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Verification and validation of Parallel Support Vector Machine algorithm based on MapReduce Program model on Hadoop cluster
From the recent years the large volume of data is growing bigger and bigger. It is difficult to measure the total volume of structured and unstructured data that require machine-based systems and technologies in order to be fully analyzed. Efficient implementation techniques are the key to meeting the scalability and performance requirements entailed in such scientific data analysis. So for the same in this paper the Sequential Support Vector Machine in WEKA and various MapReduce Programs including Parallel Support Vector Machine on Hadoop cluster is analyzed and thus, in this way Algorithms are Verified and Validated on Hadoop Cluster using the Concept of MapReduce. In this paper, the performance of above applications has been shown with respect to execution time/training time and number of nodes. Experimental Results shows that as the number of nodes increases the execution time decreases. This experiment is basically a research study of above MapReduce applications. 2013 IEEE. -
Virtual Reality in Tourism Industry within the Framework of Virtual Reality Markup Language
Virtual Reality (VR) technology has grown and emerged in the tourism industry. It offering immersive and interactive experiences, VR has transformed how people discover and interact with the VRML and people interact with different destinations. This article explores the use of VR in tourism, and focusing on Virtual Reality Markup Language (VRML) and its role in showcasing the evolution of head-mounted displays (HMDs) and the various applications of VR. It emphasizes how VR can improve travel experiences, aid in destination planning, preserve cultural heritage, support adventure tourism, and revolutionize destination marketing. The article also gives the challenges and limitations faced by VR in tourism, as well as future trends and opportunities in the field. The article impact of VR on the tourism industry and discusses the combination of Augmented Reality (AR) and VR to create virtual art exhibitions in physical and online spaces. Additionally, it provides insights into the future of VR, AR, and Mixed Reality (MR), the use of VRML, and the development of 3D modeling for creating virtual environments that help users achieve learning objectives. 2024 IEEE. -
Vision Based Vehicle-Pedestrian Detection and Warning System
Road Sense must be respected and obeyed by both the pedestrian and the driver. Moreover, urbanization has led to a steadfast rise in the fleet of vehicles, their speed, as well as non-compliance with road safety measures, and other such factors have provoked an inescapable increase of accidents in road traffic involving pedestrians. Pedestrian collisions can be predicted and prevented. At the very basic, there has to be vehicle and pedestrian detection along with speed estimation, which can be further applied to Vehicle-Pedestrian Collisions and various emerging fields like Industrial Automation, Transportation, Automotive, Security/Surveillance, or in Dangerous environments. This paper reviews the literature on vehicle and pedestrian detection based on two significant categories: pre-processing phase and detection phase, with a detailed comparative analysis. The papers reviewed cover video-based surveillance systems. 2022 IEEE. -
Visual Symphony for Swift and Accurate Object Detection in Choreographed Deck of Cards
The Convolutional Neural Network model used for playing card recognition and categorization, offering trustworthy data regarding the suits of playing cards hearts, diamonds, clubs and spades as well as the corresponding numerical or alphabetical values. The model is built on a sophisticated dataset that guarantees high levels of precision for nearly all sorts of graphical representations and playing card scenarios. A wide range of entertainment andgames bands canuse the CNN idea. As aresult, the CNN-trained model is an excellent alternative for many different kinds of applications, including virtual reality games and card game automation, due to its capacity to extract and retain complex features from card pictures for accurate object identification. As a result, this research has shown how crucial deep learning models like CNNs are for enhancing computervision systems' suitability for real-world scenarios requiring precise and quick identification of objects. As a result, the suggested CNN-based approach offers a great chance to enhance cardidentification system performance and promoteadvancements in memory and gaming technology. 2024 IEEE. -
VLSI Implementation of Area-Error Optimized Compressor-Based Modified Wallace Tree Multiplier
Approximate multiplier designs can improve their energy efficiency and performance with only a slight loss in accuracy by using approximate arithmetic circuits. This method is appropriate for applications where an approximative answer is acceptable because it uses a range of calculation approaches to those priorities, returning a potentially erroneous result above one that is assured to be exact. The basic idea underlying approximate computing is that, while accurate calculation may require a lot of resources, bounded approximation can result in considerable speed and energy efficiency advantages without sacrificing accuracy. The approximate 4:2 compressor and exact compressors, as well as half adders and full adders, make up the proposed approximate multiplier. The steps of the multiplier architecture are optimised using the recently suggested modified Wallace Tree Multiplier Architecture. When compared to previous designs, the proposed multiplier architecture can generate outcomes with the least amount of inaccuracy. The multiplier architecture is also finished in just two steps. The Modified Wallace Tree Architecture used in the suggested approximate multiplier excels by providing an error rate of 71.80% and a mean error of 173.82. As a result, the mean ? error Product improved by 10%, the error rate improved by 23.3%, and the mean error increased by 31.04%. This is accomplished by the proposed approximate multiplier with a small increase of 22.36% in total power consumption. 2023 IEEE. -
VNPR system using artificial neural network
Vehicle number plate recognition (VNPR) is a technique used to extract the license plate from a sequence of images. The extracted information in the database can be used in the applications like electronic payment systems such as toll payment, parking lots etc. An effective VNPR can be implemented based on the quality of the acquired images. It is used for real time application and it has to recognize the number plates of all types under different environmental conditions. Different algorithms has been used which depends on the features present in the images. It should be generalised to extract different types of license plate from the images. In this paper we propose a new method which is robust enough to recognize the characters from the number plates with help of artificial neural network. This algorithm is practical for the front view and rear view of orientation of the vehicle. 2016 IEEE. -
Voltage stability analyis of radial distribution systems by considering load models
Generally, the distribution systems have served for different types of loads like commercial, industrial, residential, agriculture and municipality etc. and diverse changes in consumption pattern occur at any part of the network at any time of the day. During light loading condition, the voltage profile can increase and vice versa for peak loading condition. Under these circumstances, it is worthwhile to understand the voltage stability for planning of any Volt/VAr controls. This paper has presented the voltage stability analysis of 12-bus and 85-bus standard radial distribution systems using line stability index. Different load models have been taken and under each model, the system performance as well as its stability discussed. The focal points are suitable for planning studies like Volt/VAr controls, optimal location of Distribution Generation (DG) or load shedding etc. 2018 IEEE. -
Voltage stability analysis using L-index under various transformer tap changer settings
Voltage stability is a major problem in power system which depended on many factors like improper load forecasting, generator outage, line fault and shortage of reactive power supply etc. For a secure and economic power system operation voltage stability should be maintained within permissible limit. Voltage stability is a measure of whole power system quality. Voltage stability studies can done by analyzing reactive power production, transmission of power and consumption. In this paper voltage stability analysis of an IEEE 14 bus system is done by calculating L-index of the buses. From load flow studies optimized voltage is chosen, and by using these voltage values L-index is calculated. From the calculated L-index values we can find out vulnerable buses. How the transformer tap changing effect the voltage stability is also calculated here. 2016 IEEE. -
War Strategy Optimization for Optimal Integration of Public Fast Charging Stations in Radial Feeders
In light of rising pollution and global warming, there is need for raising the acceptance rate of electric vehicles (EVs) across the globe for sustainable and clean transportation. However, low-voltage electrical distribution networks (EDN) are necessary to provide the electrical power needed to charge the EV batteries. Due to their radial construction and high r/x ration branches; these networks typically suffer from significant energy losses, inadequate voltage profiles, and low stability margins. Therefore, the performance of EDNs shouldnt be further compromised by the incorporation of EV charging stations. In light of this, this work presents a unique heuristic war strategy optimization (WSO) for integrating fast charging stations (FCS) as efficiently as possible utilizing the voltage stability index (VSI). The effect of equivalent EV load penetration in EDS is initially evaluated in terms of loss, voltage profile, and voltage stability for a certain number of EVs. Simulations are executed for IEEE 15-bus system for three different scenarios. The technological advantages seen in the simulations illustrate the efficiency of the suggested technique for real-time adaptation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Water Demand Prediction Using Support Vector Machine Regression
Water is a critical resource for sustainable economic and social development of a country. To maintain health hygiene, energy agricultural products, and the environment management water plays a key role. Water demand prediction is essential to analyze the requirement that indicate emergency state for water management decisions. This paper explores the water usage data for dairy plants to understand the spatial and temporal patterns for future water requirements, to optimize the water demand estimation. It uses concept of Machine learning algorithms to compare and achieve an effective and reliable system for water prediction. 2019 IEEE. -
Waveform Analysis and Feature Extraction from Speech Data of Dysarthric Persons
Speech recognition systems provide a natural way of interacting with computers and serve as an alternative to the more popular but less intuitive peripherals (input / output devices). Tools employing the techniques of Automatic Speech Recognition (ASR) can be extended to serve people with speech disabilities so that they can overcome the difficulties faced in their interaction with general public. An attempt is made here to achieve this goal by mapping the distorted speech signals of people with severe levels of dysarthria to that of a normal speech and/or less severe dysarthric speech. The analysis is carried out by comparing the speech waveforms of the people with and without communication disorders and then extracting the features from the audio files. The differences in time, duration, frequency and PSD are used to facilitate the mapping of unintelligible speech data to intelligible ones. When reasonable accuracy levels are achieved in this mapping, the normal voice can be used as the substitute / surrogate of the original distorted voice. 2019 IEEE. -
Wavelet packet transform based fusion of misaligned images
This paper proposes an image fusion method based on wavelet packet transform (WPT) for images with misaligned region of interest, which finds wide application in target recognition and feature extraction. The region of interest of the images are first aligned and then fused in the transform domain. The various no-reference parameters such as standard deviation (SD), spatial frequency (SF) are measured for the fused image. The result obtained from this method is compared with the other methods such as image fusion using discrete wavelet transform (DWT), stationary wavelet transform and guided filtering. It is evident from the simulation results that the two parameters are high for the fused image using wavelet packet transform. 2016 IEEE. -
Wearable Leaf-Shaped Slotted Antenna Including Human Phantom for WBAN Applications
A 5.8 GHz leaf-shaped slotted antenna for Wireless Body Area Network (WBAN) applications is presented in this piece of content. The leaf structure includes tri leaves, having a complete ground plane at the lowest floor and a central circle slot. The suggested antenna is 60 mm by 60 mm by 1.16 mm in total dimensions. The ISM (Industrial Science and Medical) band frequency of 5.8 GHz is covered by this antenna's radiation range of 5.5 to 6.4 GHz. The radiated pattern, efficiency, S11 magnitude and gain were the different attributes of the leaf-patterned slot antenna. The creation of a stylish leaf-shaped antenna that can be incorporated into clothing designs is the main goal of this project. This antenna may be used in difficult situations because of its flexible base and conductive fabric. The method considers the needs of wearable antennas, such as the impact of human interactions on this antenna, as well as the opposite. 2023 IEEE. -
Weather Forecasting Accuracy Enhancement Using Random Forests Algorithm
In today's world, weather forecasting is essential for decision-making in a variety of fields, including agriculture, transportation, and disaster preparedness. It's not simple to make weather predictions. Today, both in business and academia, data analytics is growing in importance as a tool for decision-making. The adoption of data-driven concepts is for our graduates, enhancing their marketability. Data Analytics us a study belonging to science that analyses gathered raw data, which makes conclusions about the particular information. Data analytics has been used by many sectors recently, such as hospitality, where this industry can collect data, find out where the problem is, and manage to fix the problem. Nominal, ordinal, interval, and ratio data levels are the four types of data measurement. Applications of data analytics can be found in many industries, including shipping and logistics, manufacturing, security, education, healthcare, and web development. Any business that wants to succeed in the modern digital economy should make analytics a core focus. To make such data meaningful, a transformation engine was used with types from several sources. Ironically, this has made analytics harder for businesses. As businesses employ more platforms and applications, the amount of data available has grown tremendously. This article focuses on different applications of data analytics in the modern world. Weather forecasting is a highly intricate and multifaceted process that draws upon data from various sources. It relies on a combination of scientific studies and sophisticated weather models to decipher the vast amount of information available. 2023 IEEE. -
Web Platforms for Fintech Products
Internet marketing and digital marketing are not synonymous in the minds of the majority of the population, yet this may not be true. Given the rise in popularity of digital marketing as a marketing tactic, it is critical to comprehend the distinctions between the two methods. Even while it should be evident that they might be connected, there is very little difference between them. Internet marketing is merely a subclass of digital marketing, as well as the extent of digital marketing encompasses much more than internet marketing. This paper discussed digital marketing technologies, as well as the advantages and disadvantages of employing digital marketing and digital finance tools in general. In order to remain competitive, businesses must overcome obstacles and seize possibilities presented by digital marketing technologies. Lastly, it's critical to prioritise digital marketing and make use of digital finance techniques in order to maintain a good performance without wasting time or money. 2022 IEEE. -
Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images
Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. 2023 IEEE. -
Whale Optimization Based Approach toCompress andFasten CNN forCrop Disease andSpecies Identification
In recent years deep learning and machine learning have been widely researched for image based recognition. This research proposes a simplified CNN with 3 layers for classification from 39 classes of crops and their diseases. It also evaluates the performance of pre-trained models such as VGG16 and ResNet50 using transfer learning. Similarly traditional Machine Learning algorithms have been trained and tested on the same dataset. The best accuracy using proposed CNN was 87.67% whereas VGG16 gave best accuracy of 91.51% among Convolution Neural Network models. Similarly Random Forest machine learning method gave best accuracy of 93.02% among Machine Learning models. Since the pre-trained models are having huge size hence in order to deploy these solutions on tiny edge devices compression is done using Whale Optimization. The maximum compesssion was obtained with VGG16 of 88.19% without loss in any performance. It also helped betterment of inference time of 44.13% for proposed CNN, 56.76% for VGG16 and 63.23% for ResNet50. 2023, Springer Nature Switzerland AG. -
Wheat Yield Prediction using Temporal Fusion Transformers
In precision framing, Machine Learning models are an essential decision-making tool for crop yield prediction. They aid farmers with decisions like which crop to grow and when to grow certain crops during the sowing season. Many Machine Learning algorithms have been used to support agriculture yield prediction research, but it is observed that Deep Learning models outperform the benchmark Machine Learning algorithms with a significant difference in accuracy. However, though these Deep Learning models perform better, they are not preferred or widely used in place of Machine Learning models. This is because Deep Learning methods are black box methods and are not interpretable, i.e., they fail to explain the magnitude of the impact of the features on the output, and this is unsuitable for our use case.In this paper, we propose using Temporal Fusion Transformer (TFT), a novel approach published by Google researchers for wheat yield prediction viewed as a Time Series Forecasting problem statement. TFT is the state-of-the-art attention-based Deep Learning architecture, which combines high-performance forecasting with interpretable insights and feature importance. We have used TFT to perform wheat yield prediction and compare its performance with various Machine Learning and Deep Learning algorithms. 2023 IEEE. -
Wide band cascade RF amplifier for 0.01GHz to 6 GHz application
This paper presents a design of wide band cascade RF amplifier for 0.01 GHz to 6 GHz application using Hybrid Microwave Integrated (MIC) Technology. Wideband amplifier provides ultra-flat gain response of 1 dB for 4 GHz bandwidth and 3 dB for 6 GHz bandwidth. A coplanar wave guide (CPWG) is fabricated using printed circuit board technology and used for RF transmission line topology to convey microwave frequency signals. The output power at 1 dB compression is 17 dBm while the high gain is 22 dBm. The return loss shows below minimum -10 dB for all frequency and amplifier have good linearity and stability. The proposed amplifier can be used for L, S, and C band applications. 2019 IEEE -
Wideband Compact Two-Element Millimeter Wave MIMO Antenna for Communication Systems
This article presents the wide band two-element MIMO antenna with an I-shaped decoupling structure in the ground plane. It is to enhance the isolation on the MIMO antenna. The dimension is 7.5 17.5 mm2. The measured bandwidth is 2 GHz (22.25-24.25 GHz) with a maximum gain of 4.5 dBi and bidirectional radiation. MIMO antenna satisfies three diversity metrics. 2024 IEEE.