which is having good weldability, while AA 7075 is having higher strength. Therefore, the welding of these aluminum alloys will produce superior mechanical properties. The optimization of input parameters was accomplished in this work based on L9 orthogonal array designed in accordance with Taguchi methodusing which the friction stir welding experiment was conducted. There were nine experimental runs in total after formulating the L9 orthogonal array table in Minitab software. The input parameters which were selected for optimization weretool rotation speed, feed rate, tool pin profile. The output parameters which were optimized were hardness, tensile strength and impact strength. In addition, the microstructure of the fractured surfaces of the friction stir welded joint was analyzed. It was found from the optimization of the process parameters that strong friction stir welded joints for aerospace applications can be produced at an optimized set of parameters of tool rotational speed of 1100rpm, traverse speed of 15mm/min with a FSW tool of triangular pin profile of H13 tool steel material. 2020, Springer Nature Singapore Pte Ltd. -
Optimization of Friction Stir Welding Parameters for the Optimum Hardness of AlCu Butt Joints Using the Taguchi Method
In the present study, the base plates made of alloys AA6101 and C11000 (each 5 mm thick) were welded bythe FSW technique using a hardened OHNS steel weld tool. The percentage contribution of the input process parameters, such as tool rotational speed in rpm, feed rate in mm/min, and tool pin offset in mm, on the output parameter joint hardness, were examined using the experimental design Taguchi L9 and ANOVA numerical tool analysis. From the optimization method, at 1000rpm tool rotational speed, 40mm/min feed rate and weld tool pin toward AA6101 alloy side will have the highest hardness. The tool rotational speed experiences a maximum significant impact on the joint hardness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.