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Full Reference Image Quality Assessment (FR-IQA) of Pre-processed Structural Magnetic Resonance Images
Deep learning-based Artificial Intelligence algorithms have surpassed human-level performance in many fields including medicine. Specifically in diagnosis using radiology images, deep neural networks empowered AI to excel by educating intricate nonlinear relationships which is a core part of the complicated radiology problems. However, these models require a massive amount of quality data for training. The accuracy of the deep learning model is based on the amount of training data and the quality of the trained data being fed. So, preprocessing the data from different capturing devices is inevitable. This study aimed to highlight some of the image quality metrics that can be used to quantify the efficiency of the chosen preprocessing pipeline. By quantifying the result of each preprocess step, the user can choose an optimal set of preprocesses that can greatly improve the image quality, leading to a high and accurate diagnosis through a deep learning model. Thus, this study detailed how the full reference image quality metrics can be used to validate the performance of sMRI preprocess tasks. 2024 IEEE. -
Full Swing Logic Based Full Adder for Low Power Applications
During the design of Application-Specific Integrated Circuits, a whole adder logic circuit plays a significant role. The full adder is a fundamental part of the majority of VLSI and DSP applications. Power consumption in full adders is one of the key factors; hence it is necessary to build full adders with low power consumption. Full adders are developed in this work employing full swing AND, OR, and XOR gates and compared with pass transistor logic (PTL) based AND, OR, and XOR gates, and complementary metal oxide semiconductor logic (CMOS) based AND gate, OR gate, and XOR gate. The Mentor Graphics Tool is used to construct and simulate every planned circuit. After receiving simulation data, we compared the power consumption, delay and PDP of several complete adder-based logic designs. In the proposed full swing XOR, the power dissipation and delay is decreased by 10.5% and 9.8% respectively and hence the full swing full adder PDP is decreased by 0.6%. As compared to alternative full adder designs based on logic, full swing by using gates like AND gate, by using the OR gate, and with the help XOR gate, full adder design consumes less power and hence suitable for low power applications. 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. -
Fusion of medical image using STSVD
The process of uniting medical images which are taken from different types of images to make them as one image is a Medical Image Fusion. This is performed to increase the image information content and also to reduce the randomness and redundancy which is used for clinical applicability. In this paper a new method called Shearlet Transform (ST) is applied on image by using the Singular Value Decomposition (SVD) to improve the information content of the images. Here two different images Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are taken for fusing. Initially the ST is applied on the two input images, then for low frequency coefficients the SVD method is applied for fusing purpose and for high frequency coefficients different method is applied. Then fuse the low and high frequency coefficients. Then the Inverse Shearlet Transform (IST) is applied to rebuild the fused image. To carry out the experiments three benchmark images are used and are compared with the progressive techniques. The results show that the proposed method exceeds many progressive techniques. Springer Nature Singapore Pte Ltd. 2017. -
Future Innovation in Healthcare by Spatial Computing using ProjectDR
Spatial Computation is the next step in the continuing convergence between the digital and physical realms. It is a set of inventions and developments that can better our lives through learning the real world, acknowledging and connecting our connection to, and traveling through various locations in the world. The lack of modern, precise, and effective diagnosis limits the rehabilitation of patients, despite technical advancements in medicines. The capabilities of spatial computing are expanded in a healthcare framework during the care and treatment of the patient. In this article, our purpose is to clarify the function of ProjectDR in the field of healthcare, which enables the display of medical images, such as CT scans and MRI results, directly on the patient's body in a manner that moves as patients do. 2021 IEEE. -
Fuzzy based Controller for Bi-Directional Power Flow Regulation for Integration of Electric Vehicles to PV based DC Micro-Grid
Utilization of Electric Vehicle as an auxiliary power source to a DC micro-grid for active power regulation is examined here. This paper focus on development of a Fuzzy based controller capable of regulating the bi-directional active power flow between a 10 kW DC Micro-grid and an Electric Vehicle. The system enables to balance the load on grid by performing peak shaving during peak hours and valley filling during off-peak hours. The load curve of Bangalore city for a typical day was taken as the reference and was used to implement the power flow control. The DC grid was designed for a 10 kW PV based micro-grid. The integrated DC micro-grid was simulated on MATLAB/Simulink platform and the obtained characteristics demonstrate that the power flow from grid to vehicle and vehicle to grid during the peak and off-peak periods respectively. The auxiliary battery pack was stressed only to 10.7 % of its 1C-rating leaving scopes for higher level power transmission possible between the systems. 2019 IEEE. -
Fuzzy Logic Based Energy Storage Management for Parallel Hybrid Electric Vehicle
For the parallel hybrid electric vehicle, the various control strategies for energy management are illustrated with the implementation of fuzzy logic. The controller is designed and simulated in two modes for the economy and fuel optimisation. In order to manage the energy in HEV with three separate energy sources - batteries, Fuel cell and a supercapacitor system, - this article intends to create a fuzzy logic controller. By considering a complete system, the operating efficiency of the components need to be optimized. the control strategy implementation will be performed by the forward-facing approach. The fuel economy is optimised by maximising the operating efficiency in this strategy while other strategies does not have this extra aspect. The ability controller for parallel hybrid vehicles is mentioned in this research to enhance fuel economy. Although the earlier installed power controllers optimise operation, they do not fully utilise the capabilities. Hybrid vehicles can be equipped with a variety of power and energy sources such as batteries, internal combustion engines, fuel cell systems, supercapacitor systems or flywheel systems. The Authors, published by EDP Sciences, 2024. -
Gain and bandwidth enhancement by optimizing four elements corporate-fed microstrip array for 2.4GHz applications
This paper presents the performance analysis of an optimized corporate-fed Rectangular Microstrip Antenna Array of four elements and Rectangular Microstrip Antenna array with Semi-Circular Tabs on the nonradiating edges of each element of the array to operate at 2.4 GHz, with detailed steps of the design process. The proposed antenna structures have been designed using FR4 dielectric substrate having a permittivity ?r of 4.4 with a thickness of 1.6 mm. The simulations have been carried out by using Antenna simulator HFSS version 15.0.0 and performance was analyzed for gain, bandwidth, VSWR, return loss and radiation pattern. The gain of these simulated antenna arrays is 2.4381 dB, 8.2684 dB and 8.5621 dB with a return loss of ?22.4123 dB, ?14.1095 dB and ?15.7621 dB for Single-Element patch, conventional Rectangular Microstrip array and Rectangular Microstrip Antenna array with semicircular tabs respectively at 2.4 GHz. Bandwidths exhibited by Single-Element patch, RMSACT and RMSA are 59.8 MHz, 83.9 MHz, and 212.7 MHz, respectively. 2020, Springer Nature Singapore Pte Ltd. -
Game Rules Prediction Winning Strategies Using Decision Tree Algorithms
With the availability of extensive data spanning over the years, sports have become an emerging field of research. The application of analytics in cricket has become prominent over the years. Cricket, the most loved sport in India, draws the attention of fans worldwide. The Indian Premier League is no exception. Created in 2008, this franchise-based T20 format of cricket has gripped the attention of cricket enthusiasts. With ardent fans cheering for their favorite teams, teams have mounting pressure to maintain their winning streak. One such team is the beloved Chennai Super Kings. Statistical techniques for winner prediction have become popular over the last decade. In this study, we try to frame decision rules for IPL teams to win a series using the CART algorithm. By considering Chennai Super Kings, this study aims to understand the criteria for winning and identify potential weaknesses, allowing the team to predict the likelihood of winning the IPL series. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Gamification and Game-Based Learning: A Systematic Review and Comparative Analysis
In the modern world, characterized by the rapid development of technology and digitalization of almost all spheres of life, it is necessary to keep up with the times and gradually introduce information technology into our lives. This will allow us to remain competitive in a changing world, take advantage of new opportunities and improve our quality of life. It is important to understand that information technology is not just a fashion trend, but a necessary tool for successful development and progress. The paper examines the very concept of gamification, the main methods of introducing gamification into education, highlights the advantages of learning with the addition of gamification, and also works on comparing learning with and without gamification elements. The introduction of game elements into the educational process helps to improve the perception of educational material, as well as increase the level of motivation of the students themselves. It is worth noting that the learning process with the addition of game elements helps to improve attention, develop logical thinking, as well as analyze various situations. Gamification can be viewed from several angles. For a teacher, this teaching method will help to capture the attention of children, which will help create a working atmosphere in the classroom. And for students, gamification is a great opportunity to explore really important topics in game mode. They will have an increased interest in learning, which will have a beneficial effect on their further academic performance and learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Gems of Prediction: From Clarity to Carats - Unveiling Diamond Prices with Machine Learning in Waikato Environment for Knowledge Analysis
Background: This research focuses on using Weka's toolkit to test machine learning models for predicting diamond prices. The complexity of diamond value characteristics, such as carat, cut, color, and clarity, motivates the study to find the most accurate models. The goal is to promote fairer market processes and customer education. Methods used: The research rigorously preprocesses a diamond attributes dataset using Weka for analysis. Various machine learning algorithms are examined, including simple algorithms like Decision Stump and ZeroR, sophisticated models like M5P and REP Tree, and advanced ensemble approaches like Bagging with REP Tree. Model performance is evaluated using train/test splits (80-70-60%) and cross-validation (5-fold and 10-fold) with metrics such as Correlation Coefficient, MAE, and RMSE. Results achieved: The research finds that ensemble approaches, particularly Bagging with REP Tree, outperform simple and sophisticated models in diamond price prediction. These techniques demonstrate higher accuracy and lower error rates, highlighting the need for multiple models to capture the complexity of diamond valuation. Simple models provide benchmarks and insights into dataset trends but are less precise. Concluding remarks: This study contributes to the understanding of machine learning algorithms for diamond price prediction, an important economic valuation subject. It demonstrates the effectiveness of complex data analysis methods using Weka. The research also highlights the accessibility and sophistication of machine learning at the crossroads, with Weka's cutting-edge algorithms making complicated analytical methods more accessible for practical, everyday use. This work adds to the knowledge of the dynamics of diamond prices and the role of machine learning in economic research. 2024 IEEE. -
Gender as a Predictor in the Perception of Sexual Harassment Definition
Sexual harassment is a pervasive problem across the globe and it is generally viewed subjectively. The review of the literature suggests that individual perception, history of past sexual harassment and other personal factors influence beliefs concerning the seriousness of the problem. The present study aims to explore the role of socio demographic variables in the definition of sexual harassment. One hundred and sixty-one college students volunteered for this study. Personal profile sheet and Sexual Harassment Definition Questionnaire were used to collect the data. The results of the chi-square test suggested that girls and students who already experienced sexual harassment found larger social incidents as harassment. However, the results of logistic regression found gender as a strong predictor of sexual harassment definition and the history of past harassment was failed to provide a statistical significance. Educating men on male privilege, violence against women and identifying behaviours in them that are not acceptable by women will be helpful. The Electrochemical Society -
Gender Identification of Silkworm Pupa and Automated Cocoon Cutting Machine for Benefiting the Sericulture Grainages in Karnataka
Sericulture is the backbone of a mediocre farmer family in India. Sericulture provides a major financial support to the farmers with minimum infrastructure and maintenance. Farmers collect the seed cocoons from the grainages also known as seed factories. Grainages produce high quality seeds by mating the male and female cocoons. There is a huge demand of labor in these seed factories to process cocoons. The process includes deflossing, removal of pupa from the cocoon, gender identification, mating, storage of seeds, dispersal of eggs to farmers. Removal of pupa from the cocoon requires the labor to cut open a small portion of the cocoon and remove the pupa from inside. Presently in India, most of the grainages induce female laborers to perform the above job. Pupa is removed from the cocoon by cutting the cocoon using a stainless-steel blade. Each labour is given certain amount of cocoons to cut in a day. This requirement would force the laborers to perform the job at a higher speed which poses a threat of getting wounded by the blade. Hence the process of removal of pupa from the cocoon and sex identification of pupa to be automated. Thereby it is important to automate the possible processes in the grainages which could reduce human intervention and increase productivity. Bivoltaine hybrid race of silkworm namely FC1 and FC2 are the varieties under consideration for the research. Theses silkworm varieties are majorly used in grainages for seed production and hence the proposed machine was introduced. This semi automated cocoon cutting machine identifies the gender of the cocoon and later cut the required amount of cocoons minimally. This process would help in maintaining the maximum reliability of silk thread. Thereby the silkworm gender identification has to be non destructive. Image classification were done using Convolution Neural Network (CNN), Visual Geometry Group 16 (VGG16) and Efficient net methods, among which the latter produced highest accuracy. The Efficient Net method has produced the validation accuracy of 98.99% for FC1 and 99.9 for FC2 variety. An automated cocoon cutting machine was developed to cut open the cocoons at a high speed. It is important to automate the possible processes in the grainages which could reduce human intervention and increase productivity. This paper focuses on automating the gender identification and removal of the pupa from the cocoon. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Genetic Algorithm-Based Optimization ofUNet forBreast Cancer Classification: A Lightweight andEfficient Approach forIoT Devices
IoT devices are widely used in medical domain for detection of high blood sugar and life threatening disease such as cancer. Breast cancer is one of the most challenging type of cancer which not only affects women but in some cases men also. Deep learning is one of the widely used technology which provides efficient classification of cancerous lumps but it is not useful for IoT devices as the devices lack resources such as storage and computation. For the suitability in IoT devices, in this work, we are compressing UNet, the popular semantic segmentation technique, for the pixel-wise classification of breast cancer. For compressing the deep learning model, we use genetic algorithm which removes the unwanted layers and hidden units in the existing UNet model. We have evaluated the proposed model and compared with the existing model(s) and found that the proposed compression technique suppresses the storage requirement to 77.1%. Additionally, it also improves the inference time by 3.82without compromising the accuracy. We conclude that the primary reason of inference time improvement is the requirement of less number of weight and bias by the proposed model. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Geometry of Variably Inclined Inviscid MHD Flows
A steady plane variably inclined magnetohydrodynamic flow of an inviscid incompressible fluid of infinite electrical conductivity studied. Introducing the vorticity, magnetic flux density, and energy functions along with the variable angle between magnetic field and velocity vector, governing equations are reformulated. The resulting equations are solved to analyze the geometry of the fluid flow. Considering streamlines to be parallel, stream function approach is applied to obtain the pattern for magnetic lines and the complete solution to the flow variables. Next considering parallel magnetic lines, magnetic flux function approach is applied to obtain streamlines and the complete solution of the flow. A graphical analysis of pressure variation is made in all the cases. 2020, Springer Nature Singapore Pte Ltd. -
Gesture based Real-Time Sign Language Recognition System
Real-Time Sign Language Recognition (RTSLG) can help people express clearer thoughts, speak in shorter sentences, and be more expressive to use declarative language. Hand gestures provide a wealth of information that persons with disabilities can use to communicate in a fundamental way and to complement communication for others. Since the hand gesture information is based on movement sequences, accurately detecting hand gestures in real-time is difficult. Hearing-impaired persons have difficulty interacting with others, resulting in a communication gap. The only way for them to communicate their ideas and feelings is to use hand signals, which are not understood by many people. As a result, in recent days, the hand gesture detection system has gained prominence. In this paper, the proposed design is of a deep learning model using Python, TensorFlow, OpenCV and Histogram Equalization that can be accessed from the web browser. The proposed RTSLG system uses image detection, computer vision, and neural network methodologies i.e. Convolution Neural Network to recognise the characteristics of the hand in video filmed by a web camera. To enhance the details of the images, an image processing technique called Histogram Equalization is performed. The accuracy obtained by the proposed system is 87.8%. Once the gesture is recognized and text output is displayed, the proposed RTSLG system makes use of gTTS (Google Text-to-Speech) library in order to convert the displayed text to audio for assisting the communication of speech and hearing-impaired person. 2022 IEEE. -
GLANCEGuided Language Through Autoregression Establishing Natural and Classifier-Free Editing
In this study, researchers aimed to simplify text conversion into images using the latest text-to-image generation methods. While these methods have improved the quality and relevance of generated images, certain crucial questions remained unanswered, limiting their practicality and overall quality. To address these issues, the researchers introduced a novel text-to-image method. This method allows for better control of the scene depicted in the image through text, enhances the tokenization process by incorporating specific knowledge about key image regions such as faces and important objects, and provides guidance to the transformer model without needing a classifier. The outcome of this work was a model that achieved state-of-the-art results in terms of image quality and human evaluation, enabling the generation of high-fidelity 512?512-pixel images. Moreover, this method introduced new capabilities, including scene editing, text editing with reference scenes, handling out-of-distribution text prompts, and generating story illustrations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Global Analysis of Quantum Technology Discourse
he study provides a thorough exploration of the global quantum technology landscape, offering valuable insights for researchers, policymakers, and industry stakeholders. It employs advanced analytical methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) for topic modeling. The research focuses on understanding discussion intensity, geographical distribution, co-mentioning patterns among countries, prevalent topics, and keyword-based trends. Utilizing diverse datasets, the study employs heatmaps, network analysis, and thematic analysis to categorize textual data. Evaluation metrics like Topic Coherence and Network Centrality Measures contribute to a robust methodology.Key findings include dominant discussions on quantum computing and investment strategies, with focused attention on governmental roles in R&D and specific quantum computer research. Notably, there is a niche focus on quantum algorithmic risks in Australia. Document characteristics vary, with some blending multiple themes and others centered around a single topic. LDA topic modeling and network analysis identify key countries, showcasing global hotspots and potential collaborations in quantum technology discussions. 2024 IEEE. -
Global and Indian Perspectives on Russia-Ukraine War using Sentiment Analysis
In today's world, social media has become a platform through which people express their opinions and thoughts regarding various topics. Twitter is one such platform wherein people resort to expressing their opinions or portraying sentiments to the world. Today it has become easier to analyze mass opinion by using sentiment analysis. This paper investigates the ongoing Russia-Ukraine war by analyzing opinionated tweets, and it seeks to understand the sentiments from a global and Indian perspective. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. Multinomial Naive Bayes classifier classified the tweets into positive, neutral, and negative categories. The paper employed NRCLex for emotion classification and aspect-based sentiment analysis to divide opinions into aspects and determine the sentiment associated with each element. For the study, 4,31,857 tweets were extracted, and the results of sentiment analysis depict that 44.09% users had negative sentiments followed by 33.378% users expressing positive sentiment and remaining 22.53% people were neutral in their tweets. Fear, anger and sadness were amongst the top emotions expressed in the negative tweets whereas the positive tweets expressed trust and anticipation that the war would end soon. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. An analysis was performed on 1542 tweets that were obtained for Operation Ganga. 74.5% of the users had positive sentiments about Operation Ganga, whereas 16.67% and 8.5% had negative and neutral sentiments respectively. The people trusted this evacuation process resulting in more positive sentiments. Fear of losing near and dear ones and fear of safety was the topmost concern for Indians and leadership was one of the topmost aspects tweeted in the positive sentiments. Thus, the overall results depict that the common man does not prefer war and is fearful of the outcomes. The government should hear the voice of the common man and plan strategies and decisions considering the common man's sentiments. 2022 ACM. -
Global Governance of Artificial Intelligence: Ethical, Legal Challenges and Changes in Economy and Business
Artificial intelligence (AI) and global governance are an inclusive platform to discover the policy challenges worldwide augmented by artificial intelligence. The platform has three predominant subjects: AI and the global order, governance of AI, insights on the platform consider for mapping of AI futures. AI has great impact in revolution of geopolitical order and the reaction of multifaceted organizations which minimize AI risks and unpremeditated significances and its social aids are maximized through governance structures. It focusses on setups, collaborations, and tensions between different actors responsible for plan, deployment, support and governance of AI. AI improves the benefit for human well-being, productivity, social good, and safety with substantial risks for workers, developers, firms, and governments. The actors and organization begin to realize the ethical, legal, and regulatory challenges associated with AI. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
GNSS Signal Obstruction Removal Tool for Evaluating and Improving Position Accuracy in Satellite Networks
The positioning accuracy of Global Navigation Satellite System (GNSS) is largely affected by the site's surroundings. However, the methods to simulate GNSS signal obstruction and the nature of signal obstruction have not yet been explored fully. In this research, we investigated a way to remove the signals received from a specific region by specifying azimuth and elevation from GNSS observation files and evaluating how the removal of signals affects GNSS positioning accuracy. In addition, we also investigated the signal blockage for buildings of certain dimensions and a mountain. Python was used as a programming language to develop a program for the signal removal. RTKPOST was used for the GNSS data processing, and RTKPLOT was used for the visualisation of processed data and analysis of positioning accuracy. We successfully developed a Python shell script to remove the signals in GNSS data file from specific region by specifying azimuth and elevation. It was also found that removing signals from azimuth 0 to 100 degree and elevation 0 to 30 degree increased the positioning accuracy within a low multipath dataset. However, when the maximum elevation angle was increased to 45 degrees, positioning accuracy degraded, indicating that the signal from certain elevations have a positive or negative impact on positioning accuracy. Further research avenues are explored as an extension of work done here. 2023 IEEE.