Computer Artificial Intelligence Research Articles

PlanteSaine: An Artificial Intelligent Empowered Mobile Application for Pests and Disease Management for Maize, Tomato, and Onion Farmers in Burkina Faso

This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine’s AI system analyzes these images to provide accurate diagnoses. The application’s offline functionality ensures accessibility even in remote areas with limited Internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture

Reversible Crystalline‐Crystalline Transitions in Chalcogenide Phase‐Change Materials

AbstractPhase‐change random access memory (PCRAM) is one of the most technologically mature candidates for next‐generation non‐volatile memory and is currently at the forefront of artificial intelligence and neuromorphic computing. Traditional PCRAM exploits the typical phase transition and electrical/optical contrast between non‐crystalline and crystalline states of chalcogenide phase‐change materials (PCMs). Currently, traditional PCRAM faces challenges that vastly hinder further memory optimization, for example, the high‐power consumption, significant resistance drift, and the contradictory nature between crystallization speed and thermal stability, nearly all of them are related to the non‐crystalline state of PCMs. In this respect, a reversible crystalline‐to‐crystalline phase transition can solve the above problems. This review delves into the atomic structures and switching mechanisms of the emerging atypical crystalline‐to‐crystalline transitions, and the understanding of the thermodynamic and kinetic features. Ultimately, an outlook is provided on the future opportunities that atypical all‐crystalline phase transitions offer for the development of a novel PCRAM, along with the key challenges that remain to be addressed.

Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities.

In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massive amount of data. Meanwhile, scientific research such as quantum computing and protein synthesis increasingly demand higher computing capacities. As the Moore's Law approaches its terminus, there is an urgent need for alternative computing paradigms that satisfy this growing computing demand and break through the barrier of the von Neumann model. Neuromorphic computing, inspired by the mechanism and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. This review studies the expansion of optoelectronic devices on photonic integration platforms that has led to significant growth in photonic computing, where photonic integrated circuits (PICs) have enabled ultrafast artificial neural networks (ANN) with sub-nanosecond latencies, low heat dissipation, and high parallelism. In particular, various technologies and devices employed in neuromorphic photonic AI accelerators, spanning from traditional optics to PCSEL lasers are examined. Lastly, it is recognized that existing neuromorphic technologies encounter obstacles in meeting the peta-level computing speed and energy efficiency threshold, and potential approaches in new devices, fabrication, materials, and integration to drive innovation are also explored. As the current challenges and barriers in cost, scalability, footprint, and computing capacity are resolved one-by-one, photonic neuromorphic systems are bound to co-exist with, if not replace, conventional electronic computers and transform the landscape of AI and scientific computing in the foreseeablefuture.

Facial Frontiers: Unveiling the Potential of LBPHs and Haar Cascades in Facial Recognition for Enhanced School Security

The article aims at introducing a Facial Recognition School Security System as one solution to improve security while speeding up administrative processes in educational institutions. The need for such a system emanates from concerns over the safety of schools as well as inadequacies inherent in conventional attendance and access control methods. Inadequate methods can be manual or rely on an older technology that leads to inefficiencies, inaccuracies and breaches of security. This proposed solution exploits contemporary artificial intelligence algorithms and computer vision techniques to facilitate the reliable identification and validation of entrants thereby providing contactless approach during Pandemic times in compliance with the COVID-19 safety protocols. At this period of COVID-19 it also acts as an avenue where physical touchpoints are reduced with consideration to social distancing measures. Our method is novel in the fact that it only detects and recognizes human faces as opposed to general object detection systems. We use Local Binary Pattern Histograms (LBPH) for face recognition and Haar Cascades for face detection. The Haar Cascade algorithm employs simple rectangular features to detect faces, using a cascade of weak classifiers to achieve high detection rates. The LBPH algorithm captures local texture patterns of facial features, calculating LBP values for each pixel. Our project demonstrates variable performance across different classes, with precision ranging from 0.50 to 1.00, recall from 0.33 to 1.00, and F1 scores from 0.33 to 0.94, while achieving an overall accuracy of 0.75, indicating robust performance in certain scenarios but room for improvement in others.

The Human Brain Versus Computer: Which is Smarter?

Our thoughts, movements, memories, and decisions are all controlled by the brain, which is the hub of the human neurological system. The complexity of the human brain has increased with evolution, and many of its fascinating characteristics are still poorly understood by researchers. When it comes to mathematical computations, quantitative analysis, and game show questions, computers may be able to consistently outperform human brains, but that does not imply that computers are generally smarter. Humans are more adept at drawing conclusions about a new situation by remembering prior experiences. Qualitative analysis and emotional intelligence are skills that humans possess. The article examines a few topics pertaining to the human brain's capacity for information. The amount of information that can be stored in the brain and its capacity for retaining and reproducing text and auditory information are evaluated and estimated based on the fact that the unit of information measurement is a bit. The potentials of the computer have been examined in order to simulate the recognition of pictures that occurs in the human brain. A theory is put out that real-time modeling of these processes is challenging to implement given the capabilities of today's computers. A Comparative Study of Human and Computer Intelligence Intellect in Humans and Computers: A Comparative Analysis Human vs. Computer Intelligence: An Evaluation of Cognitive Abilities An Investigation into the Relative Intelligence of Humans and Computers Human Intelligence and Computer Intelligence: A Comparative Analysis of Human and Machine Intelligence Comparison between Humans and Computers: An Empirical Study An Examination of the Capabilities of Human and Artificial Intelligence Human and Computer Intelligence: A Comparative Study of Cognitive Functions The Great Debate: Human Intelligence vs. Artificial Intelligence

SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG.

Innovative Applications of AIGC in Television Content Generation

Artificial Intelligence and Generative Computing (AIGC) technologies have revolutionized television content generation by enabling automated processes that were previously labor-intensive and time-consuming. This paper explores the innovative applications of AIGC in the broadcast television sector, focusing on three key areas: automated script generation, creation of virtual hosts and actors, and automated video editing. Through case studies and empirical analysis, we examine the effectiveness and impact of these technologies on content quality, production efficiency, and audience engagement. Despite significant advancements, challenges such as ethical considerations and technological limitations persist. Looking forward, the integration of AIGC promises continued advancements in transforming the landscape of television content creation.

Enhancing the expressivity of quantum neural networks with residual connections

In noisy intermediate-scale quantum era, the research on the combination of artificial intelligence and quantum computing has been greatly developed. Here we propose a quantum circuit-based algorithm to implement quantum residual neural networks, where the residual connection channels are constructed by introducing auxiliary qubits to data-encoding and trainable blocks in quantum neural networks. We prove that when this particular network architecture is applied to a l-layer data-encoding, the number of frequency generation forms extends from one, namely the difference of the sum of generator eigenvalues, to O(l2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}({l}^{2})$$\\end{document}, and the flexibility in adjusting Fourier coefficients can also be improved. It indicates that residual encoding can achieve better spectral richness and enhance the expressivity of various parameterized quantum circuits. Extensive numerical demonstrations in regression tasks and image classification are offered. Our work lays foundation for the complete quantum implementation of classical residual neural networks and offers a quantum feature map strategy for quantum machine learning.

Detection of Breast Cancer in Digital Mammograms by Using Artificial Intelligence

Abstract Background Daily reading of a large number of normal mammograms ni breast cancer screening programs is a tedious task reducing the attractiveness of the field. The advent of Artificial Intelligence (AI) in medical imaging could, however, provide means to improve the efficiency of mammography screening. Objective Our study aims to emphasize the role of AI system as an aid in detection of breast cancer in digital mammograms. Patients and Methods The main source of data for this study were the mammography studies on the picture archiving and communications system (PACS) of theradiology department, i n Shams university hospitals of the patients referred to the radiology department at Ain Shams university hospitals for screening mammography. Results Our study showed that the improvement in diagnosticperformance and reading times with AI support was observed for the radiologists who were least experienced with mammography while the Al is increased the reading times and had no effect on diagnostic performance on the more experienced radiologists. Conclusion Using the AI as an aid has proven to improve the overall performance of less experienced radiologists allowing them to reach expert like levels of diagnostic performance in the detection of breast cancer at mammography by using an AI computer system for support without the need for additional reading time.

Harnessing AI and NLP Tools for Innovating Brand Name Generation and Evaluation: A Comprehensive Review

The traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce© or SnapChat©. Furthermore, brands such as Kodak©, Xerox©, Google©, Häagen-Dazs©, and Twitter© have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike.

Impact of Digitalization on the Life Insurance Industry

Insurance companies are focusing more on digital transformation as it becomes more crucial in today's market. It presents chances and difficulties, especially for IT departments, which are vital in supporting or impeding advancement. The objective of this paper is to offer a detailed explanation of how digital technologies, including artificial intelligence and cloud computing, are used in the insurance sector. We examined scholarly articles, industry reports, and regulatory publications to collect information. We found the IT needs of insurers and uncovered numerous relationships among various digital technologies. Our results emphasize the importance of insurers implementing a thorough digital strategy to successfully utilize these technologies.

Artificial Intelligence (AI) in Customer Service: Revolutionising Support and Engagement

Customer service, marketing, human resource management, finance, accounting, product and service development, healthcare, commerce, and manufacturing are just a few of the areas where artificial intelligence (AI) has completely changed the game. AI enhances decision-making and work processes by means of machine learning algorithms, automation, and predictive analytics. AI makes it possible to personalise, target adverts accurately, and forecast sales in marketing. Through recruiting, performance reviews, and training of employees, AI in human resource management raises productivity and engagement. Demand forecasting, inventory control, and condition based monitoring are all made easier in factory management by artificial intelligence. Maintaining compliance requirements, AI also helps with risk reduction, financial reporting, and fraud prevention. AI enhances consumer happiness and competitiveness in product creation via use of data analytics, modelling, and suggestions. AI also aids in strategic planning and decision-making in healthcare and life sciences. In retail and e-business, AI improves stocking management, customer profiling, and shopping experiences. This review examines AI in Customer Service: Revolutionising Support and Engagement. We utilised relevant published data (2004–2014) from diverse, reliable databases. Findings suggest that other trends like creative AI, XAI, and quantum computing, as well as collaboration between human beings and AI, continue to advance. As a result, ethical concerns remain a critical element to address when it comes to the application of AI, identifying both threats and opportunities. Finally, we note that AI continues to be a formidable and revolutionary force in organisations, enhancing value creation while promoting ethical principles. Keywords: Artificial Intelligence, Customer Service, Chatbots, Virtual Assistants, Predictive Analytics, Manufacturing, Creative AI

The Most Important Types of Cyber Attacks that France is Expected to Face in the Future and the Cyber Security Measures it Must Implement to Protect Critical Infrastructure, Telecommunication Networks and Personal Data

As the digital landscape continues to evolve, nations like France find themselves on the frontline of an escalating cyber warfare scenario. This abstract provides an insightful overview of the most significant types of cyber attacks that France is expected to face in the future, offering a comprehensive analysis based on current trends and emerging technologies. The research delves into the realm of advanced persistent threats (APTs), examining the sophisticated techniques employed by state-sponsored actors and cybercriminal organizations to compromise critical infrastructure, sensitive data, and national security. It explores the evolving landscape of ransomware attacks, focusing on the potential impact on both public and private sectors, and the looming threat of extortion campaigns that could cripple essential services. Furthermore, the abstract sheds light on the rising concern of supply chain attacks, as interconnected global networks make businesses and government entities vulnerable to malicious actors seeking to exploit weaknesses in third-party systems. The research also explores the nuances of social engineering attacks, recognizing the human element as a critical vulnerability that threat actors may exploit through targeted phishing campaigns and manipulation tactics. In addition, the abstract touches upon the growing menace of Internet of Things (IoT) vulnerabilities, acknowledging the increased interconnectivity of devices and the potential for large-scale disruptions. It also considers the implications of emerging technologies such as artificial intelligence and quantum computing in shaping the future threat landscape. By examining these diverse aspects of cyber threats, this abstract aims to provide a forward-looking perspective on the challenges France is likely to encounter in the digital realm. As Cyber security becomes an integral component of national defense, understanding these anticipated threats is crucial for policymakers, security experts, and technology professionals seeking to fortify France’s resilience against the evolving nature of cyber attacks.

Film and Television Special Effects AI System Integrating Computer Artificial Intelligence and Big Data Technology

Particle systems can achieve many scenarios that are difficult to achieve in the field or expensive in reality. In this paper, the requirements of 3D film special effects and the design process of particle systems are studied. Unity3D engine was used to simulate 3D movie special effects. Then, the motion trajectory planning of 3D video group animation characters based on particle swarm optimization is proposed. Then, the system models the animated characters’ moving track to achieve the realism’s dynamic effect. This project intends to use the gravity optimization method for particle swarm optimization. The aim is to overcome the optimization difficulty caused by particle swarm optimization, which is easy to fall into local extreme values. Finally, the generated trajectory information is input into the 3D simulation system for conflict detection and clustering tests. Experiments show that the proposed algorithm can effectively render memorable scenes such as movies and TV. The picture has a high real-time frame rate and is realistic.

METAVERSE: ADVANCEMENTS, APPLICATIONS, AND EDUCATION

The Metaverse, a concept first introduced in 1992, has evolved significantly to encompass augmented reality (AR), virtual reality (VR), and extended reality (XR). This paper defines the Metaverse, explores digital twins, and examines the educational applications of the Metaverse. It also highlights how countries and companies have embraced this concept and delve into future advancements in blockchain, artificial intelligence (AI), and cloud computing. The Metaverse's potential across various fields is underscored, particularly its role in education, interactive learning, and immersive experiences. This study aims to provide a comprehensive understanding of the Metaverse and its far-reaching implications.

Discussion on the Construction of SWPU Smart Library Evaluation Indicator System

With the rapid development of information technology, especially the wide application of artificial intelligence, big data and cloud computing and other technologies, the management mode of libraries is also undergoing a profound change. From the traditional management mode based on paper books, it is gradually transformed into a smart library mode characterized by digitization and intelligence. In this context, the construction of SWPU smart library has become an important issue. In order to ensure that the construction of SWPU smart library can keep pace with the times and meet the needs of teachers and students, this paper adopts the Delphi method to construct the evaluation indexes as well as the weights of the indexes, aiming to provide scientific and reasonable guidance for the construction level of SWPU smart library. In this paper, we first determined the evaluation index system of SWPU smart library, including smart technology and facilities, smart resources, smart services and other aspects. Then, we invited experts in the fields of library management and information technology to evaluate these indicators and their weights using the Delphi method. Through several rounds of solicitation and feedback, the experts reached a relatively unanimous opinion on the evaluation indicators and their weights of SWPU's smart libraries. On this basis, we conducted a comprehensive evaluation of the construction level of SWPU Smart Library and put forward corresponding suggestions for improvement. These suggestions will provide strong support for the future development of SWPU Smart Library so that it can better serve the students and faculty and promote the development of university education.

Research on the Application of Quantum Computing in Artificial Intelligence

This paper aims to explore the application of quantum computing in the field of artificial intelligence (AI). The continuous development of AI technology has brought revolutionary changes to various industries, but traditional computers may encounter bottlenecks when facing complex problems. Quantum computing, as an emerging computing paradigm, has the potential to address complex issues. This paper first introduces the fundamentals of AI and quantum computing, then discusses the application cases of quantum computing in AI, including quantum neural networks, quantum optimization algorithms, and quantum reinforcement learning. It also analyzes the current technical challenges and solutions, and prospects the future development direction of quantum computing in the field of AI. Through research in this emerging field, we can better understand and utilize quantum computing technology to promote progress and innovation in the field of AI.

Optimizing renewable energy systems through artificial intelligence: Review and future prospects

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

Philosophy of cognitive science in the age of deep learning.

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the center stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful. This article is categorized under: Philosophy > Artificial Intelligence Computer Science and Robotics > Machine Learning.

Highly-integrable analogue reservoir circuits based on a simple cycle architecture

Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.