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Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms
Machine learning algorithms enable computers to learn from data and continuously enhance performance without explicit programming. Machine learning algorithms have significantly improved the accuracy and efficacy of thyroid diagnosis. This study identified and analysed the usefulness of several machine-learning algorithms in predicting hypothyroid profiles. The main goal of this study was to see the extent to which the algorithms adequately assessed whether a patient had hypothyroidism. Age, sex, health, pregnancy, and other factors are among the many factors considered. Extreme Gradient Boosting Classifier, Logistic Regression, Random Forest, Long-Term Memory, and K-Nearest Neighbors are some of the machine learning methods used. For this work, two datasets were used and analysed. Data on hypothyroidism was gathered via DataHub and Kaggle. These algorithms were applied to the collected data based on metrics such as Precision, Accuracy, F1 score and Recall. The findings showed that the Extreme Gradient Boosting classification method outperformed the others regarding F1 score, accuracy, precision, and recall. The research demonstrated how machine learning algorithms might predict thyroid profiles and identify thyroid-related illnesses. 2023 IEEE. -
HumanComputer Interactions with ArtificialIntelligence and Future Trends of HCIA Study
Artificial Intelligence, the name itself depicts the meaning that providing the knowledge of human to the machine artificially. AI is not a sense or feeling but the software or a model evolved to do complex tasks like human beings. With the invention of computer it has become so easy to do day to day jobs without much effort. HCI is all about interacting with computers. Now-a days it is possible to mesh with the computer through voice, touch, eye movement, and hand gestures. HCI has many challenges but has established in grand manner with the support of Artificial Intelligence. This study provides some important roles of Artificial Intelligence in HCI and its future development. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Artificial Intelligence Involvement in Graphic Game Development
Games have always been a popular form of entertainment and with the advancements in technology, the integration of Artificial Intelligence (AI) in gaming has revolutionized the gaming industry. This research article aims to explore the various applications of AI in gaming and its impact on the industry and player experience. Unlike the typical straightforward nature of AI, this research paper takes a more human approach to discussing the topic. It delves into the evolution of AI in games and the various types of AI used in game development. These include rule-based AI, learning- based AI, and evolutionary AI, which have all contributed to the development of increasingly immersive gaming experiences. The benefits and challenges of using AI in games are also explored, considering the impact on player experience. While AI-powered opponents can provide a greater challenge, balancing the difficulty level is critical to ensuring the game remains enjoyable. The potential ethical concerns of using AI in games are also discussed, such as data privacy, bias, and fairness. Furthermore, this research paper looks into the future of AI in games and how it may shape the gaming industry and player experience in the years to come. With the continued development of AI techniques such as reinforcement learning and GANs, the possibilities for more immersive and engaging gaming experiences are endless. 2023 IEEE. -
Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE. -
Kho Kho Model: A Novel Technique for Efficient Handoff in Vehicular Ad-hoc Networks
The highly mobile nature of VANET implies that the nodes involved are constantly disconnecting and reconnecting as they switch between access points or move out of the range of their access points. In such scenarios, seamless connectivity is essential, especially when emergency services are involved. Handoff is a process in wireless communication that takes care of the switching process that happens between access points whenever a mobile device moves from one point to another. In a dynamic scenario involving vehicular nodes, this switching needs to take place between a mobile node or a fixed access point (known as RSUs), as quickly as possible. To this end, this research work proposes a novel handoff method known as the Kho Kho Model - which is loosely based on the traditional Indian sport of the same name. The model groups together nodes that are moving in the same direction, thereby effectively reducing the amount of processing required to perform handoff for a set of nodes. The use of ANN have helped to improve handoff since it can help in making decisions quickly by making use of multiple parameters including signal strength, noise, direction, and others. To improve the efficiency of the proposed handoff model, RBFNN has been used in this research. The proposed model was implemented using NS-3 simulator. The results have shown that the proposed method has a slightly better improvement in the overall NRO, a reduced average delay and reduced jitter compared to the existing handoff method employed by the IEEE 802.11p standard. 2023 IEEE. -
A Space Vector Modulated Direct Torque Control of Induction Motor with Improved Transient Performance and Reduced Parameters Dependency
Direct torque control (DTC) of induction motors is hampered by high torque and current ripple. Integrating DTC with space vector pulse width modulation (DTC-SVPWM) is one of the frequently used approaches to solve this problem. However, it adds to the computational complexity, increases the number of necessary motor parameters needed for control scheme implementation, and also affects the transient performance of the induction motor; this approach compromises the robustness and simplicity of DTC scheme. To get around these restrictions, a novel control strategy is put forth in this paper. The suggested scheme enhances the steady-state performance and transient response of the motor while preserving the simplicity and robustness of the DTC scheme. To accomplish this, the proposed control scheme operates at varying switching frequencies during transient conditions and constant switching frequencies during steady-state. The suggested speed control method does not employ any rotating reference frame transformations or usage of many rotor parameters for computation, nor does it call for sector identification and operates with a single PI controller. The suggested topology also uses a bus-clamped PWM modulation technique, which lowers the average switching frequency to 2/3 times the actual switching frequency. Thus, switching losses are also decreased. Simulation results show the effectiveness of the proposed topology in enhancing the transient and steady-state performance of the induction motor. The results are compared with the traditional DTC and DTC-SVPWM scheme. 2023 IEEE. -
Analytical Results of Heart Attack Prediction Using Data Mining Techniques
In the modern era of living a fast lifestyle, people are not more conscious of their food eating and lifestyle. Due to these reasons, the chances of having a cardiac-related disease have risen drastically. This paper has studied the various supervised and unsupervised machine learning algorithms in comparative methods with best accuracy. Models like classification algorithms, regression algorithms, and clustering algorithms have been used for this paper. This research paper majorly focuses on patients with certain medical attributes that indicate a higher risk of heart disease. The model almost gives a good accuracy for all the regression and classification models when compared to the clustering models. Among all the algorithms, random forest and decision tree gives better accuracy 2023 IEEE. -
A Video Surveillance-based Enhanced Collision Prevention and Safety System
Road traffic crashes that result in fatalities have become a global phenomenon. Therefore, it is imperative to use caution and vigilance while being on the road. Human mistake, going over the speed limit, being preoccupied while driving or walking, disobeying safety precautions, and other factors can also contribute to such unforeseen accidents or injuries, which can result in both bodily and material loss. So, safety is what we seek to achieve. Furthermore, as the number of automobiles has increased, so too have collisions between vehicles and pedestrians. Using computer vision and deep learning approaches, this research seeks to anticipate such encounters. The data often comes from traffic surveillance cameras in video formats. We have therefore concentrated on video sequences of vehicle-pedestrian collisions. We begin with a detection phase that includes the identification of vehicles and pedestrians; for this phase, we employed YOLO v3 (You Only Look Once). YOLO v3 has 80 classes, but we only took six of them: person, car, bike, motorcycle, bus, and truck. Following detection, the Euclidean distance approach is used to determine the interspace between the vehicle and the pedestrian. The closer the distance between a vehicle and a pedestrian, the more likely it is that they will collide. As a result, pedestrians in risk are located, and once we are aware of the pedestrians in danger, we search for nearby safer regions to alert them to head to the nearest location that is secure. Grenze Scientific Society, 2023. -
Deep Learning Algorithms Comparison forMultiple Biological Sequences Alignment
In this paper, deep learning algorithms are compared for aligning multiple biological molecular sequences such as DNA, RNA, and protein. Efficient algorithms are necessary for sequence alignment to identify significant insights, but there is a trade-off between time and accuracy. This study compares deep learning algorithms for multiple sequence alignment with better accuracy, using a new similarity measure to choose the best resemblance sequences in a set. Using a benchmark dataset, the algorithms compared include CNN, VAE, MLPNN, DBNs, Deep Boltzmann Machine, and GAN. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Encoder-Decoder Approach toward Vehicle Detection
Vehicle Detection algorithms run on deep neural networks. But one problem arises, when the vehicle scale keeps on changing then we may get false detection or even sometimes no detection at all, especially when the object size is tiny. Then algorithms like CNN, fast-RCNN, and faster-RCNN have a high probability of missed detection. To tackle this situation YOLOv3 algorithm is being used. In the codec module, a multi-level feature pyramid is added to resolve multi-scale vehicle detection problems. The experiment was carried out with the KITTI dataset and it showed high accuracy in several environments including tiny vehicle objects. YOLOv3 was able to meet the application demand, especially in traffic surveillance Systems. Grenze Scientific Society, 2023. -
Calibration of Optimal Trigonometric Probability for Asynchronous Differential Evolution
Parallel optimization and strong exploration are the main features of asynchronous differential evolution (ADE). The population is updated instantly in ADE by replacing the target vector if a better vector is found during the selection operation. This feature of ADE makes it different from differential evolution (DE). With this feature, ADE works asynchronously. In this work, ADE and trigonometric mutation are embedded together to raise the performance of an algorithm. The work finds out the best trigonometric probability value for asynchronous differential evolution. Two values of trigonometric mutation probability (PTMO) are tested to obtain the optimum setting of PTMO. The work presented in this paper is tested over a number of benchmark functions. The benchmark functions results are compared for two values of PTMO and discussed in detail. The proposed work outperforms the competitive algorithms. A nonparametric statistical analysis is also performed to validate the results. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Pertaining analysis of fracture risk in Osteoporotic patients using Machine Learning Techniques
Bone fractures in the spine or hip are the most severe complications of Osteoporosis. Older subjects with Osteoporosis are vulnerable to falls. This paper aims to review the breakthrough in machine learning methods over the past four years in assessing fracture risk in osteoporotic patients. Machine learning is applied in the healthcare and medical field. Machine learning professionals can accurately predict disease onset by analyzing a large amount of data. Osteoporosis is one of the healthcare domains in which new Machine learning and Artificial Intelligence techniques can be implemented. The objective of this research is to give an overview of the recent advancements in machine learning methods in finding out the risk factors for fractures or predicting the onset of disease. A systematic search was conducted in PubMed to get research papers published on Machine learning methods to detect, classify, or predict osteoporosis-related fracture risk. The articles belonging to Fracture prediction and risks (n=14), Osteoporosis classification(n=3), Diagnosis of fracture(n=3), and Predicting length of stay (n=1) were identified. The quality of the articles is assessed. Most articles described the efforts to create the model and showcased excellent results in predicting the risks. Significant limitations were in the form of inadequate data splitting and data validations. More validation studies are needed in various large groups to improve the model. Most of the participants in significant studies were in their initial stage of the disease, and the reproducibility analysis was done with major disease issues. 2023 IEEE. -
Systematic Contemplate Paradigm on Diabetes Mellitus using different Machine Learning Predictive Techniques
As the foodies love fast food, from micro to combined families across the world the ratio of family members 1:4 is affected with silent killer named as diabetes. A very high blood glucose levels, metabolism, improper carbohydrate, damaged hormone insulin alleviating a human body disability leading to the silent killer of the body parts is the diabetes. An estimated 425 million of people around the globe suffering with diabetes up to 108 million to 1.7 trillion will be affected with diabetes. Therefore millennium, the universe ubiquity suffering with diabetes has next to quadrupled, growing from 9 percent and above among the people. As the eating habits of people in this trendy 21st century is dramatically devastating to the risk of overweight or obese. The silent killer diabetes consequences include kidney failure, Diabetic retinopathy, Heart attack, Stiffness of body muscles, Nerves stroke and lower limb amputation leads to type I and type II diabetes. As the researchers across the globe are using the machine learning algorithms as the reliable problem solver, The complications still continue. The purpose of this percu is to help with the apt selection of features garnishing with machine learning paradigm techniques in selecting the accurate attributes for each person to be properly diagnosed. In this archetype survey paper, we have done a systematic review chronologically a decade research which will help the researchers to explore and get the contemplate on various tangible and intangible data sets they can adopt in diagnosing the mellitus diabetes. Grenze Scientific Society, 2023. -
Non-Contact Vital Prediction Using rPPG Signals
In this paper, we present the clinical significance of various cardiac symptoms with the use of heart rate detection, ongoing monitoring and present emotions. The development of algorithms for remote photoplethysmography has drawn a lot of interest during the past decade (rPPG). As a result, using data gathered from the video feed, we can now precisely follow the heart rate of individuals who are still seated. rPPG algorithms have also been developed, in addition to technique based on hand-crafted characteristics. Deep learning techniques often need a lot of data to train on, but biomedical data frequently lacks real-world examples. The experiment described in this work, we looked at how illumination affected the rPPG signals' SNR. The findings show that the SNR in each RGB channel varies depending on the colour of the light source. Paper describes development in video filtering for recognising the comprehending human face emotions. In our method, emotions are deduced by identifying facial landmarks and analysing their placement. 2023 IEEE. -
Cryptocurrencies: An Epitome of Technological Populism
From a global perspective, which holds significant cryptocurrencies, this study discusses the volatility and spillover effect between the whales cryptocurrencies. Volatility in cryptocurrency markets has always been a time-varying concept that changes over time. As opposed to the stock market, which has historically and recently, the cryptocurrency market is much more volatile. The markets have evidenced many fluctuations in the prices of cryptos. As a result, countries are transforming their policies to suit financial technologies in their economic practices. Blockchain technology allows people to obtain more benefits in a financial transaction and breaks hurdles in the financial system. The study has found no ARCH effect in BinanceCoin, BT Cash, Bitcoin, Vechain, and Zcash. It is discovered that there is an ARCH effect in the case of Ethereum, Tether, Tezos, and XRP. Whale cryptocurrencies have an ARCH effect. Daily closing prices of ten cryptocurrencies, including bitcoin, from January 1, 2019, to December 31, 2020, to determine the price volatility where the bitcoin whales hold significant cryptocurrency values. It has given significant results in case of volatility since we also found that Bitcoin's largest cryptocurrencies among the sample taken for the study have less volatility than other currencies. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Log-Base2 of Gaussian Kernel for Nuclei Segmentation from Colorectal Cancer H and E-Stained Histopathology Images
Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively. 2023 SPIE. -
Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT
ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet; countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques. 2023 IEEE. -
LCLC Based AC-DC Single-Stage Resonant Converter with Natural Power Factor Correction
LLC-based AC-DC resonant converters make excellent EV chargers because of their high efficiency, high power density, and soft switching properties. Efficiency is increased and the need for a larger series inductor is lowered by connecting a capacitor across the magnetising inductance of the LLC resonant architecture (LCLC configuration). Switching frequency control is commonly used to regulate the converter's output DC voltage. However, there is a significant relationship between the converter's power factor and switching frequency. As a result, any changes in load may result in a lower power factor for the converter. This paper suggests a single-stage topology based on the LCLC resonant structure. The LCLC resonant configuration ensures zero voltage switching (ZVS) of the IGBTs used in the converter. Converters have a power factor correction (PFC) stage on the front of the converter to achieve natural power factor correction. Since the PFC stage and the resonant stage are controlled by the same switches, the converter is smaller and less expensive. A bridgeless rectification method is applied in the proposed topology to reduce the number of switching devices. MATLAB/Simulink simulations are used to validate the topology. 2023 IEEE. -
Sustainability Indicators and Ten Smart Cities Review
The motivation of smart cities is to improve the standard of living of citizens and enhance the use of technology in sustainable city services. A city's sustainability can be measured using various sets of smart indicators. This study will analyse urban sustainability indicators as a research problem for ten smart cities. The review of smart cities will focus on the Internet of things (IoT), Mobile devices, and Artificial intelligence technologies (Sensors in street lights, smart homes) that help our citizens transform from rural to urban areas towards sustainability. This research uses a qualitative framework for the taxonomy of the literature for the terms 'smart city' and 'sustainability' Further, the characteristics, critical technology, and IOT application for mobility are elaborated upon. Finally, we discuss ten smart city review proposals reports, based on their sustainability indicators around the world. Concluding and Future studies could focus on using sustainable indicators for developing smart cities in India. 2023 IEEE. -
Intelligent Course Recommendation for Higher Education based on Learner Proficiency
A course recommendation provides valuable guidance and support to learners navigating their educational and career journeys. Artificial Intelligence paves the way for recommending higher education courses. In this article, a framework is proposed that uses different features like learners' interest, their past performance and mainly their family talent history. This framework emphasizes the Intelligence Robotic Course Recommendation System. The system is very helpful for the learner who don't have that much of an explorer of the current trends happening in the world. When the learners similarity knowledge interest is known with respect to real-world needs, the perfect higher education is suggested for them. This paper shows that the framework gives better results when using with artificial intelligence algorithms. 2023 IEEE.