Rise Of Artificial Intelligence Research Articles

New Frontiers in Breast Cancer Imaging: The Rise of AI

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

FRAUD DETECTION AND PREVENTION IN FINANCIAL INSTITUTIONS

Financial fraud, characterized by deceptive practices aimed at securing financial gains, has emerged as a prevalent threat within companies and organizations. Traditional methods like manual verification and inspection are inaccurate, expensive, and time-consuming in identifying fraudulent activities. With the rise of artificial intelligence, machine learning-based approaches offer a promising solution to detect fraudulent transactions through the analysis of vast financial datasets. This paper presents a systematic literature review (SLR) that systematically surveys and consolidates existing literature on machine learning (ML)-based fraud detection. Employing the Kitchenham approach, which employs defined protocols to extract and synthesize relevant articles, this review reports the findings from various studies gathered through specified search strategies from electronic databases. After applying inclusion/exclusion criteria, 93 articles were selected, synthesized, and analyzed. The review outlines popular ML techniques utilized for fraud detection, prevalent fraud types, and evaluation metrics. The surveyed articles indicate that support vector machine (SVM) and artificial neural network (ANN) are commonly employed ML algorithms for fraud detection, with credit card fraud being the most addressed fraud type using ML techniques. Additionally, the paper discusses key issues, gaps, and limitations in financial fraud detection and suggests potential areas for future research. Furthermore, the review identifies challenges like class imbalance in datasets and the dynamic nature of fraud patterns. While SVM and ANN are popular, exploring ensemble methods, deep learning, and anomaly detection techniques shows promise for improving detection accuracy in suitability. Future research should address these limitations and foster collaboration between academia, industry, and regulatory bodies to advance fraud detection methods and safeguard financial transactions.

Rise of Artificial Intelligence in Business and Industry

The integration of artificial intelligence (AI) into business and industry is catalyzing a paradigm shift in how organizations operate, innovate, and interact with stakeholders. This abstract explores the multifaceted implications of AI across various domains, highlighting its role in automation, predictive analytics, personalized customer experiences, supply chain optimization, enhanced decision-making, natural language processing, product innovation, risk management, fraud detection, healthcare advancements, and workforce augmentation. By leveraging AI technologies, businesses can automate repetitive tasks, anticipate trends, tailor experiences, optimize operations, mitigate risks, and foster innovation. However, the widespread adoption of AI also poses ethical and societal challenges, including concerns about job displacement, data privacy, and algorithmic bias. Therefore, a holistic approach that balances technological advancement with ethical considerations is essential to harness the full potential of AI while ensuring its responsible and equitable deployment in business and industry.

Deep learning approaches to the game of Connect6

Especially with the rise of artificial intelligence, people need more ability to think logically and make judgments than before. Board games can be used as a tool for training logic, which has a positive effect on the sustainable development of society. Connect6 is a very popular board game because it is a fair and highly complex game with simple rules. This research aims to address the technical challenges of building a game-based logical learning system with a deep neural network for Connect6. We used heuristic knowledge to establish the knowledge base. Our novel algorithm, based on residual deep convolutional neural networks (DCNNs), outperforms conventional approaches. We compare neural network architectures, demonstrating the superiority of residual DCNNs.Our AI program, “Kavalan,” achieves remarkable search performance. Furthermore, the introduced approaches lend themselves to application in other sudden-death board games, such as Gomoku, the endgame of Chess and Shogi.

The role of AI and mobile apps in patient-centric healthcare delivery

Patient-centric healthcare is an approach to healthcare that prioritizes the needs, preferences, and values of patients in the delivery of medical services. As part of a patient-centric model, patients take active part in decision-making regarding their own care, while healthcare providers tailor treatments and services to each patient's needs. The rise of artificial intelligence (AI) and mobile applications (apps) has now opened new avenues for enhancing patient engagement and improving healthcare outcomes, so by leveraging AI's sophisticated data analytics and predictive capabilities, healthcare providers are able to gain unprecedented insights into patient health patterns, thereby enabling personalized, and proactive care plans tailored to the patient’s individual needs. Furthermore, mobile apps have become potent tools, giving patients smooth access to healthcare resources, virtual medical consultations, and real-time health tracking, boosting patient independence and enabling them to manage their health with greater ease. The integration of AI and mobile apps into healthcare delivery is revolutionizing patient-centric care by enhancing patient engagement, empowerment, and self-management capabilities. However, addressing concerns around data privacy, security, and digital literacy is crucial for successful implementation. This study outlines a framework for harnessing these technologies to enhance patient outcomes and overall healthcare experiences. It explores the roles AI and mobile apps play in facilitating patient-centric healthcare and examines how the use of these technologies can promote personalized care, boost patient education and self-management, and improve communication between patients and healthcare providers.

The emergence of AI in mental health: A transformative journey

This paper traces the evolution of Artificial Intelligence (AI) in psychiatry, from early attempts like the ELIZA program to the integration of neuroimaging studies and the emergence of digital interventions. Utilizing deep learning models, AI now demonstrates high accuracy in classifying psychiatric disorders, such as schizophrenia based on functional connectivity patterns. The rise of AI chat/therapy bots, exemplified by Woebot and Tess, addresses mental health challenges, providing accessible and effective cognitive behavioral therapy tools. Innovative interventions like avatar therapy and intelligent animal-like robots contribute to broadening therapeutic perspectives. While AI-based interventions offer advantages in reducing stigma and increasing accessibility, challenges including diagnostic accuracy, legal responsibilities, and privacy concerns must be carefully considered. Despite challenges, the significant potential of AI in psychiatry calls for large, controlled trials, national standards, and responsible integration into clinical practice. The convergence of AI and psychiatry represents a technological revolution poised to transform mental health care delivery globally.

Study on Class Management Change and Teacher Orientation under the Vision of Artificial Intelligence

With the rise of artificial intelligence (AI), class management is experiencing unprecedented changes, but it is also facing certain risks and tests. It is of great practical significance to clarify the class management changes and teacher orientation under the vision of AI. Since the application of AI in the field of education, class management has realized the burden reduction and efficiency increase under the ability of technology, and has changed from the traditional mode of standardization, one-sided and unitization to a personalized, systematic and diversified intelligent mode, which provides great convenience for the management of class teachers. However, at the same time, the rise of AI has also caused a series of dilemmas such as the disclosure of students’ privacy and the challenge of the main value, which has put forward higher requirements for teachers’ literacy, positioning and management. Based on this, this paper puts forward the orientation of teachers in class management under the vision of AI. Teachers should improve their intelligent application literacy and boost the level of class management. Teachers should reasonably reflect on intelligent technology and grasp the applicable management situation. Teachers should fill the gaps in intelligent management and pay attention to humanistic emotional assistance to promote the positive integration of AI and class management.

Mediated trust, the internet and artificial intelligence: Ideas, interests, institutions and futures

AbstractThis paper addresses the question of trust in communication, or mediated trust, with regard to the historical evolution of the Internet and, more recently, debates around the impacts of artificial intelligence (AI). At a conceptual level, it proposes a ‘Three I's’ framework of ideas, interests, and institutions as a way of understanding how and why current proposals for greater regulation of digital platforms counterpose questions around credibility and social licence for digital tech giants against a dominant set of ideas around the Internet as a privileged domain of free speech. By contrast, the rise of AI comes at a time when data‐driven business models associated with dominant platform businesses are in the ascendancy, so institutional arrangements need to be considered as a form of countervailing power to the capacity of tech giants to use AI to further consolidate forms of economic, political and communications power.

Rethinking job insecurity research: Challenging the homogeneous, linear, and negative effects of job insecurity

AbstractThe rise of artificial intelligence is reshaping the future of work, making a discussion on job insecurity both timely and imperative. In this commentary, I critically examine several assumptions presented in Klug et al.'s lead article and propose alternative avenues to advance the field. First, I argue for a nuanced understanding of job insecurity by distinguishing between cognitive and affective dimensions, as well as between quantitative and qualitative aspects. By avoiding oversimplification, we can better capture the complex experiences of job insecurity. Second, I challenge the prevailing notion of job insecurity as solely a hindrance stressor with uniformly negative consequences. Instead, I emphasize its potentially motivating effects under certain conditions and advocate for exploring its curvilinear effects on outcomes. Third, I advocate for the adoption of competing theoretical frameworks to enrich our understanding of job insecurity's implications across multiple levels, especially focusing on outcomes at the team, unit, and organizational levels. Finally, I call for more intervention studies aimed at mitigating job insecurity at its sources and improving the well‐being outcomes of job‐insecure employees. To sum, I highlight the importance of challenging conventional assumptions and fostering innovative approaches in job insecurity research.

Enhancing Skin Disease Classification: A Novel Approach With Tailored Loss Functions And SMOTE Sumeet Ghumare

Although skin disease like skin cancer is a common health problem worldwide, early identification is crucial to its recovery. By using medical photos, the rise of artificial intelligence (AI) has transformed the detection of skin diseases. Despite the reality that various deep learning models have been created with this goal in mind, class imbalance in multi class classification all poses a serious difficulty and imbalance among the classes. This work offers a novel strategy to reduce class imbalance in the categorization of skin diseases, leading to significant accuracy gains. One of the largest skin cancer datasets currently accessible, it consists of 33,106 dermoscopy photos from seven different kinds of skin diseases as a starting point. The main goal is to enhance skin disease classification algorithms’ performance on this imbalance dataset. We are going to use a two-pronged strategy to combat the class disparity and GAN (Generative adversarial network) Based Oversampling or Meta-learning in order to successfully equalize the representation of each class during training, we first design a balanced Dynamic Mini-Batch Size logic, SMOTE (Synthetic Minority Oversampling Technique) and Augmenter. Second, we provide a new loss function designed to take into account the unique features of the dataset. The outcomes are startling. These findings underscore the efficacy of our hybrid approach in training deep convolutional neural networks for imbalanced skin disease datasets. By combining data-level balancing techniques with a carefully designed loss function at the algorithm level, we address the challenge of among the classes, paving the way for more accurate and reliable skin cancer diagnoses. Key Words: Dermoscopy images, DenseNet169, Convolution neural network, SMOTE (Synthetic Minority Over-sampling Technique, GAN(Generative adversarial network), Mini-Batch Logic, Augmenter

Impact of AI on Healthcare: A Descriptive Study

Artificial intelligence (AI) has become a multifaceted breakthrough in healthcare with the promise of transforming patient care and clinical practices. The paper presents a comprehensive retrospective mainly concerned with AI and its future scope in health-care services. The paper offers an overview of prevailing trends, illustrating AI’s transformative potential towards diagnostic accuracy, treatment efficiency and operational productivity in healthcare systems. This research paper will delve into the rise of AI in healthcare, from its early stages to technologies that are cutting edge and influencing the future medicine. Furthermore, we will investigate how AI affects patient outcomes, healthcare staff dynamics and the overall healthcare ecosystem through aprism of multidimensionality. The goal of the research as such is to equip all takeholders with historical trends, current advancements and future trajectories so that they can use AI in healthcare effectively but responsibly. The aim is to create a healthcare environment where there are AI driven improvements in patient care which provide equal opportunity for all.

Yet another turn? priotising the needs of diplomacy over the capabilities of generative AI

AbstractIn this forum piece, we argue that the widespread heraldry regarding artificial intelligence (AI) as a panacea in diplomacy and articulating research agendas on the changes it might bring are potentially clouding the future hardships of diplomacy. With all its subfields, International Relations (IR) has gone through numerous “turns”, especially during the last two decades which made encounters poised to change the nature of foreign policy—such as new actors, ideas, or technologies—a familiar experience. While these discussions enriched the discipline of IR, hardly any of these turns lived up to their promises. Certainly, we have an increasingly challenging and complex multipolar world ahead of us. This manifests that a broader network of actors, interests, and technologies needs to be considered. AI, indeed, has the potential capacity to assist and disrupt the ways diplomacy works. Yet heralding an anticipatory practice and study of diplomacy based on AI’s socio-technical imaginaries and calculations rather than as a participatory process centered on immediate human interaction, resources, intelligence, and rapport bears the potential of obscuring the analytical clarity needed. In short, we argue that the rise of AI should not be discussed as yet another new turn poised to cure diplomacy and international relations. We conclude our piece by reminding scholars to bring analytical focus on what lies at the heart of diplomacy.

Quo vadis Indonesian public relations profession: dilemma on the rise of AI

The debate over AI has focused on its contrast with human employment. Simple perspectives are inadequate in public communication fields like Public Relations (PR). The conversation must shift from discussing AI's impact on human jobs to how AI will shape public communication as AI becomes more important in PR. To examine AI's effects on Public Relations, this study uses a literature review focusing on authority and public voice. Due to the risks of AI misuse, data discrimination, and data poisoning, public relations must carefully consider the implications of AI integration. Artificial Intelligence (AI) in Public Relations is studied using a literature review as its main method. Experts are consulted to identify AI's most significant challenges and opportunities in public relations. Public relations (PR) are affected by Artificial Intelligence (AI), which presents both opportunities and challenges. AI can boost public communication, but it also risks data misuse and discrimination. Ethical issues arise when AI represents the public's voice. This study provides useful insights into how Indonesia's Public Relations profession should approach Artificial Intelligence. PR professionals can overcome technical and ethical challenges and revolutionise public communication with AI integration. Therefore, ethical PR practice and the use of AI are becoming increasingly important.

The Rise of Artificial Intelligence in Language Translation: A Comprehensive Review

The Rise of Artificial Intelligence in Language Translation: A Comprehensive Review

Organic Thermoelectric Devices for Energy Harvesting and Sensing Applications

AbstractThe rise of artificial intelligence and the Internet of Things have spurred an increasing demand for wearable, sustainable, and maintenance‐free power sources. Organic thermoelectric (OTE) devices are emerged as promising candidates because they are capable of converting heat energy into electricity directly without the need of moving parts, capable of flexible and seamless integration with multifunctional miniaturized electronics. In addition, OTE devices can perform straightforward as various self‐powered sensors, boosting their applications in the field of intelligent interactions. This review focuses on recent advances in OTE materials and devices for applications in energy harvesting and sensing. The basic knowledge and key parameters of OTE materials and devices are presented, followed by detailed introduction of recent progress of OTE generators, sensors, and other OTE‐integrated devices. Next, several aspects of optimizing OTE devices toward multifunctional applications are highlighted. Finally, an overview of the current challenges and future research directions of OTE‐based devices is addressed. It is hoped that this review can pave the way for speeding up a bright future for the development and practical applications of OTE devices.

Security issues of news data dissemination in internet environment

With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment.

Bridging technology and pedagogy from a global lens: Teachers’ perspectives on integrating ChatGPT in English language teaching

The rise of Artificial Intelligence in educational contexts has sparked both excitement and trepidation, highlighting the urgent need to investigate its implications, especially in English Language Teaching. In light of this context, this study aimed to find out how English language teachers perceive the pedagogical benefits and challenges posed by ChatGPT when incorporated into ELT and identify potential avenues for such digital innovations. Adopting a qualitative research design, data were purposively collected by distributing an open-ended questionnaire to 46 English language teachers from multiple countries. This sample was solicited via the academic and research platform ResearchGate, assuring a broad representation of academic ranks and teaching experience. Thematic analysis was used to unpack the rich textual responses. Findings revealed that, while teachers recognize ChatGPT's potential to facilitate personalized and dynamic learning interactions, they also harboured perceptible concerns regarding linguistic fidelity, potential overreliance on the tool, and the possibility of creativity suppression. In addition, the perceived limitations of the instrument in developing crucial language skills such as listening and speaking were highlighted. The data further emphasized the need for targeted professional development and agile curriculum adaptation to maximize the potential of ChatGPT and other AI tools. This research contributes to the burgeoning discourse on AI's interaction with ELT by incorporating a global perspective, making it invaluable for teachers, curriculum designers, and tech innovators. Limitations, recommendations and implications were provided.

The Investigation of Performance Comparison for VGG, YOLO, and DINO in Image Classification

The rise of artificial intelligence has led to a proliferation of deep learning models, yet there remains a noticeable shortage of comparative analyses, particularly among computer vision models rooted in different design philosophies. As such, this study seeks to delve into the strengths of various models through an examination of their structural attributes, with the aim of offering insights that can inform the development of more high-performing models in the future. This study first selects three representative models with different design ideas in their respective research directions, and preliminarily distinguishes the differences between different models. Then, through experiments on the dataset, the performance of different models is obtained, and the reasons for their current performance are analyzed. In this experiment, four models, VGG16, YOLOv5, YOLOv8, and DINOv2, were deployed and tested using the Fruit 360 dataset. The final accuracy was 0.955, 0.997, 0.998 and 0.986, respectively. The accuracy of YOLO model and DINO model was much higher than that of VGG model. The reason for this result may be related to the introduction of anchor boxes in the YOLO model and attention mechanisms in the DINO model, both of which indirectly increase the receptive area for feature extraction. The YOLOv8 model has a slight improvement in accuracy compared to the YOLOv5 model, possibly due to its use of a decoupled head, which reduces the impact of location information on classification tasks.

Enhanced Artificial Intelligence Strategies in Renal Oncology: Iterative Optimization and Comparative Analysis of GPT 3.5 Versus 4.0.

The rise of artificial intelligence (AI) in medicine has revealed the potential of ChatGPT as a pivotal tool in medical diagnosis and treatment. This study assesses the efficacy of ChatGPT versions 3.5 and 4.0 in addressing renal cell carcinoma (RCC) clinical inquiries. Notably, fine-tuning and iterative optimization of the model corrected ChatGPT's limitations in this area. In our study, 80 RCC-related clinical questions from urology experts were posed three times to both ChatGPT 3.5 and ChatGPT 4.0, seeking binary (yes/no) responses. We then statistically analyzed the answers. Finally, we fine-tuned the GPT-3.5 Turbo model using these questions, and assessed its training outcomes. We found that the average accuracy rates of answers provided by ChatGPT versions 3.5 and 4.0 were 67.08% and 77.50%, respectively. ChatGPT 4.0 outperformed ChatGPT 3.5, with a higher accuracy rate in responses (p<0.05). By counting the number of correct responses to the 80 questions, we then found that although ChatGPT 4.0 performed better (p<0.05), both versions were subject to instability in answering. Finally, by fine-tuning the GPT-3.5 Turbo model, we found that the correct rate of responses to these questions could be stabilized at 93.75%. Iterative optimization of the model can result in 100% response accuracy. We compared ChatGPT versions 3.5 and 4.0 in addressing clinical RCC questions, identifying their limitations. By applying the GPT-3.5Turbo fine-tuned model iterative training method, we enhanced AI strategies in renal oncology. This approach is set to enhance ChatGPT's database and clinical guidance capabilities, optimizing AI in this field.

Navigating Cyber Diplomacy in the Governance of Emerging AI Technologies: Lessons from Transatlantic Cooperation

The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.