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Allometry Authentication in the Field of Finance: Creation of Well Secured System using AI Algo Based Systems
It is true the banking sector is increasingly under pressure to tighten security in an ever-changing digital arena, even as the customer experience needs to be strengthened. Thus, the use of biometric authentication through enhanced AI-driven systems that would enhance the security protocols while at the same time smoothening the users' interactions was a promising way in response. The paper that follows explores the integration of biometric authentication within banking systems in a bid to make clear its effectiveness in relation to reinforcing security and enhancing user experience. Accordingly, bijson etal. argue that biometric security fits perfectly in banks, since with the increasing cyber threats, banks are bound to deploy more advanced security mechanisms. These traditional means, suchjson, use of passwords and PINs, have shown vulnerabilities that are liable to exploitation and should be changed into something much more resilient. The authentication under biometrics also validates a user's identity by basing it on unique physiological or behavioral traits, such as a fingerprint, features of the face, patterns of the iris, and the voice. Biometric systems authenticate users with a very high level of confidence through AI-based algorithms, averting the security risks associated with unauthorized access and identity theft. Further, biometric authentication overcomes the flaws that prevail with the traditional mode of methods and hence, it ensures a very comfortable and user-friendly mode of system security. 2024 IEEE. -
Alpha-Bit: An Android App for Enhancing Pattern Recognition using CNN and Sequential Deep Learning
This research paper introduces Alpha-Bit, an Android application pioneering Optical Character Recognition (OCR) through cutting-edge deep learning models, including Convolutional Neural Networks (CNNs) and Sequential networks. With a core focus on enhancing educational accessibility and quality, Alpha-Bit specifically targets foundational elements of the English language - alphabets and numbers. Beyond conventional OCR applications, Alpha-Bit distinguishes itself by offering guided instruction and individual progress reports, providing a nuanced and tailored educational experience. Significantly, this work extends beyond technological innovation; Alpha-Bit's potential impact encompasses addressing educational inequalities, contributing to sustainability goals, and advancing the achievement of Sustainable Development Goal 4 (SDG 4). By democratizing education through innovative OCR technologies, Alpha-Bit emerges as a transformative force with the capacity to revolutionize learning experiences, making quality education universally accessible and empowering learners across diverse socio-economic backgrounds. 2024 ITU. -
Alzheimer's Disease Detection using Machine Learning: A Review
Alzheimer's is a progressive brain disorder which is an untreatable, and inoperable and mostly affect the elderly people. There is a new case of Alzheimer's disease being discovered globally in every four seconds. The outcome is fatal, as it results in death. Timely identification of Alzheimer's disease can be beneficial for us to get necessary care and possibly even avert brain tissue damage by the time. Effective automated techniques are required for detecting Alzheimer's disease at very early stage. Researchers use a variety of novel approaches to classify Alzheimer's disease. machine learning, an AI branch use probabilistic technique that allow system to acquire knowledge from huge amount of data. In this paper we represent a analysis report of the work which is done by researcher in this field. Research has achieved quite promising prediction accuracies however they were evaluated the the non-existent datasets from various imaging modalities which makes it difficult to make the fair comparison with the other methods comparison among them. In this paper, we conducted a study on the effectiveness of using human brain MRI scans to detect Alzheimer's disease and ended with a future discussion of Alzheimer's research trends. 2021 IEEE. -
Ambient monitoring in smart home for independent living
Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes. Springer Nature Singapore Pte Ltd. 2019. -
An Abstractive Text Summarization Using Decoder Attention with Pointer Network
Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods. 2023 IEEE. -
An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector Machine
As indicated by a measurable report distributed by the registry of central brain tumor at United States (CBTRUS), roughly 59,550 individuals were recently diagnosed to have essential benign and essential harmful brain tumors in 2017. Besides, in excess of 91,000 individuals, in the United States alone, were living with an essential harmful cerebrum tumor and 367,000 were living with an essential kind brain tumor. The task of detecting the position of the tumor in the body of the patient is the starting point for a medical treatment in the diagnosis process. The main aim of this study is to design a computer system, which is able to detect the tumor presence in the digital images of the brain in the patient and to accurately define its borderline. In this proposed model, gamma distribution method is used for training, testing, and for the feature extraction process, while SVM, support vector machine is used for the classification process. Most of the algorithms find it difficult to segment the tumors that were present in the edges. But with the help of gamma distribution along with the use of edge analysis, it is easier to identify those tumor areas that are present in the edges, thus making it easier for the preprocessing process. Gamma distribution also provides us with high accuracy, and it can also point the exact location of the tumor than compared to other algorithms. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An AI Approach to Pose-based Sports Activity Classification
Artificial intelligence systems have permeated into all spheres of our life-impacting everything from our food habits to our sleep patterns. One untouched area where such intelligent systems are still in their infancy is sports. There has not been enough indulgence of AI techniques in sports, and most of the works are carried on manually by coaching staff and human appointees. We believe that intelligent systems can make coaching staff's work easier and produce findings that the human eye can often overlook. Here, we have proposed an intelligent system to analyze the beautiful game of tennis. With the use of computer vision architecture Detectron2 and activity-based pose estimation and subsequent classification, it can identify an action from a tennis shot (activity). It can produce a performance score for the player based on pose and movement like forehand and backhand. It can also be used to understand and evaluate the strengths and weaknesses of the player. The proposed approach provides a piece of valuable information for a player's performance and activity detection to be used for better coaching. The study achieves a classification accuracy of 98.60% and outperforms other SOTA CNN models. 2021 IEEE -
An AI-Based Forensic Model for Online Social Networks
With the growth of social media usage, social media crimes are also creeping sprightly. Investigation of such crimes involves the thorough examination of data like user, activity, network, and content. Although investigating social media looks quite straight forward process, it is always challenging for the investigators due to the complex process involved in it. Due to the immense growth of social media content, manual processing of data for investigation is not possible. Most of the works from this area provide an automatic model or semi-automated, and much of the contributions lacks the logical reasoning and explainability of the evidence extracted. Searching techniques like entity-based search and explainable AI add value to the quick retrieval within appropriate scope and explain the results to the court of law. This paper provides a model by adding these new techniques to the basic forensic process. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Alternative Deep Learning Approach for Early Diagnosis of Malaria
Considering the malaria disease-related moralities prevailing mainly in underdeveloped countries, early detection and treatment of malaria must be an essential strategy for lowering morbidity and fatality rates. Detection of Malaria using traditional investigation methods through blood samples and expert judgments was found to be time-consuming. In this paper, the authors introduced a Machine Learning automated system to eliminate the need for human intervention, which in turn enables early detection of malaria. The study has used various Deep Learning techniques such as traditional Convolutional Neural Network (CNN), VGG19, ConvNeXtXLarge, ConvNeXtBase, ConvNeXtSmall, ConvNeXtTiny, InceptionResnetv2, Xception, DenseNet169, EfficientNetB7, MobileNet, ResNet50, and NasNetLarge as base models. These models have been trained and tested with microscopic blood smear images dataset and observed that ConvNeXtXLarge detects malarial parasites with an accuracy of 96%. The proposed method outperforms the existing approaches in terms of both accuracy and speed. The findings of this work can contribute to the development of more accurate and efficient automated systems for early detection of Malaria. 2024 IEEE. -
An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid
The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience. 2024 IEEE. -
An Analysis of Financial and Technological Factors Influencing AgriTech Acceptance in Bengaluru Division, Karnataka
In 2023, India surpassed China to become the world's most populated nation. This demographic surge has precipitated an escalating exigency for sustenance as populace burgeons unabatedly. To satiate this burgeoning demand there arises an imperative to augment yield of agriculture commensurately. It is pertinent to acknowledge that as per Global Hunger Index of 2019, India occupies disconcerting rank of 102 amongst consortium of 117 nations when gauged by severity of hunger quantified through Hunger Severity Scale with disquieting score of 30.3. Aspiration of attaining utopian objective of zero hunger by 2030 as promulgated by Sustainable Development Goals appears to be quixotic endeavor seemingly beyond realm of plausibility. In this milieu agricultural technology (AgriTech) enterprises within India present veritable opportunity to invigorate agricultural sector. Agrarian landscape of India has been undergoing profound metamorphosis owing to technological renaissance that has permeated nation facilitated by innovative solutions proffered by nascent corporate entities. State of Karnataka stands as an epicenter of sorts for AgriTech enterprises within India. In this study we meticulously scrutinize impact wielded by financial factors on adoption of AgriTech solutions by agrarian stakeholders and elucidate technological determinants that actuate embracement of AgriTech within this demographic. The study uses descriptive statistics and chi-square analyses to rigorously assess predefined objectives. Geographic ambit of this inquiry encompasses regions of Chikkaballapura and Doddaballapura Taluks situated within Bengaluru division of Karnataka in 2022. The empirical revelations distinctly illuminate that individuals vested with access to technological and financial resources exemplified by parameters such as annual household income, accessibility to commercial banking services, cooperative financial institutions, mobile telephony, internet connectivity and Global Positioning System (GPS) technology exhibit palpable predilection for integration of AgriTech solutions into their agrarian practices. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
An Analysis of Grimms' Transmedia Storytelling in the Age of Technology
This research paper delves into an intersection of traditional literature and transmedia storytelling, with particular emphasis on Grimms' tales and its television series adaptation. Providing young audiences with engaging and dynamic experiences, transmedia storytelling involves delivering a single story across numerous platforms. Utilizing narrative analysis, this research seeks to uncover hidden themes, character growth, and story dynamics by breaking down the complex presentation and structure of stories in diverse media. Natural Language Processing (NLP) techniques like thematic analysis, sentiment analysis, keyword sentiment analysis have been employed to examine the differences between the presentation of these stories in varied formats as well as evaluating audience reception. It also assesses the degree to which transmedia adaptations support the resuscitation of beloved children's books in popular culture. By incorporating digital surrealism and aspects of technology, this paper enhances our understanding of how traditional stories captivate audiences across various media forms while maintaining their timeless quality. 2024 IEEE. -
An Analysis of Levenshtein Distance Using Dynamic Programming Method
An edit distance (or Levenshtein distance) amongst dual verses refers to the slightest amount of replacements, additions and omissions of signs essential to turn one name addicted to the additional is referred to as the edit distance (or Levenshtein distance) amongst dual verses. The challenge of calculating the edit distance of a consistent verbal, that is the set of verses recognised by a fixed mechanism, is addressed in this research. The Levenshtein distance is a straightforward metric for calculating the distance amongst dual words using a string approximation. After witnessing its efficiency, this approach was refined by combining certain comparable letters and minimising the biased modification between associates of the similar set. The findings displayed a considerable enhancement over the old Levenshtein distance method. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An analysis of load balancing algorithms in the cloud environment
The emerging area in an IT environment is Cloud Computing. There are many advantages of the computing but unfortunately, allocation of the job request effectively is a trouble. It requires lots of infra structural commitments and the quality inputs of the resources. Also, in the cloud computing environment, Load Balancing is an important aspect. Efficient load balancing algorithm helps the resource to have optimized utilization with the proper dissemination of the resources to the cloud user in pay-as-you-say-manner. It also supports ranking the job request based on the priority with the help of scheduling technique. We present the various types of Load Balancing Techniques in the different platform of Cloud Environment specified in SLA (Service level Agreement). 2016 IEEE. -
An Analysis of Machine Learning and Deep Learning to Predict Breast Cancer
According to the report published by American Cancer Society, breast cancer is currently the most prevalent cancer in women. In addition, it is the second leading cause of death. It needs to be taken into serious consideration. Earlier and faster detection can help in the earlier and easier cure. Normally, medical practitioners take a large amount of time to understand and identify the presence of cancer cells in the human body. This can lead to serious complications even to the death of the individual. Hence there is a need to identify and detect the presence of this disease very accurately and in a shorter span of time. Like every other industry, the medical industry is shifting its paradigm to automation giving excellent results having high accuracy and efficiency, which is achieved using Artificial Intelligence. There are two sets of models developed based on the numerical dataset Wisconsin and image dataset BreakHis. Machine Learning algorithms and Deep Learning algorithms were applied on the Wisconsin dataset. Meanwhile, Deep Learning models were used for analysis of the Breakhis dataset. Machine Learning models- Logistic Regression, K Neighbors, Naive Bayes, Decision tree, Random Forest and Support vector classifiers were used. Deep Learning models- normal deep learning models, Convolutional Neural Network (CNN), VGG16 & VGG19 models. All the models have provided a very good accuracy ranging between 75% and 100%. Since medical research has a requirement for higher accuracy, these models can be considered and embedded into several applications. Grenze Scientific Society, 2022. -
An Analysis of Manufacturing Machine Failures and Optimization Using Replacement Year Prediction
The manufacturing industry is highly susceptible to equipment failures, leading to costly downtime, production delays, and increased maintenance expenses. Effective maintenance planning and resource allocation depend on the early detection of possible faults and the precise forecasting of replacement years. The fundamental technique for assuring operational resilience, limiting disruptions, and improving preventative maintenance processes is manufacturing failure analysis. It entails the methodical analysis of failures and spans several sectors, including the automobile, aerospace, electronics, and heavy machinery. In this research, an integrated methodology for predicting replacement years in the manufacturing industry using operations research approaches and the Python-based machine learning algorithm Random Forest Classifier (RFC) is proposed. The program first calculates the total failure rate after importing manufacturing data from a dataset. The failure rate for each manufacturing line is then determined, and the lines with a high failure rate are identified. The program uses machine learning to improve the analysis by teaching a Random Forest classifier to anticipate failures. The model's performance is assessed by measuring the accuracy of a test set. To determine machine replacement years, it also incorporates replacement theory assumptions. Based on the company's founding year and the current year, it determines the replacement year considering the machine's lifespan. This program's advantages include recognizing production lines with high failure rates, employing machine learning to forecast problems, and offering suggestions on when to replace machines. Manufacturers may enhance their processes, lower failure rates, and increase overall efficiency by utilizing statistical analysis, machine learning, andoptimizationstrategies. As technology advances, the field of failure analysis will continue to evolve, enabling firms to achieve improvements. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Analysis of Word Sense Disambiguation (WSD)
Word sense disambiguation (WSD) is the method of using computer algorithms to determine the sense of arguments in the background. As a result of its difficult nature, WSD has measured an AI-complete problem, i.e., a problem whose key is as minimum as difficult as those posed by artificial intelligence. This article describes the task and introduces motives to resolve the ambiguity of words discussed throughout the text. This article summarizes supervised, unsupervised, and knowledge-based solutions. Senseval/semeval campaigns are described in relation to the assessment of WSDs, with the aim of an unbiased assessment of schemes working on numerous disambiguation errands. Finally, future directions, requests, open difficulties, and open problems are discoursed. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An analysis on direct authentication of data
Authentication is the procedure which permits a sender and receiver of data to validate each other. On the off chance that the sender and receiver of data can't legitimately confirm each other, there is no trust in the activity or data gave by either party. This paper talks about where and when can the service providers use the various authentication models adopted and the comparison between two authentication models. 2017 IEEE. -
An Analytical Review on Data Privacy and Anonymity in 'Internet of Things (IoT) Enabled Services'
Nowadays, the Internet of Things (IoT) is an emerging technology, spreading all over the world so the number of devices is increasing day by day. So, the volume and data complexity has increased drastically in the past three years. The resultant system might contain a significant number of heterogeneous devices, posing integration and scalability issues that must be addressed. In such a situation, security and privacy are commonly regarded as a significant concern. On the other hand, user privacy, defined as the capacity to provide data protection and anonymity, must be protected, which is especially important when personal and/or sensitive information is involved. This paper presents the comprehensive survey, characteristics, and application of IoT and the immense number of challenges raced and faced during the implementation of IoT frameworks. 2021 IEEE. -
An Analytical Study on the influence of using Trimmed Gait Energy Images for Human Gait Biometrics using Deep Learning
Gait based human recognition is founded on the principle that every human being has a distinctive style of walking. With the rise in the use of video surveillance devices, gait is one of the most convenient biometrics to use, in forensics. This paper is an analytical study of the effect of using trimmed Gait Energy Images (GEI) for Human Recognition using different deep learning techniques. Gait energy images are a spatiotemporal, silhouette-based representation of the human gait. GEIs from the CASIA B Multiview dataset was used to build two other sets of data by subtracting the upper body Deep learning and transfer learning techniques including Convolution Neural Networks (CNN) and VGG16 algorithms had been implemented to carry out the recognition. Results showed that the performance of the model using upper body images gives a greater accuracy than the lower body images. It has also been observed that the accuracy of recognition provided by the upper part of the body is almost the same as that achieved by the whole body, which brings forth the idea that the upper part of the body is the most pertinent in Human Identification using Gait as a biometric. 2022 IEEE.