Realm Of Intelligence Research Articles

AI-driven canine cataract detection: a machine learning approach using support vector machine

ABSTRACT The pivotal role of the Support Vector Machine becomes evident in addressing the impact of aging and genetics on canine cataract development. Timely detection, crucial for preventing illness in dogs, is facilitated by Artificial Intelligence. This proves especially beneficial for veterinarians grappling with detection challenges. Within the realm of Artificial Intelligence, the subset of Machine Learning emerges as a promising avenue for tackling intricate tasks. The current study introduces a model for grading canine cataracts through images captured on standard mobile phone cameras. Notably, the proposed system centers around harnessing the power of the Support Vector Machine, a potent Machine Learning algorithm tailored for classifying (grading) cataracts based on images. Rigorous evaluation, employing K-fold Cross-validation, validates the images’ reliability. Both binary-class and multi-class experimentation contribute to the dataset. Impressively, the accuracy rates for the trained classification model stand at 83% (without Cross-validation) and 81% (with Cross-validation for K = 10), underscoring the robustness of the approach.

HirMTL: Hierarchical Multi-Task Learning for dense scene understanding

In the realm of artificial intelligence, simultaneous multi-task learning is crucial, particularly for dense scene understanding. To address this, we introduce HirMTL, a novel hierarchical multi-task learning framework designed to enhance dense scene analysis. HirMTL is adept at facilitating interaction at the scale level, ensuring task-adaptive multi-scale feature fusion, and fostering task-level feature interchange. It leverages the inherent correlations between tasks to create a synergistic learning environment. Initially, HirMTL enables concurrent sharing and fine-tuning of features at the single-scale level. This is further extended by the Task-Adaptive Fusion module (TAF), which intelligently blends features across scales, specifically attuned to each task’s unique requirements. Complementing this, the Asymmetric Information Comparison Module (AICM) skillfully differentiates and processes both shared and unique features, significantly refining task-specific performance with enhanced accuracy. Our extensive experiments on various dense prediction tasks validate HirMTL’s exceptional capabilities, showcasing its superiority over existing multi-task learning models and underscoring the benefits of its hierarchical approach.

Does AI need guardrails?

Purpose As the realm of artificial intelligence (AI) expands, its societal implications soar. Today, society actively debates its rollout – yet such discourse often permeates news articles and social media unobjectively and without rigorous peer review. The purpose of this paper is to discuss the most pressing concerns associated with AI development and deployment. Design/methodology/approach This paper proposes a tripartite framework as a strategic guide and guardrail for AI’s evolution. Findings This robust framework addresses challenges, mitigates potential risks and harnesses AI opportunities for our shared future. Originality/value A policy-techno-legal framework for understanding key AI challenges and foundations for building AI guardrails.

Deep Learning-based Emotion Recognition Algorithms in Music Performance

In the realm of artificial intelligence and musicology, emotion recognition in music performance has emerged as a pivotal area of research. This paper introduces EmoTrackNet, an integrated deep learning framework that combines sparse attention networks, enhanced one-dimensional residual Convolutional Neural Networks (CNNs) with an improved Inception module, and Gate Recurrent Units (GRUs). The synergy of these technologies aims to decode complex emotional cues embedded in music. Our methodology revolves around leveraging the sparse attention network to efficiently process temporal sequences, thereby capturing the intricate dynamics of musical pieces. The incorporation of the 1D residual CNN with an upgraded Inception module facilitates the extraction of nuanced features from audio signals, encompassing a broad spectrum of musical tones and textures. The GRU component further refines the model’s capability to retain and process sequential information over longer timeframes, essential for understanding evolving emotional expressions in music. We evaluated EmoTrackNet on the Soundtrack dataset a comprehensive collection of music pieces annotated with emotional labels. The results demonstrated remarkable improvements in the accuracy of emotion recognition, outperforming existing models. This enhanced performance can be attributed to the integrated approach, which efficiently combines the strengths of each component, leading to a more robust and sensitive emotion detection system. EmoTrackNet’s novel architecture and promising results pave the way for new avenues in musicology, particularly in understanding and interpreting the emotional depth of musical performances. This framework not only contributes significantly to the field of music emotion recognition but also has potential applications in music therapy, entertainment, and interactive media where emotional engagement is key.

The Application of Deep Learning to Accurately Identify the Dimensions of Spinal Canal and Intervertebral Foramen as Evaluated by the IoU Index.

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Emerging Technologies and Cyber Ethics: Exploring Future Challenges and Opportunities

In an era marked by the rapid evolution of technology, the fusion of state-of-the-art innovations with cybersecurity has become both a beacon of promise and a source of profound ethical contemplation. This article embarks on a comprehensive exploration of the intricate relationship between emerging technologies and the ethical considerations they evoke, delineating a roadmap for navigating the future challenges and opportunities they present. From the realm of artificial intelligence, where machine learning algorithms hold the promise of revolutionizing cybersecurity defense mechanisms, to the frontier of quantum computing, with its potential to render current encryption standards obsolete, each advancement brings forth a multitude of ethical quandaries. Similarly, blockchain technology, hailed for its potential to enhance data integrity and transparency, also raises concerns regarding privacy and decentralization. The convergence of biotechnology and cybersecurity introduces unprecedented possibilities for biometric authentication and medical data security, yet it also invites scrutiny regarding the ethical use of personal biological data and the potential for biometric surveillance. Moreover, as autonomous systems become increasingly prevalent in cybersecurity operations, questions regarding accountability, transparency, and the potential for autonomous decision-making to align with ethical principles come to the fore.Within this complex landscape, issues such as data privacy, algorithmic biases, digital rights, and the ethical implications of technological proliferation require careful examination. By delving into these ethical dimensions, this article aims to equip stakeholders with the knowledge and insights necessary to navigate the evolving intersection of technology and morality. Ultimately, this exploration serves not only to illuminate the ethical challenges inherent in emerging technologies but also to catalyze a collective effort towards developing ethically conscious approaches to cybersecurity. By fostering a deeper understanding of the ethical implications of technological advancements, we can strive towards a future where innovation and morality coexist harmoniously in the digital age. Keywords: Cybersecurity, Artificial Intelligence, Machine Learning, Quantum Computing, blockchain technology, Biometric authentication

Contextual embedding generation of underwater images using deep learning techniques

<p>This article delves into the cutting-edge realm of artificial intelligence, specifically focusing on its application in marine research via underwater image analysis. It introduces an innovative, integrated approach that combines object detection with image captioning tailored for the aquatic domain. Central to this approach is the advanced technique of image feature extraction, complemented by the strategic implementation of attention mechanisms within neural networks. These mechanisms are key in enhancing the precision and contextual understanding of underwater imagery. The efficacy of this method is underscored by extensive experiments on diverse underwater datasets. Results show notable improvements in detecting and describing complex underwater scenes, thereby providing invaluable insights for marine biologists, environmentalists, and the broader scientific community. This exploration marks a significant advancement in marine research, offering a new lens through which the underwater world can be understood and preserved.</p>

The gendered dress of DALL-E 2: Exploring profession-based images in the Indian context

This study delves into the intricate realm of gender and artificial intelligence (AI) through an examination of DALL-E 2 generated images within the Indian context. The study takes a methodological approach that focuses on assessing images generated in response to prompts such as ‘a farmer cultivating crops in rural Punjab’ or ‘a nurse providing care in a hospital in Delhi’ to reveal the dynamics of gender performativity within profession-based visual content. The generated images were analysed using the dress as a phenomenon to visualise Indian man, Indian woman and ambiguous Indian. The study concludes that DALL-E 2’s algorithm reiterates the binary gender norms leaving no space for ambiguous Indian in its responses. Though the generated image is centred on female professions, it is largely employed by male surroundings. Hindu religious symbols are largely used among male professionals denoting its predominance. In its aesthetics, the individuals portrayed exhibit a brown complexion, echoing the demographic landscape irrespective of gender.

A Bibliometric Analysis Of Emotional Intelligence And Leadership

This research aims to conduct a bibliometric analysis of studies on the relationship between emotional intelligence and leadership. The investigation encompasses a compilation of 1136 articles sourced from Scopus, spanning from 1996 to 2023. The data analysis process involved utilizing the VOSviewer software, which facilitated the creation of maps depicting co-authorship and keywords. A combination of descriptive statistics and social network analysis techniques were employed to analyze the data. Within the 1136 articles, 105 author keywords were referenced at least five times. These keywords were subsequently categorized into 11 clusters. The prevalent terms included “training,” “competence,” “job satisfaction,” “self-awareness,” and “organizational citizenship behavior.” An exploration of the temporal aspect of the data revealed that recent scholarly interest has gravitated toward topics such as “higher education,” “job performance,” “leader development,” “soft skill,” and “employee engagement.” This study’s findings suggest potential avenues for further research in the realm of emotional intelligence and leadership. Subsequent researchers might consider conducting studies in these domains to contribute to the evolving knowledge system.

Utilizing Elephant Herd-Inspired Spiking Neural Networks for Enhanced Ship Detection and Classification in Marine Scene Matching

In the realm of marine intelligence, effective ship classification across vast oceanic expanses is pivotal. Despite strides in conventional identification techniques, existing methods exhibit limitations in terms of effectiveness, robustness, and overall performance. This paper introduces a novel Elephant Herd optimization based Spiking Neural Networks (EHO-SNN) for discerning large ships, small vessels, and the absence of ships. Initial satellite images from the Airbus dataset capture ships at sea, subjected to preprocessing via the wavelet transform-based Retinex algorithm (WRA) to eliminate noise and fog artifacts. Deep learning mobile net facilitates feature extraction, while the Elephant Herd algorithm culls irrelevant features, honing in on the most pertinent ones. Finally, the classification through a spiking neural network, distinguishing between large ships, small vessels, and the absence of ships. Detected large and small ships are accurately positioned within a selected scene, while the absence of a ship terminates the process. The Proposed EHO-SNN model attains an impressive classification accuracy of 99.10%. Notably, it surpasses OMRCNN-SHD, Efficient Net, and AN-YOLOv4 by 1.15%, 12.30%, and 7.79%, respectively, thereby advancing overall accuracy in ship classification.

Navigating the age of AI influence: A systematic literature review of trust, engagement, efficacy and ethical concerns of virtual influencers in social media

This systematic literature review (SLR) delves into the realm of Artificial Intelligence (AI)-powered virtual influencers (VIs) in social media, examining trust factors, engagement strategies, VI efficacy compared to human influencers, ethical considerations, and future trends. Analyzing 60 academic articles from 2012 to 2024, drawn from reputable databases, the study applies specific inclusion and exclusion criteria. Both automated and manual searches ensure a comprehensive review. Findings reveal a surge in VI research post-2012, primarily in journals, with quantitative methods prevailing. Geographically, research focuses on Europe, Asia Pacific, and North America, indicating gaps in representation from other regions. Key themes highlight trust and engagement’s critical role in VI marketing, navigating the balance between consistency and authenticity. Challenges persist regarding artificiality and accountability, managed through brand alignment and transparent communication. VIs offers advantages, including control and cost efficiencies, yet grapple with authenticity issues, addressed through human-like features. Ethically, VI emergence demands stringent guidelines and industry cooperation to safeguard consumer well-being. Looking ahead, VIs promises transformative storytelling, necessitating vigilance in ethical considerations. This study advocates for continued scholarly inquiry and industry reflection to navigate VI marketing evolution responsibly, shaping the future influencer marketing landscape.

Multimodal deep learning using on-chip diffractive optics with in situ training capability

Multimodal deep learning plays a pivotal role in supporting the processing and learning of diverse data types within the realm of artificial intelligence generated content (AIGC). However, most photonic neuromorphic processors for deep learning can only handle a single data modality (either vision or audio) due to the lack of abundant parameter training in optical domain. Here, we propose and demonstrate a trainable diffractive optical neural network (TDONN) chip based on on-chip diffractive optics with massive tunable elements to address these constraints. The TDONN chip includes one input layer, five hidden layers, and one output layer, and only one forward propagation is required to obtain the inference results without frequent optical-electrical conversion. The customized stochastic gradient descent algorithm and the drop-out mechanism are developed for photonic neurons to realize in situ training and fast convergence in the optical domain. The TDONN chip achieves a potential throughput of 217.6 tera-operations per second (TOPS) with high computing density (447.7 TOPS/mm2), high system-level energy efficiency (7.28 TOPS/W), and low optical latency (30.2 ps). The TDONN chip has successfully implemented four-class classification in different modalities (vision, audio, and touch) and achieve 85.7% accuracy on multimodal test sets. Our work opens up a new avenue for multimodal deep learning with integrated photonic processors, providing a potential solution for low-power AI large models using photonic technology.

Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation.

As both the proportion of older people and the length of life increases globally, a rise in age-related degenerative diseases, disability, and prolonged dependency is projected. However, more sophisticated biomedical materials, as well as an improved understanding of human disease, is forecast to revolutionize the diagnosis and treatment of conditions ranging from osteoarthritis to Alzheimer's disease as well as impact disease prevention. Another, albeit quieter, revolution is also taking place within society: human augmentation. In this context, humans seek to improve themselves, metamorphosing through self-discipline or more recently, through use of emerging medical technologies, with the goal of transcending aging and mortality. In this review, and in the pursuit of improved medical care following aging, disease, disability, or injury, we first highlight cutting-edge and emerging materials-based neuroprosthetic technologies designed to restore limb or organ function. We highlight the potential for these technologies to be utilized to augment human performance beyond the range of natural performance. We discuss and explore the growing social movement of human augmentation and the idea that it is possible and desirable to use emerging technologies to push the boundaries of what it means to be a healthy human into the realm of superhuman performance and intelligence. This potential future capability is contrasted with limitations in the right-to-repair legislation, which may create challenges for patients. Now is the time for continued discussion of the ethical strategies for research, implementation, and long-term device sustainability or repair.

К вопросу о правовом регулировании экспериментальных правовых режимов в сфере искусственного интеллекта в ЕАЭС и ЕС

This article covers regulatory and theoretical approaches to the governance of experimental legal regimes concerning artificial intelligence within the Eurasian Economic Union (EAEU) and the European Union (EU). The author observes that the rapid advancement of artificial intelligence technologies poses novel challenges not only of practical concern but also of legal significance. The necessity of establishing a qualitatively new legal framework for testing groundbreaking technologies is emphasized. The author identifies opportunities for their successful implementation through the mechanism of experimental legal regimes. It is concluded that the EAEU should develop an integrated legal framework to facilitate the effective operation of «regulatory sandboxes» in the realm of artificial intelligence, thereby advancing its digital agenda. Furthermore, attention is drawn to the ongoing completion of the regulatory framework for experimental legal regimes concerning artificial intelligence within the EU, suggesting that such legal insights may prove beneficial in shaping the legislation of the EAEU. A comprehensive array of methods including comparative legal analysis, functional examination, and systemic scrutiny of normative legal acts have enabled the examination of legal regulations within the EAEU and the EU, leading to the formulation of proposals for their enhancement.

Conceptual Difficulties in the Transformation of Human Rights to the Realm of Artificial Intelligence

Artificial intelligence has been seeping into various fields of international law for some time, affecting fields such as international humanitarian law – especially regarding the legality of autonomous weapon systems, but also intellectual property law and the legal profession as a whole. A conflicting zone encompassing many subfields is human rights, where an already sensitive subject that is open to debates and interpretation is met with rough questions. For instance, should and could human rights norms be transferred into pre-programmed entities? What relevance can human rights have to a non-human being that has been created, programmed and assembled by humans? Vast regional differences exist between the European, African and Inter-American systems with a lack of coherent structure in the Asia-Pacific region. Our understanding of human rights has also developed substantially over the decades, especially regarding norms on slavery, free speech, the prohibition of discrimination and the rights of women, of disabled persons and indigenous peoples to name a few examples. Furthermore, a vast array of international documents on human rights are political manifestos utilising expressions such as “respecting” and “ensuring” human rights as obligations for members of the international community. Since these provisions deliberately leave a lot of room for interpretation, it seems almost an impossible task to translate them to “binary code”, to a format that is digestible for an artificial entity. The article aims to answer these questions by analysing the abovementioned line of thought and combining it with various attempts at international regulation by states, international organisations as well as non-governmental organisations and think-tanks. The fundamental focus of this paper is to ascertain whether human rights and AI can be made compatible under the current framework of international law at today’s level of development.

Legal Ramifications of Employing AI-Generated Logos as Brand Identities: A Juridical Examination

The utilization of Artificial Intelligence (AI) technology is experiencing rapid expansion in contemporary times. Within the domain of trademark law, a logo serves as a visual identity utilized to distinguish a product or service from its competitors. The utilization of a logo as a brand identity is afforded specific legal protections pursuant to Indonesian Law No. 20/2016 concerning Trademarks. A pivotal consideration pertains to discerning the rightful owner of the rights to the logo created by AI. Is it the proprietor of the AI software employed in crafting the logo, or is it the proprietor of the company or individual who commissioned the logo? The method employed in this research is a normative juridical approach, which scrutinizes the application of legal principles or norms. The approaches employed in this research encompass conceptual and statutory analyses. The objective of this research pertains to understanding the Legal Implications of Utilizing Artificial Intelligence-Generated Logos as Brand Identities, and serving as a reference material for subsequent legal inquiries, particularly those related to the advancement of artificial intelligence. The majority of regulations concerning copyright and ownership of artistic works still hinge upon Copyright Law No. 28/2014. Despite its enactment, the Copyright Law remains bereft of provisions safeguarding works generated by Artificial Intelligence. In the realm of AI or artificial intelligence, there are instances where AI applications inadvertently generate trademark logos bearing visual resemblance to other trademark logos. Such resemblances have the potential to bewilder consumers and undermine the authenticity of a brand.

The AI handbook for financial services leaders : Tips and tactics for mastering AI in banking and finance

This paper explores the multifaceted realm of artificial intelligence (AI) implementation in financial services, providing insights into its potential, challenges and best practices. Highlighting the emergence of generative AI (GenAI) as a transformative tool, the paper underscores its significant impact on productivity and revenue generation within investment banks and capital markets. Addressing inherent risks of AI adoption, the paper stresses the importance of robust governance frameworks to mitigate operational, reputational and compliance risks. Specific attention is given to the phenomenon of GenAI hallucinations and the imperative for deterministic AI models to ensure data integrity and regulatory compliance. The paper outlines four key pillars of AI’s applications in financial services: predictive AI, anomaly detection AI, classification AI and GenAI. Examples highlight AI’s role in risk management, fraud prevention, customer experience enhancement and internal process optimisation, underscoring its transformative potential across the industry. The paper also covers the distinction between public and private AI models, emphasising the advantages of proprietary data-driven insights in ensuring competitive advantage and regulatory compliance. Concluding with actionable insights for AI implementation, the paper advocates for a strategic approach encompassing clear vision setting, risk oversight, data privacy management, centralised data architecture and comprehensive process automation.

Emotional Intelligence in Text-To-Speech Synthesis in Pali Language Using Fuzzy Logic

The field of emotional text-to-speech (TTS) synthesis is making swift progress within the realm of artificial intelligence, holding immense promise to transform our interaction with technology. By using advanced algorithms to analyze and understand the emotional content of text, these systems are able to produce spoken language that accurately conveys the intended emotional tone of the message. Despite the existence of several Text-To-Speech systems across various languages, Pali language is yet to have its own. As a result, we have taken the initiative to create a Text-To-Speech synthesizer exclusively for Pali. Our system offers an end-to-end solution for emotional speech synthesis via Text-To-Speech. We address the problem by incorporating disentangled, well-grained prosody features with global, sentence-level emotion implanting. These well-grained features learn to denote local prosodic differences disentangled from the speaker, tone, and worldwide emotion label. Prosody is usually modeled by rules, so we have implemented the fuzzy logic system to develop a controller for the prosody of Pali speech. The fuzzy controller handles different linguistic parameters in three types of sentences: interrogative, exclamatory, and declarative. The final system produces comprehensible speech that mimics the appropriate intonation for every type of sentence. In this paper, we introduce and outline the application of a fuzzy paradigm to incorporate a Text-To-Speech system for the Pali language while preserving a rule-based Concatenative synthesizer. In the outline of classic Concatenative TTS systems, we recommend a new method in order to increase Concatenative unit selection computation, directed at increasing synthetic speech perceptual superiority. In order to tackle the problem of phonemes that are prone to multiple descriptions in rule-based speech synthesis, the proposed solution involves a fuzzy system. In the introductory section, we offer a concise description of the current context surrounding the challenge of emotional speech synthesis. The second section of this paper outlines the notable advancements made in emotional speech synthesis, acknowledging the contributions of various researchers in this field. The third section delves into the technical details of implementing a fuzzy system The last section of the paper presents the main conclusions and future research scope.

Transferring Black-Box Decision Making to a White-Box Model

In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed to augment these algorithms with explainable AI (XAI) features to enhance transparency. A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models. Where discrepancies were noted, training data were refined to align a white-box model’s decisions closer to its black-box counterpart. Testing this methodology on three distinct medical datasets revealed consistent correlations between the adapted white-box models and their black-box analogs. Notably, integrating this strategy with established methods like local interpretable model-agnostic explanations (LIMEs) and SHapley Additive exPlanations (SHAPs) further enhanced transparency, underscoring the potential value of decision trees as a favored white-box algorithm in medicine due to its inherent explanatory capabilities. The findings highlight a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains.

Unlocking Health Literacy: The Ultimate Guide to Hypertension Education From ChatGPT Versus Google Gemini.

Background Google Gemini (Google, Mountain View, CA) represents the latest advances in the realm of artificial intelligence (AI) and has garnered attention due to its capabilities similar to the increasingly popular ChatGPT (OpenAI, San Francisco, CA). Accurate dissemination of information on common conditions such as hypertension is critical for patient comprehension and management. Despite the ubiquity of AI, comparisons between ChatGPT and Gemini remain unexplored. Methods ChatGPT and Gemini were asked 52 questions derived from the American College of Cardiology's (ACC) frequently asked questions on hypertension, following a specified prompt. Prompts included: no prompting (Form 1), patient-friendly prompting (Form 2), physician-level prompting (Form 3), and prompting for statistics/references (Form 4). Responses were scored as incorrect, partially correct, or correct. Flesch-Kincaid (FK) grade level and word count were recorded. Results Across all forms, scoring frequencies were as follows: 23 (5.5%) incorrect, 162 (38.9%) partially correct, and 231 (55.5%) correct. ChatGPT showed higher rates of partially correct answers than Gemini (p = 0.0346). Physician-level prompts resulted in a higher word count across both platforms (p < 0.001). ChatGPT showed a higher FK grade level (p = 0.033) in physician-friendly prompting. Gemini exhibited a significantly higher mean word count (p < 0.001); however, ChatGPT had a higher FK grade level across all forms (p > 0.001). Conclusion To our knowledge, this study is the first to compare cardiology-related responses from ChatGPT and Gemini, two of the most popular AI chatbots. The grade level for most responses was collegiate level, which was above average for the National Institutes of Health (NIH) recommendations, but on par with most online medical information. Both chatbots responded with a high degree of accuracy, with inaccuracies being rare. Therefore, it is reasonable that cardiologists suggest either chatbot as a source of supplementary education.