Transformation Capabilities Research Articles

The Future of Electric Vehicles: Navigating the Intersection of AI, Cloud Technology, and Cybersecurity

The emergence of electric vehicles (EVs) represents a paradigm shift in transportation, offering not only the promise of reduced carbon emissions but also the potential for enhanced sustainability and innovation. However, the full realization of EVs' transformative capabilities extends beyond mere electrification; it encompasses the integration of state-of-the-art technologies such as Artificial Intelligence (AI) and Cloud Computing. This meticulously crafted research article delves into the profound impact of AI and cloud technologies on the EV landscape within the United States. It meticulously examines how these technological advancements are reshaping EV ecosystems, catalyzing advancements in autonomous driving, optimizing battery management systems, and enriching user experiences. Furthermore, it elucidates the imperative need for robust cybersecurity measures to fortify these sophisticated systems against cyber threats, thereby ensuring the integrity, privacy, and stability of the transportation network. With a diverse audience in mind, including automotive industry professionals, policymakers, cybersecurity experts, environmental advocates, technology enthusiasts, and the broader public, this article serves as a beacon illuminating the future of transportation, sustainability, and digital security within the realm of EVs. Through a blend of rigorous analysis, insightful commentary, and visionary foresight, it aims to provide profound insights into the trajectory of EV technology in the United States and beyond.

The Influence of Transformative Learning, Affective Commitment, Digital Transformation, Capability Dynamic, Ambidexterity, and Education Policy Toward Educational Leadership: A Case from Indonesia

Purpose: The implications of external environment changes and the presence of a new generation of students in the school to the school leadership are often overlooked. Very few studies elaborate on the need to shape educational leadership concepts and practices to deal with the changes. The study intention is to identify the influences of transformative learning, affective commitment, digital transformation, capability dynamic, and the ambidexterity to educational leadership, and to craft the educational leadership based on Indonesia context. Design/Methodology/Approach: The study is using Covariance Based Structural Equation Modelling (CB-SEM), the data is collected through a Likert based questionnaire from 271 respondents and it is processed by SPSS and AMOS. Findings: Transformative Ethics Agile (TEA) Leadership as educational leadership is influenced by Ambidexterity, transformative learning, affective commitment, and educational policy. Digital transformation and capability dynamic have insignificant influences, however ambidexterity mediating the influence of dynamic capability to TEA Leadership. Research Implications: The research contributes to renewing educational leadership concepts by putting emphasis on transformative, ethics, and agile elements. The variables that must be put into attention for leading school during changing time, specifically ambidexterity, transformative learning, affective commitment, and educational policy. The research highlights the importance of context on educational leadership. Originality/Values: The research contributes to the identification of variables that influence the effectiveness of school leadership and by doing that able to facilitate the strategy of leadership development for school management. The values of the research are on the mapping of factors contribute to educational leadership and lead the practical aspects of managing school in the changing time.

Upcycling waste graphite from spent LIBs for fabrication of novel mesoporous carbon and p-GN/BT based supercapacitor

Lithium-ion batteries (LIBs) are rapidly gaining popularity around the world, and this trend is certain to continue. As a result of increased use, more end-of-use LIBs are discarded in landfills without recycling. Innovative LIB recycling methods are emerging to solve the simultaneous challenges of increasing waste and the need for environmentally friendly energy storage. Graphite, a fundamental LIB component, has long been underestimated in terms of its potential for upcycling. This study investigates into the potential of recycling graphite sourced from spent LIB as precursor for economical synthesis of P-doped graphene/BaTiO3 (p-GN/BT) and Mesoporous Carbon (MC). These materials are being investigated for their application in the efficient manufacturing of high-performance supercapacitor, showcasing transformative capability of graphite. Waste graphite recovered from LIB recycling is chemically treated to remove impurities and create a porous structure, which is subsequently utilized in forming the supercapacitor components. The supercapacitor has an outstanding specific capacitance of 150 F.g−1 at a current density of 1.0 A. g−1. The power density, measured at 3260 W.kg−1, and the energy density, measured at 83.3 Wh.kg−1, are both substantially higher. Due to its exceptional cyclic stability, it can endure 10,000 charging and discharging cycles with negligible decline in performance. The findings indicate that the upcycling of graphite from retired LIBs may be a viable source for the construction of high-efficiency supercapacitors. This not only offers an eco-friendly energy storage solution, but also promises cost savings and increased profitability in recycling efforts.

Transforming Agriculture through Artificial Intelligence: Advancements in Plant Disease Detection, Applications, and Challenges

Artificial intelligence (AI) has emerged as a revolutionary tool in agriculture, particularly in the realm of plant disease detection. This article provides an overview of AI-powered plant disease detection methods, their applications, and the limitations associated with their implementation. By leveraging AI, farmers can enhance crop management practices, optimize resource utilization, and mitigate yield losses caused by plant diseases. However, challenges such as data scarcity, model interpretability, and deployment in resource-constrained environments remain significant barriers to widespread adoption. Addressing these limitations is crucial for maximizing the potential of AI in revolutionizing agriculture and ensuring global food security. Artificial intelligence (AI) has emerged as a pivotal tool in modernizing agriculture, particularly in the domain of plant disease detection. This article presents a comprehensive examination of AI-driven methodologies for plant disease detection, exploring their applications and inherent limitations. Through the utilization of machine learning and computer vision techniques, AI facilitates early disease identification, precision agriculture, disease surveillance, and decision support systems. Despite these transformative capabilities, challenges such as inadequate data availability, model interpretability, and implementation in resource-constrained settings impede widespread adoption. Addressing these obstacles is imperative for fully harnessing the potential of AI in agricultural innovation, thereby safeguarding global food security and sustainability.

An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging

With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capability, we propose a fusion model that integrates the functions of the stacked denoising autoencoder (SDAE) and the Transformer model in order to improve the performance of RUL prediction. Firstly, the health factors under three different conditions are extracted from the measurement data as model inputs. These conditions include constant current and voltage, random discharge, and the application of principal component analysis (PCA) for dimensionality reduction. Then, SDAE is responsible for denoising and feature extraction, and the Transformer model is utilized for sequence modeling and RUL prediction of the processed data. Finally, accurate prediction of the RUL of the four battery cells is achieved through cross-validation and four sets of comparison experiments. Three evaluation metrics, MAE, RMSE, and MAPE, are selected, and the values of these metrics are 0.170, 0.202, and 19.611%, respectively. The results demonstrate that the proposed method outperforms other prediction models in terms of prediction accuracy, robustness, and generalizability. This provides a new solution direction for the daily life prediction research of lithium-ion batteries.

Relationship between Occupational Pension, Corporate Social Responsibility (CSR), and Organizational Resilience: A Study on Listed Chinese Companies

Numerous researchers acknowledge that the occupational pension protects employees. However, in China, the total cost of occupational pensions is shared between employees and employers, representing a significant financial commitment. This study aimed to explore the effect of the occupational pension on corporate social responsibility (CSR) and organizational resilience. Drawing on insights from cost-stickiness and resource-based theories, we developed a model that elucidated the influence of occupational pensions on firms’ approaches to CSR within the context of COVID-19 and how this, in turn, impacted organizational resilience. This study categorized CSR into strategic and responsive activities, employing the concept of cost stickiness as a framework. We analyzed a sample of 34,145 observations from Chinese A-share listed companies spanning the period 2010–2023 to examine the influence of occupational pension adjustments on CSR strategies. The findings of this study revealed that the cost pressure associated with contributions to occupational pensions prompted firms to decrease their engagement in responsive CSR activities while enhancing their strategic CSR initiatives. Furthermore, it was observed that strategic CSR contributed to improved organizational resilience, whereas responsive CSR did not exhibit the same effect. The relationship between occupational pension contributions and CSR was found to be significantly and negatively moderated by factors such as the minimum wage and population aging. Conversely, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities.

Low-Cost Data, High-Quality Models: A Semi-Automated Approach to LOD3 Creation

In the dynamic realm of digital twin modeling, where advancements are swiftly unfolding, users now possess the unprecedented ability to capture and generate geospatial data in real time. This article delves into a critical exploration of this landscape by presenting a meticulously devised workflow tailored for the creation of Level of Detail 3 (LOD3) models. Our research methodology capitalizes on the integration of Apple LiDAR technology alongside photogrammetric point clouds acquired from Unmanned Aerial Vehicles (UAVs). The proposed process unfolds with the transformation of point cloud data into Industry Foundation Classes (IFC) models, which are subsequently refined into LOD3 Geographic Information System (GIS) models leveraging the Feature Manipulation Engine (FME) workbench 2022.1.2. This orchestrated synergy among Apple LiDAR, UAV-derived photogrammetric point clouds, and the transformative capabilities of the FME culminates in the development of precise LOD3 GIS models. Our proposed workflow revolutionizes this landscape by integrating multi-source point clouds, imbuing them with accurate semantics derived from IFC models, and culminating in the creation of valid CityGML LOD3 buildings through sophisticated 3D geometric operations. The implications of this technical innovation are profound. Firstly, it elevates the capacity to produce intricate infrastructure models, unlocking new vistas for modeling digital twins. Secondly, it extends the horizons of GIS applications by seamlessly integrating enriched Building Information Modeling (BIM) components, thereby enhancing decision-making processes and facilitating more comprehensive spatial analyses.

THE IMPACT OF DIGITAL TRANSFORMATION AND LEADERSHIP ON ORGANIZATIONAL RESILIENCE IN DISTANCE EDUCATION INSTITUTION: HIGHER-ORDER SEM APPROACH

To overcome the difficulty and unprecedented suspensions due to the COVID-19 pandemic, every organization is required to consider strategic steps to sustain, one of which is by implementing digital transformation and developing leadership capability. This study aims to determine the impact of digital transformation and leadership capability in maintaining the organization, particularly in distance education. This study used questionnaires distributed to 402 students to obtain their opinions on digital transformation, leadership capability, and organizational resilience in Universitas Terbuka, a founder of distance education institutions in Indonesia. Data were analyzed using descriptive and Higher-Order Structural Equation Modeling analysis. Based on the modelling, it is shown that the modelling had a good value of the Goodness of Fit Indicator through a standardized loading factor (SLF) more than the tolerable loading factor limit (>0.50). The Variance Extracted (VE) and Construct Reliability (CR) values also showed that the questions on every indicator used in this study were sufficiently measurable and reliable. The results of hypothesis testing indicated that there was a positive and significant impact of digital transformation and leadership capability on the organızatıonal resılıence of Universitas Terbuka.

Evaluation of rural ecological resilience from the perspective of communities and farmers: a study on Laochehe ethnic minority village in China

The ecological problems of tourism villages in the context of globalization cannot be ignored. Rural tourism has destroyed the rural ecological environment, and it is of great significance to explore the ecological resilience of tourism villages to achieve sustainable development. This study introduces an analytical framework with which to assess the ecological resilience of rural tourism locales, focusing on micro-level perspectives involving communities and farmers. Ecosystem service values and a comprehensive index are utilized as proxies to characterize ecological resilience, and its evolutionary characteristics and mechanisms are investigated. The results are as follows. (1) From the community perspective, rural land use has changed significantly from the agricultural function to the tourism compound function. During this transition, the ecological resilience index has decreased by 2.05 × 106. (2) From the perspective of farmers, the rural ecological resilience index and various dimensional indices have altogether increased. Notably, the transformation capability index has increased by 30.34%, the interference capability index has increased by 21.78%, and the response capability index has increased by 14.72%. The ecological resilience indices for different livelihood types, from highest to lowest, are ranked as follows: tourism-oriented farmers (0.4412), tourism-participating farmers (0.4274), and migrant-oriented farmers (0.4066). (3) Socio-economic factors and farmers’ livelihoods lead to the imbalance of the rural social ecosystem. The adaptive transformation of tourism villages has facilitated the dynamic evolution of ecological resilience. This study contributes to the enrichment of ecological resilience research perspectives and may offer valuable insights for the sustainable development of tourism villages in similarly developing countries around the world.

Effective Data Migration Strategies Using Talend and DataStage

Modern company operations need reliable data migration across systems to preserve continuity, performance, and business process integrity. Data transfer techniques need careful preparation, powerful tools, and system knowledge. Talend and IBM DataStage, two renowned data integration and migration systems, are used in this study to implement successful data transfer techniques. Talend and DataStage provide ETL, data quality management, and real-time integration services for data migration. Talend, an open-source platform, supports several data sources and formats with flexibility, scalability, and many connections. Organizations seeking cost-effective solutions appreciate it for its real-time data processing, user-friendly interface, and comprehensive data transformation capabilities. Talend's powerful data profiling, cleaning, and governance tools assure high-quality, compliant data migration. IBM DataStage, part of IBM InfoSphere, is a sophisticated ETL tool for large-scale data integration. DataStage is ideal for complicated data settings with big data volumes because to its great performance and scalability. DataStage can handle the most difficult data migration operations due to its parallel processing and interaction with IBM's data management environment. Enterprises who need a dependable data migration solution can benefit from the platform's metadata management, data lineage, and real-time data integration, which interface with many databases and applications.

Learning achievement in purposive communication of ESL college students: A blended learning approach

With the ongoing studies of blended learning in different areas, the study contrasts blended learning with online classes by analyzing pre/post-test scores, grades, and activity scores, indicating blended learning might offer superior outcomes. Additionally, it explores college students’ experiences in a blended learning class, providing insights into its effectiveness and areas for improvement. Participants are engineering students from Camarines Sur Polytechnic Colleges, Camarines Sur, Philippines, which includes 45 participants for each group (controlled and experimental groups). The study was based on a triangulation approach of integrating Independent Samples T-test and Multiple Regression Analysis for quantitative data and semi-structured interviews for qualitative insights. Accordingly, certain activities were crucial in improving non-verbal communication, persuasive writing, and research skills that led to confidence build-up and improved students’ performances. Narrative descriptions give more enlightening information about boosted confidence and better presentation competence due to better communication skills. Writing skills go a long way in improving students’ academic achievement, and Research and writing practices are essential skills that help students achieve higher grades. Integrating the qualitative narratives and quantitative results adds credibility to the study since it gives a different perspective on the blended learning environment. The recommendations to instructors and instructional designers call for blending unique instruction methods, multimedia content, and individualized feedback options. It is also advised to make pedagogical revisions for less impactful activities and provide customized exercises to build student confidence. Finally, this study illustrates the transformative capabilities of blended learning to enhance ESL teaching practice. The insights deepen pedagogue practices and broadly contribute to ongoing discussions around effective ESL education strategies toward better learning.

Design and Implementation of GaN-Based Two Switch Flyback With Synchronous Rectification for New Space Applications

This article presents a comprehensive investigation and implementation of a continuous conduction mode pulse width modulated two-switch DC/DC flyback converter utilizing Gallium Nitride Field Effect Transistors (GaNFETs) for New Space applications. The New Space concept advocates for the integration of Commercial Off-The-Shelf (COTS) materials in the space industry. The approach offers cost-effective solutions and reduces lead time for space designs. The study provides steady-state analysis and design considerations for two switch flyback converters, highlighting the transformative capabilities of GaNFETs, especially in high-frequency operations. The rationale behind the preference for GaNFETs over MOSFETs in satellite power supply applications and the strategic benefits of COTS materials are explained in detail. In addition, the synchronous rectification method is implemented to obtain better efficiency, particularly for high-output current operations. The converter achieves an efficiency of over 87% when the load exceeds 50%, with varying input voltages from 20 V to 40 V and a switching frequency of 300 kHz. The experimental findings show that the designed converter can serve as a high efficiency and reliable satellite power converter.

SYNTHESIZING AI'S IMPACT ON CYBERSECURITY IN TELECOMMUNICATIONS: A CONCEPTUAL FRAMEWORK

As the telecommunications sector increasingly relies on interconnected digital infrastructure, the proliferation of cyber threats poses significant challenges to security and operational integrity. This review presents a conceptual framework for understanding and harnessing the potential of artificial intelligence (AI) in fortifying cybersecurity within the telecommunications industry. The framework integrates the transformative capabilities of AI with the unique demands of cybersecurity in telecommunications, aiming to enhance threat detection, mitigation, and response strategies. It encompasses a multidimensional approach that encompasses both technical and organizational facets, recognizing the interconnectedness of technology, human factors, and regulatory environments. Firstly, the framework delves into the application of AI in bolstering proactive threat intelligence gathering and analysis. Through advanced algorithms and machine learning techniques, AI empowers telecom operators to identify anomalous patterns, predict potential vulnerabilities, and pre-emptively adapt defensive measures. Secondly, it explores AI-driven solutions for dynamic risk assessment and adaptive cybersecurity protocols. By leveraging real-time data analytics and automated decision-making, telecom networks can swiftly adapt to evolving threats and ensure continuous protection against intrusions or breaches. Furthermore, the framework emphasizes the role of AI in augmenting human capabilities through intelligent automation and cognitive assistance. By offloading routine tasks and providing context-aware insights, AI enables cybersecurity professionals to focus on strategic initiatives and complex threat scenarios. Lastly, the framework addresses the imperative of ethical considerations, accountability, and transparency in deploying AI for cybersecurity in telecommunications. It advocates for responsible AI governance frameworks that prioritize privacy, fairness, and bias mitigation while fostering collaboration across industry stakeholders. In summary, this conceptual framework provides a roadmap for harnessing AI's transformative potential to fortify cybersecurity resilience in telecommunications, thereby safeguarding critical infrastructure and ensuring the integrity of global communication networks.
 Keywords: AI, Cybersecurity, Telecommunication, Framework, Conceptual, Impact, Review.

Enhancing Natural Language Processing (NLP) through VIKOR Method: A Comprehensive Approach for Improved Computational Linguistics

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR

In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of −15 dB and above (mAP@0.5 > 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods.

Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study.

Previous research applying large language models (LLMs) to medicine was focused on text-based information. Recently, multimodal variants of LLMs acquired the capability of recognizing images. We aim to evaluate the image recognition capability of generative pretrained transformer (GPT)-4V, a recent multimodal LLM developed by OpenAI, in the medical field by testing how visual information affects its performance to answer questions in the 117th Japanese National Medical Licensing Examination. We focused on 108 questions that had 1 or more images as part of a question and presented GPT-4V with the same questions under two conditions: (1) with both the question text and associated images and (2) with the question text only. We then compared the difference in accuracy between the 2 conditions using the exact McNemar test. Among the 108 questions with images, GPT-4V's accuracy was 68% (73/108) when presented with images and 72% (78/108) when presented without images (P=.36). For the 2 question categories, clinical and general, the accuracies with and those without images were 71% (70/98) versus 78% (76/98; P=.21) and 30% (3/10) versus 20% (2/10; P≥.99), respectively. The additional information from the images did not significantly improve the performance of GPT-4V in the Japanese National Medical Licensing Examination.

Achieving Environmental sustainability through the adoption of industry 4.0: an exploratory case study within the information technology industry

Abstract In the present-day competitive business landscape, integrating Industry 4.0 has transitioned from a choice to a necessity for companies striving to maintain their edge. Given the automation functions of IoT, the data management and transformation capabilities of AI, and the traceability benefits provided by Blockchain, this imperative is now more evident than ever. While widespread interest in Industry 4.0 is prevalent, the uncertainties surrounding the implementation process pose notable challenges. For this reason, in this paper, we present a single case study of a firm that operates in the information technology market to showcase the implementation process and how they overcome the challenges of digital transformation. Furthermore, the effect of this implementation on environmental sustainability experienced by the company and three of its customers was discussed.

Leveraging cognitive digital twins in industry 5.0 for achieving sustainable development goal 9: An exploration of inclusive and sustainable industrialization strategies

In the emerging landscape of Industry 5.0, the integration of innovative technologies such as Cognitive Digital Twins (CDTs) is pivotal for steering industrial processes towards sustainability and inclusivity, particularly in developing nations striving to align with Sustainable Development Goals (SDGs). The study explores the transformative capabilities of CDTs in Industry 5.0, concentrating on India's quest for inclusive and sustainable industrialization in alignment with SDGs. The inclusion of the TOE-HOT framework enriches the research's analytical foundation, providing a nuanced perspective to investigate the multifaceted dynamics of technology, organization, environment, and human aspects within the sphere of industrial evolution. By employing the Best-Worst Method (BWM), Interpretive Structural Modelling (ISM), and MICMAC analysis, the study delves deep into the synergies between industrial strategies, technology, and sustainable development. The findings reveal the paramountcy of the Technological Dimension, emphasizing CDTs strategies like real-time optimization and data analytics, which are instrumental in dynamically adjusting manufacturing processes and fostering alignment with market demands. The research also stresses the roles of various CDTs strategies like strategic alignment, change management, job security, skill augmentation, and environmental compliance in industrial sustainability and inclusivity. The paper concludes with invaluable insights for policymakers, industry leaders, and researchers, emphasizing the necessity for a holistic, interconnected approach to fully harness the potential of CDTs in realizing the aspirations of Industry 5.0 and contributing to India's sustainable industrial advancement by 2030.

Naturalize Revolution: Unprecedented AI-Driven Precision in Skin Cancer Classification Using Deep Learning

Background: In response to the escalating global concerns surrounding skin cancer, this study aims to address the imperative for precise and efficient diagnostic methodologies. Focusing on the intricate task of eight-class skin cancer classification, the research delves into the limitations of conventional diagnostic approaches, often hindered by subjectivity and resource constraints. The transformative potential of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms is underscored, emphasizing significant improvements in accuracy and accessibility. Methods: Utilizing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis is conducted, employing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering “Naturalize” augmentation technique is introduced. This technique leads to the creation of two indispensable datasets—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The “Naturalize” augmentation technique involves the segmentation of skin cancer images using the Segment Anything Model (SAM) and the systematic addition of segmented cancer images to a background image to generate new composite images. Results: The research showcases the pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer. The proficiency of AI in analyzing vast datasets and discerning subtle patterns significantly augments the diagnostic prowess of dermatologists. Quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations are meticulously evaluated. The culmination of these endeavors resulted in an unprecedented achievement—100% average accuracy, precision, recall, and F1-score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. Conclusion: This groundbreaking exploration highlights the transformative capabilities of AI-driven methodologies in reshaping the landscape of skin cancer diagnosis and patient care. The research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care. This pioneering work paves the way for a new era in dermatological diagnostics, heralding the dawn of unprecedented precision and efficacy in the identification and classification of skin cancers.

Sentiment Analysis Using Transformer Method

This research delves into sentiment analysis, employing the transformative capabilities of transformer method. Specifically, leveraging BERT Models, the study aims to advance sentiment classification accuracy by intricately capturing contextual nuances and positive or negative comment on IMDB movies reviews. The transformer architecture's distinctive attention mechanism proves pivotal in comprehending intricate relationships between words, facilitating a profound understanding of sentiment in textual data. Through extensive experimentation, the study establishes the transformative prowess of these methods, showcasing their effectiveness in achieving state-of-the-art results in sentiment analysis tasks. This investigation not only contributes to the evolving landscape of sentiment analysis but also underscores the significance of transformer-based approaches in deciphering the subtleties of human expression within textual data specially for Bert models. This research will predict sentiment analysis on comment of IMDB movies and shows some results which are 3% of loss, 60% off loss validation, 98% of accuracy and 90% of validation accuracy