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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 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 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. -
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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 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 factors associated with employee satisfaction in information technology companies
BACKGROUND AND OBJECTIVES: An employees satisfaction and performance are linked to the companys work discipline, personal factors, and organizational culture. This paper studies these three factors in the context of Information Technology companies and their connection to employee satisfaction. Job satisfaction is a significant issue in Information Technology Companies, leading to increased labour turnover in Information Technology Companies. The study highlights the relevance of Information Technology companies to understanding the reasons behind their employees satisfaction. Until now, little is known concerning the variants of job satisfaction among Information Technology employees, enriching the understanding in this particular professional area. The study was conducted to assess the job satisfaction needs of the employees in major Information Technology companies. The study helps to know the preferences and problems of the employees. METHODS: In this study, data was collected from employees from various Information Technology companies to uncover the factors that impact the satisfaction of employees. Considering the studys goal and the literature review, the technique was analytical and interpretive. Due to large populations random sampling method is convenient for the study. The studys objectives were achieved explicitly via the questionnaires design. To test the proposed hypotheses, all data were processed using the Structural Equation Modelling, Statistical Package for Social Science (SPSS) and Analysis of Moment Structures. FINDINGS: Information Technology companies need their employees to feel satisfied to achieve the overall objectives and remain loyal to the company to achieve company success. From the responses, we learned that 31% of the respondents were satisfied with their employer about the various allowances and benefits they receive. Also, we knew that around 50% of the respondents were happy with their choice of the company because of its future commitments. 102 of the respondents highly disagreed that they were satisfied with the attitude and nature of their employees. Also, 22.26% of the male respondents have said they are only sometimes motivated to go to work. The limitation of this study was that the collected data was only of the general employees of the Indian Information Technology companies and not to specific departments of those companies. Also, no categories of companies were defined as per turnover. CONCLUSION: By recognizing the importance of job satisfaction, managers can create an environment that motivates and engages employees, leading to better performance, increased productivity and reduced employee turnover 2024 Tehran Urban Research and Planning Center. All Rights Reserved. -
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 analogical study of the narrative techniques used in the film Paradesi (2013) an adaptation of Tamil translation (Yerium Panikkadu) of the novel 'Red Tea' /
International Journal Of Humanities and Social Science Invention, Vol.5, Issue 3, pp.1-6, ISSN: 2319-7722 (Online) 2319-7714 (Print). -
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 Algorithmic Approach to Intrusion Detection in Ad Hoc Wireless Networks Based on Artificial Intelligence
The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the potential of MANETs through increased and improved utilization. Standards for AI have been developed thanks to the most recent release of new machine learning technologies. Different security-related issues from malware assaults affect mobile ad hoc networks (MANETs). Any node operates as a router to move data without centralized control, making nodes more vulnerable to threats from other nodes or attackers because of their brief existence. Because of this, MANET needs particular security policies to detect the incorrect entrance of misbehaving nodes. If all nodes are self-assured and correctly collaborate, the networks function better. The paper presents a practical artificial intelligence algorithm-based security system that uses AdaBoost and DT algorithms to recognize and identify packet falling nodes, classify information packets as normal or abnormal, and detect insider threats in real-time. The results showed that DT performed better than AdaBoost, with a 98% accurate prediction rate. Consequently, DT is better able to recognize damaging attacks in MANETs. 2025 IEEE. -
An algorithm to detect an object in a confined space by using improved fingerprinting approach
The rapid evolution of location-based services has made tremendous changes in the society. In this paper, Trilateration method is implemented in fingerprinting methodology to obtain very precise and low error position details of the client portable device. Trilateration is a method in which the portable device is determined by the received signal strength intersecting at one position from the three reference points. Fingerprinting method involves several steps like training stage and positioning stage in which the training stage consists of the creation of the database of the signal strengths along with its associated location measurements. In the positioning step where effective and efficient received signal strength collected from the portable device is matched with the data saved into the database to get the position information of the client. The position of the user is estimated by collecting the received signal strengths from three reference points by using the concepts of trilateration approach in fingerprinting methodology to obtain more precise and accurate information. 2005 - ongoing JATIT & LLS. -
An algorithm for IoT based vehicle verification system using RFID
The verification of vehicle documents is an important role of transport department which is rising day by day due to the mass registration of the vehicles. An automated vehicle verification system can improve the efficiency of this process. In this paper, we propose an IOT based vehicle verification system using RFID technology. As a result, the vehicle checking which is done now manually can be replaced by automation. There is a loss of a significant amount of time when the normal vehicle checking is done manually. The proposed system will make this process automated. The present verification process is using inductive loops that are placed in a roadbed for detecting vehicles as they pass through the loop of the magnetic field. Similarly, the sensing devices spread along the road can detect passing vehicles through the Bluetooth mechanism. The fixed audio detection devices that can be used to identify the type of vehicles on the road. Other measurements are fixed cameras installed in specific points of roads for categorising the vehicles. But all these mechanisms cannot verify the documents and certificates of the vehicles. In our work, we have suggested an algorithm using RFID technology to automate the documentation verification process of the vehicles like Pollution, Insurance, Rc book etc with the help of RFID reader placed at road checking areas. This documents will be updated by the motor vehicle department at specific periods. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach
We perform research to develop a combined deep learning algorithm that enhances security threat detection within the Internet of Things networks. The resource variations across IoT devices create obstacles for Traditional Intrusion Detection Systems (IDSs) regarding their scalability and adaptability elements. This study explores the application of Bidirectional Recurrent Neural Networks and Long Short-Term Memory networks, which are trained on Traffic data records from NSL-KDD, a widely recognized benchmark dataset. It's a secondary dataset. This dataset is preprocessed and features are engineered to be optimized for sequential pattern recognition and handling of long-term dependency. Experimental results validate the achievement of a cross-validation accuracy of 93.40%, F1 is 91.62% and precision is 90.42%, which is greater than the individual models, such as CNN, BiRNN, or LSTM. The stacking Models Bi-RNN sequential learning and LSTM dependency retention makes the system perform better at threat classification along with elevated detection accuracy for IoT-related security issues like DoS, Probe, R2L, and U2R. The consistent performance of the model through this validation split provides evidence that the system can effectively handle IoT cybersecurity threats. 2025 IEEE. -
An AI-enabled research support tool for the classification system of COVID-19
The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM). Copyright 2023 Tiwari, Bhattacharjee, Pant, Srivastava and Snasel. -
An AI-Driven Framework for Computational Literary Analysis: Bridging English Literature and Technology
Artificial Intelligence is emerging to be significantly used in the field of digital humanities, yet the focus of achieving interpretability and predictive level of performance simultaneously in the field of literary analysis is challenging. This paper highlights the study of a newly proposed effective model that is the Interpretable and Pedagogical Artificial Intelligence Framework (IPAF), a novel model that is designed for the analysis of poetry, prose and drama, while suitable outputs are provided by the model that are explainable in the field of education and literature research. The traditional models, such as the Random Forest, XG Boost and the Light BGM model, were used as base learners and have been integrated into the IPAF model by using Term Frequency-Inverse Document Frequency (TF-IDF) and the BERT embedding towards feature-level representations. The evaluation of this framework is carried out by utilising standard levels of classification metrics such as precision, accuracy level, F1 score and the SHAP-based explainability, which is applied for identifying the significant features of text that are influencing predictions. The results show that the IPAF model significantly outperforms the existing baseline models, by achieving a model with high accuracy, stability and robustness, that provides actionable insights towards educators and literary researchers. The interpretability of the framework allows users towards linking the computational outputs of the model alongside stylistic and theme-based patterns, by bridging the need for qualitative analysis along critical understanding of the literature. The future scope of the work extends the IPAF model to explore multi-modal levels of literary analysis by integrating data for audio, text and image together for digital archives for the further enhancement of cross-genre interpretation and applications of pedagogy. 2025 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 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 AHP-TOPSIS Approach for Optimizing the Mechanical Performance of Natural Fiber-Based Green Composites
Natural fibers have emerged as an effective replacement for synthetic fibers in the fabrication of green composites to be used for producing various components in automotive, aerospace, and other applications. In this proposed study, the mechanical properties of banana and coir fiber-based green composites have been optimized by using a hybrid AHP-TOPSIS approach. Corn starch along with glycerol has been used as the matrix material for fabricating the green composites. The mechanical properties such as tensile strength, flexural strength, and impact strength of the developed green composite have been optimized with a focus on the utilization of this composite in automotive and aerospace applications. Three different weight percentages (0%, 5%, and 10%) of banana and coir fibers was considered for the fabrication of green composites. The constituents of the green composite have been taken as the input variables whereas the mechanical properties of the green composite are considered as the output variables for designing the experiment. The design of the experiment consisted of nine different combinations of input and output variables. Results of the study revealed that 5 wt.% of banana fiber, 10 wt.% of coir fiber, and 85 wt.% of corn starch provide the optimum mechanical performance of the developed green composites. 2022 A. N. Shankar et al. -
An Adversarial-Resilient Multi-Agent AI Framework for Autonomous Robotic Warfare Defense
Next-generation AI-enabled defense is essential to deter enemy drones, swarms, and autonomous vehicles in contested and deceptive environments, with modern battlefields becoming increasingly dependent on autonomous robot platforms. To discover and dissect and eliminate hostile autonomous threats in real-time, this study presents an integrated Adversarial-Resilient Swarm Defense AI Framework (AR-SDAI) with a Spatio-Temporal Transformer, a Multi-Agent Reinforcement Learning Countermeasure Module, and a Hybrid Graph Attention Network. To identify hidden or counterfeit threats and improve defense against enemy attacks, the system begins with applying a Transformer-based situational awareness model to merge multi-sensor battlefield data to fuse. Autonomous defense drones are then controlled by a multi-agent reinforcement learning framework to perform actions of dynamic electronic jamming and optimal interception maneuvers in a swarm environment. Finally, the system can find unusual patterns and create human-understandable counter-strategies for human-in-the-loop control with the help of a graph-based explainability layer that models the interactions of adversary swarms as dynamic graphs. Compared to traditional rule-based and CNN-RNN baselines using experiments on a simulated Red-Blue drone warfare test benchmark, the suggested AR-SDAI is better by 23% in threat detection accuracy, 31% in swarm interception success rate, and 19% in response latency. With its provision of robust, explainable, and flexible AI capability for next-generation robotic warfare settings, the paper in general enhances the state of autonomous defense operations. 2026 Saurav Mallik, Sandeep Kumar Mathivanan, Basu Dev Shivahare.