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Compendium of Qubit Technologies inQuantum Computing
Quantum computing is information processing based on the principles of quantum mechanics. Qubits are at the core of quantum computing. A qubit is a quantum state where information can be encoded, processed, and readout. Any particle, sub-particle, or quasi-particle having a quantum phenomenon is a possible qubit candidate. Ascendancy in algorithms and coding demands knowledge of the specificities of the inherent hardware. This paper envisages qubits from an information processing perspective and analyses core qubit technologies. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Fake News Detection and Classify the Category
A new type of disinformation has emerged: fake news, or untrue stories that have been presented as actual occurrences. We can no longer tell whether the information is true from fraudulent since so much information is published on social media these days. Artificial intelligence algorithms are helpful in resolving the fake news identification issue. In the field of natural language processing, fake news identification is a crucial yet difficult issue (NLP). In this article, we discuss similar duties as well as the difficulties associated with finding bogus news. Based on these findings, we suggest intriguing avenues for future study, such as developing more accurate, thorough, fair, and useful detection models. The average public's life is impacted by mass media since it happens regularly. Because of this, news stories are written that are somewhat true or even entirely untrue. Using online social networking sites, people deliberately promote these fake goods. It is crucial to decide whether the news is false owing to its potential to have detrimental social and national effects. The false news identification process made use of many criteria, including the headline and body content of the news piece. The suggested method works effectively in terms of producing results with excellent accuracy, precision, and memory. Comparing all the models employed in this study, it was discovered that Distillbert and multinomial nae bayes models perform better than Logistic and others ml models. The credibility of the story may be evaluated using a larger dataset for better results and additional variables like the author and publisher of the news. Grenze Scientific Society, 2023. -
Brain Tumor Detection using Hyper Parameter Tuning and Transfer Learning
Brain Tumor is the development of abnormal cells in our brain. There are cancerous and noncancerous brain tumors. Because they can press against healthy brain tissue or spread there, brain tumors are harmful. The early diagnosis of brain tumors is a highly challenging assignment for radiologists. The typical size of a brain tumor doubles in just twenty-five days due to its rapid growth. If not properly cared for, the patient's survival rate typically does not exceed six months. It may quickly result in death. For the purpose of early brain tumor identification, an automatic method is necessary. In this study, an automated strategy is suggested for quickly distinguishing between malignant and non-cancerous brain images. Most of the time, it can be treated if caught during the early stages. Hence the need for more and improved brain tumor detection. The most crucial part here is image processing. The medical images obtained during the test have to be appropriately analysed. Various methods such as MobileNet, EfficientNetB7, and EfficientNetV2 have been used and their efficiency has been analysed. Here we classify the dataset containing 300 images into two. The suggested system will offer improved clinical support for the field of medicine. 2023 IEEE. -
Artificial Intelligence and Deep Learning Based Brain Tumor Detection Using Image Processing
In the field of medical science, applications that are particularly used for diagnostic purposes, are used in the detection of brain tumors since detecting an error in MRI scanning is becoming a major task for radiologists and requires a lot of their focus. Flaws that are prevalent during tumor detection must be taken care of to avoid further complications. MRI scanning is one of the most recently developing technologies. The radiologist is a key player in the identification of the brain tumor. Radiologists have to check every image perfectly to avoid the errors in identifying the brain tumor. There is a probability that sometimes cerebral fluid may also appear as mass tissue during the MRI scan. The model that is proposed in this research uses a machine learning algorithm which helps to improve the validity of the classification of the images that are taken in MRI scans. The study focuses on having an automated system that carries out an essential role in determining whether a lump is present in the brain or not. The study tries to resolve the primary flaws in detection necessary to evade further complications in MRI images in brain detection. The main aim of this study is to train the algorithm in a more extensive dataset and to check the patient-level validity with the help of various new datasets. 2023 IEEE. -
Automatic Weld Features Identification and Weld Quality Improvement in Laser Sensor Integrated Robotic Arc Welding
In this study, an integration of point laser sensor in robotic arc welding has been performed for achieving robotic positional accuracy automatically in every welding cycle. With the help of defined focal length of laser sensor, weld seam positions as well as weld gap have been found automatically for any newly positioned work-piece. If there is any change in robot positioning compared to the master job, the shift in every axis is sent as signal to the robot controller so that robot end effector will adjust the shift amount automatically. The welding process parameters are set at optimal values. Taguchi approach so that maximum values of weld quality in terms of depth of penetration, yield strength and ultimate strength can be achieved in every welding cycle. Overall, with the proposed approach, a smart and productive way of operating industrial welding robot has been proposed which can be implemented in any medium to large scale industries for obtaining welding joints with minimum defects. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deploying NLP techniques in Twitch application to comprehend online user behaviour
Sentiment analysis of emotion entails identifying and analyzing subjective information from language, such as views and attitudes, and helps to improve data visualization by employing a variety of strategies, tactics, and tools. New media channels have significantly changed how people interact, exchange ideas, and share information. Numerous businesses have begun to mine this data, concentrating on social media since it is a popular platform for customers to voice their ideas about various brands or goods and because it gives users an audience, enhancing the visibility and potential effect of this input. So far, as the internet expands and modern technology advances, new avenues have emerged with a higher ability to offer businesses pertinent feedback on their goods. The goal of this study is to investigate the many forms of online behaviour by analyzing chat interactions from the well-known streaming service Twitch. Emotes were occasionally employed in place of letters, to get attention, or to communicate emotions. We propose a system that may take in chat logs from a certain stream, use a sentiment analysis algorithm to classify each message, and then display the data in a way that might permit users to analyze the results according to its polarity (positive message, negative message, or neutral message). This application must be sufficiently versatile to be used with any platform broadcast type and to handle the datasets at very huge level. 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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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.