Combination Of Artificial Intelligence Research Articles

Animal Detection based Smart Farming in Animal Repellent Using AI and Deep Learning

The combination of artificial intelligence (AI) and deep learning in agriculture has ushered in a new era in agriculture with new solutions designed to solve many problems. This paper presents an animal killing system for smart agriculture that uses artificial intelligence and deep learning to reduce the growing problem of animal damage. As the world population continues to grow, increasing food availability is important; Therefore, it is critical to protect crops from wild animals and pests. Deforestation due to livestock farming has become one of the largest human-wildlife conflicts due to human interference with habitats and deforestation. Wild animals can kill farmers working in the fields, causing major crop losses. Farmers suffered huge crop losses due to wild animals such as elephants, wild boars and deer attacking agriculture. Protecting crops from wild animals is one of the biggest concerns of today's farmers. There are many ways to solve this problem, both lethal (such as shooting and trapping) and non-lethal. (such as railings, pesticides, organic matter, netting or electric fencing). The sensor rotates around the lens and uses DCNN software to detect the target. If an animal is found, it sends a message to the Animal Repellent Module with information on the type of ultrasound that should be produced based on the animal. The development of drones with controlled flight control, as well as lightweight and powerful hyperspectral snapshot cameras that can be used to calculate crop biomass growth and fertilization status, responds to complex agricultural management strategies. KEYWORDS: Animal detection, VGG-Net, Bi-LSTM, convolutional neural network, activity recognition, video surveillance, wild animal monitoring, alert system.

AI-Enabled Governance in Cryptocurrency Communities

This paper explores how the combination of artificial intelligence (AI) can enhance governance in cryptocurrency communities and Decentralized Autonomous Organizations (DAOs). Using insights from blockchain, machine studying, and social computing, we examine moral concerns and dangers While addressing them, we discuss the potential of AI to improve efficiency, transparency and inclusion in phrases of governance shape. Through case studies, we demonstrate sensible packages of AI, consisting of social media sentiment analysis, algorithmic trading, and decentralized forecasting markets. We explore the impact of AI on governance token systems, selection- making processes and community-pushed governance models. Challenges along with algorithmic bias, records privacy, and the need for human oversight are discussed in conjunction with suggested studies suggestions and great practices for implementing responsible AI. This paper explores how the integration of synthetic intelligence (AI) can enhance governance in cryptocurrency communities and decentralized c Decentralized Autonomous Organizations (DAOs). Using insights from blockchain, gadget learning, and social computing, we examine moral worries and dangers While addressing them, we talk the potential of AI to enhance performance, transparency and inclusion in terms of governance structure. Through case research, we display realistic programs of AI, which include social media sentiment evaluation, algorithmic trading, and decentralized forecasting markets. We discover the effect of AI on governance token systems, decision-making methods and network-pushed governance models. Challenges including algorithmic bias, records privateness, and the need for human oversight are mentioned in conjunction with suggested research tips and exceptional practices for the responsible use of AI By clarifying the ability of AI in cryptocurrency governance, we help bridge the space among AI and decentralized selection-making.

Automated Crystallization Monitoring in Material Development using Computer Vision and Neuronal Networks

AbstractCrystallization is critical in producing various substances in the food and pharmaceutical industries. The process begins with nucleation from a supersaturated solution, followed by the directed movement of solute molecules toward the newly formed nuclei. This movement is influenced by different factors such as molecular attraction and thermodynamic properties, which influence the resulting crystal size and shape. This paper presents an automated monitoring approach for the growth process of crystallization from saturated solutions. To monitor such a process, a combination of artificial intelligence and computer vision is used. The approach was developed for application in material research to automate the processing of numerous samples, which would otherwise require laboratory personnel.

Development and validation of a novel colonoscopy withdrawal time indicator based on YOLOv5.

The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT). Colonoscopy video data from 472 patients across two hospitals were retrospectively analyzed. WTS was computed through a combination of artificial intelligence (AI) and manual verification. The results obtained through WTS were compared with those generated by the AI system. Patients were categorized into four groups based on the presence of polyps and whether resections or biopsies were performed. Bland Altman plots were utilized to compare AI-computed WTS with manually verified WTS. Scatterplots were used to illustrate WTS within the four groups, among different hospitals, and across various physicians. A parallel box plot was employed to depict the proportions of WTS relative to WT within each of the four groups. The study included 472 patients, with a median age of 55years, and 57.8% were male. A significant correlation with manually verified WTS (r=0.918) was observed in AI-computed WTS. Significant differences in WTS/WT among the four groups were revealed by the parallel box plot (P<0.001). The group with no detected polyps had the highest WTS/WT, with a median of 0.69 (interquartile range: 0.40, 0.97). WTS patterns were found to be varied between the two hospitals and among senior and junior physicians. A promising alternative to traditional WT for quality control and training assessment in colonoscopy is offered by AI-assisted computation of WTS.

The Clinical Management of Leukocytospermia in Male Infertility: A Narrative Review

A major global health concern, male infertility affects 8–12% of couples globally. Leukocytospermia is a complicated illness that is distinguished from other reasons causing male infertility by having high white blood cell counts in semen. The complex mechanisms behind leukocytospermia’s effects on sperm function and fertility are examined in this review. Leukocytospermia induces oxidative stress and reactive oxygen species (ROS) that impair DNA integrity, mitochondrial function, cytoplasmic extrusion, and sperm quality overall. Leukocytospermia is exacerbated by non-infectious factors, such as substance abuse and varicocele, even though genital tract infections are a common cause. The usefulness and dependability of diagnostic techniques range from immunochemistry to direct counting. Although there is still disagreement on the most effective course of action, clinical-care techniques, such as antioxidant supplementation and antibiotic therapy, attempt to address underlying causes and reduce ROS-induced damage. Prospectively, the combination of artificial intelligence with the latest developments in artificial reproductive technologies presents opportunities for more precise diagnosis and customized treatments.

TransGEM: a molecule generation model based on Transformer with gene expression data.

It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. The code is available at https://github.com/hzauzqy/TransGEM.

Research Contents, Methods and Prospects of Emotional Architecture Based on a Systematic Literature Review

In recent years, research on building perception has increasingly become a hot topic. More and more scholars have explored the user perceptions of the built environment and guided the design through the perception results. The technical method of emotional engineering can quantify people’s emotions and facilitate the exploration of users’ perceptions in the built environment. However, most of the existing research is empirical, and there is no article to review the interdisciplinary direction of architecture and emotional engineering. This review uses the PRISMA method to conduct a systematic literature review of 147 studies on building environment assessment using emotional engineering methods, and discusses the relationship between the building environment and humans, especially in terms of emotions, cognition, behavior, and physiology. Through a systematic literature review, the theoretical basis of emotional architecture is put forward. It constructs the interactive mode and theoretical framework of emotional architecture and reveals that the combination of artificial intelligence, big data and machine learning may be the new direction of emotional architecture research in the future.

Hybrid Gamification and AI Tutoring Framework using Machine Learning and Adaptive Neuro-Fuzzy Inference System

Although technology has significantly improved the teaching and learning process, it has not been able to increase students' self-motivation and engagement at the same level. The lack of self-motivation and intermittent engagement is currently one of the primary challenges faced by educators. This new approach to learning called the hybrid gamification framework uses a combination of artificial intelligence (AI), machine learning (ML), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a more engaging and personalized learning experience. By tracking students' interactions and performance, the system can allocate rewards based on their progress, which helps to increase their motivation and engagement. This technology makes it possible for educators to collect and analyse data related to students' engagement patterns, quiz scores, time spent on learning activities, participation in discussion forums, and much more. This data analysis enables educators to identify struggling students and high achievers, allowing them to provide tailored support and instruction to maximize student success. A pilot implementation of this system involving 200 computer science students successfully demonstrated the effectiveness of this technology. This research provides a comprehensive understanding of gamification's impact by combining quantitative data with qualitative insights.

Parking Lot Occupancy Detection using Deep Learning and Fisheye Camera for AIoT System

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones.

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as "You Only Look Once" (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model's accuracy and the reliability of indoor human detection in real-time drone applications.

Blockchain: The Economic and Financial Institution for Autonomous AI?

This paper examines how the combination of artificial intelligence (AI) and blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints on AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI’s access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that blockchain provides a solution for creating digital economic and financial institutions, permitting AI to engage with these institutions through the management of private keys. This extends AI’s capabilities to form and execute contracts, participate in marketplaces, and utilize financial services autonomously. The paper encourages further research on AI as a general-purpose technology and blockchain as an institutional technology that can unlock the full capabilities of autonomous AI agents.

RL-Chord: CLSTM-Based Melody Harmonization Using Deep Reinforcement Learning.

Automatic music generation is the combination of artificial intelligence and art, in which melody harmonization is a significant and challenging task. However, previous recurrent neural network (RNN)-based work fails to maintain long-term dependency and neglects the guidance of music theory. In this article, we first devise a universal chord representation with a fixed small dimension, which can cover most existing chords and is easy to expand. Then a novel melody harmonization system based on reinforcement learning (RL), RL-Chord, is proposed to generate high-quality chord progressions. Specifically, a melody conditional LSTM (CLSTM) model is put forward that learns the transition and duration of chords well, based on which RL algorithms with three well-designed reward modules are combined to construct RL-Chord. We compare three widely used RL algorithms (i.e., policy gradient, Q -learning, and actor-critic algorithms) on the melody harmonization task for the first time and prove the superiority of deep Q -network (DQN). Furthermore, a style classifier is devised to fine-tune the pretrained DQN-Chord for zero-shot Chinese folk (CF) melody harmonization. Experimental results demonstrate that the proposed model can generate harmonious and fluent chord progressions for diverse melodies. Quantitatively, DQN-Chord achieves better performance than the compared methods on multiple evaluation metrics, such as chord histogram similarity (CHS), chord tonal distance (CTD), and melody-chord tonal distance (MCTD).

Analysis of data science and AI-enabled 6G wireless communication networks

Current networks, such as 4G and the forthcoming 5G networks, may not be capable to fully congregate quickly emerging traffic strains due to the proliferation of smart fatal, infrastructures and explosion of diverse applications with varying necessities. As a result, 6G network research has already seen participation from both the private sector and the academic community. Recently, a innovative paradigm has emerged for the intelligent design and optimization of 6G networks, based on the combination of artificial intelligence (AI) and data science (DS). Therefore, this article proposes an AI-enabled architecture for 6G-networks, which is alienated into four layers: an intelligent-sensing, a data-analytics, intelligent-control and smart-application, with the goal of realizing patterns sighting, smart-resource management, automatic network adjustment, and intelligent-service provisioning. We then go over the uses of DS&amp;AI methods in 6G networks, such as AI-enhanced mobile edge-computing, intelligent-mobility, and smart-spectrum management, and go into detail about how to implement these methods to maximize the network's performance. We also emphasize key areas for future study and possible clarifications for AI-enabled 6G networks, together with as computational efficiency, algorithm resilience, hardware-development, and energy-management.

The implementation of an AI-driven advertising push system based on a NLP algorithm

The advertising industry is developing very rapidly, especially outdoor advertising, which has attracted people's attention. All kinds of commercial advertisements can be seen everywhere in outdoor public places, but the advertising delivery system on the market is relatively simple in function, and the evaluation of advertising effect lacks effective automatic analysis means, mainly carried out by manual observation, which is low in efficiency and difficult to conduct quantitative evaluation, which directly leads to the lack of targeted advertising. Artificial intelligence advertising refers to the use of artificial intelligence technology (such as voice recognition, face recognition, deep learning, machine learning, etc.), investigation, production, publishing advertising and other fields, the combination of artificial intelligence and advertising brings great value and convenience to people's lives. Among them, the most common algorithm that can achieve accurate and intelligent advertising is NLP, and the artificial intelligence advertising push system based on natural language processing (NLP) algorithm can provide a variety of useful applications in the advertising field. These applications can help advertisers better understand user needs, improve the accuracy and effectiveness of ads, and provide a better user experience. In order to fully tap the advertising information value contained in unstructured data, this paper introduces the text mining technology based on natural language processing, explores from principle to practice, and analyzes the push process and application of intelligent advertisements in daily life by analyzing the implementation steps of NLP algorithm model.

AI-POWERED LOGISTICS AND DIGITAL MARKETING FOR BUSINESS OPTIMISATION

The purpose of the work is to determine the characteristics of the use of artificial intelligence in logistics systems and digital marketing strategies of companies. The aim of the study is to determine the impact on companies of the simultaneous use of logistics and digital marketing based on artificial intelligence in the promotion and sale of products on domestic and foreign markets. The methodology used in this study includes a statistical analysis of the dynamics of the logistics market and the artificial intelligence market, as well as specialised software for digital marketing. A comprehensive study of scientific works has made it possible to identify the main trends in the development of artificial intelligence, logistics and digital marketing. The processes of globalisation and digitalisation are encouraging companies to actively adopt innovative technologies in order to secure a highly competitive position in the long term, which is stimulating relevant scientific research. The creation of personalised recommendations based on machine learning algorithms and the reduction of the time taken to deliver products to users, with the involvement of autonomous robotic couriers, make it possible to increase the popularity of the company on the market. AI engagement targets Generation Y, Z and Alpha users who are active consumers of products and interested in innovation in the promotion and delivery of goods and services. Stimulating demand on the internet using digital marketing tools based on artificial intelligence involves identifying highly specialised groups of users, interacting with them based on specific communication patterns and using relevant content. Thanks to AI, modern content can be generated and promptly placed on specialised social media, stimulating user interest in relevant brands. For modern companies, the combination of artificial intelligence, logistics and digital marketing is an important area of activity that relates to long-term strategic tasks and enables the achievement of optimal economic results.

Potential Benefits of Information Technology Trends in Nigerian Tertiary Institutions

In recent years, research on the possible applications of IT trends in tertiary institutions has grown significantly. This review focuses on the impact of numerous IT trends on tertiary education. The main objective of this review is to give a general overview of 45 related published articles sourced from five research databases between 2015 and 2023. The review discusses a variety of IT trends, such as artificial intelligence (AI), cloud computing (CC), the internet of things (IoT), data analytics and big data, cybersecurity, and virtual and augmented reality (VR and AR), and how it improve communication and collaboration. It also discusses how big data analytics is used to make better decisions, integrate AI into tertiary institutions, the potential of mobile learning apps, and how virtual reality is used in science education, cloud computing implications, and gamification applications. The review also shows how IT trends are becoming increasingly significant in changing how students learn at postsecondary institutions. The results demonstrate how learning management systems increase student engagement, while social media platforms have the ability to promote communication and collaboration. Additionally, the combination of artificial intelligence and big data analytics has exciting prospects for data-driven decision-making and individualised learning. Innovative methods to improve student learning and engagement are made possible by the use of virtual reality technology and mobile learning applications. The use of blockchain technology at tertiary institutions demonstrates its potential in fields like secure record-keeping and credentialing. The use of gamification also opens up new opportunities for boosting student engagement and motivation. at the final section, the effects of cloud computing at tertiary institutions are critically analysed, with an emphasis on the advantages and difficulties of cloud-based solutions.

FROM IMAGE TO INSIGHT: A REVIEW OF DEEP LEARNING APPROACHES FOR CYSTIC FIBROSIS DETECTION IN COMPUTED TOMOGRAPHY

Cystic fibrosis (CF) is a genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene. This disorder causes a wide range of clinical complications, primarily affecting the respiratory and digestive systems and extending its impact to other physiological areas. Early detection and careful monitoring are paramount to mitigate disease progression and improve the quality of life of individuals with CF. Computed tomography (CT), particularly high-resolution CT (HRCT), has become a key diagnostic method for detecting pulmonary manifestations of CF. However, manual analysis of CT images requires a high level of expertise and is time consuming. The combination of artificial intelligence (AI) and deep learning with CT imaging predicts significant advances in CF detection. Deep learning, a subset of AI, uses neural networks to analyse complex morphological patterns indicative of disease from large datasets. This review traces the journey from the earliest attempts to use artificial intelligence in CF detection to recent advances made using deep learning algorithms. By exploring various deep learning architectures and their integration into clinical practice, this review illuminates the potential of these new technologies to revolutionize CF detection using CT imaging. Automated and accurate analysis enabled by deep learning aims to reduce the diagnostic burden on radiologists, speed up the diagnostic process and pave the way for timely and personalized therapeutic interventions, which is in line with the ultimate goal of improving patient care.

A study on the application of artificial intelligence in the design of intelligent medical robots

Abstract With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development has been promoted, which is an important product of the development of artificial intelligence. In this paper, the general structure of the intelligent medical robot is designed by combining artificial intelligence technology and robotics-related technology. Then, the binocular vision function of the robot was realized by visually acquiring the image of the target object, 3D reconstruction of the target object, and combining the SIFT image recognition algorithm and target tracking algorithm. Then, a new speech recognition algorithm was constructed to realize the human-robot interaction function with the medical robot based on the deep learning Transforme network after the construction of the human acoustic model. Finally, the designed intelligent medical robot was tested, and its overall performance was evaluated. The results show that the recognition errors of the intelligent medical robot on the features of the items are all within 0.05, the recognition errors on the features of the human body are within 0.2, and the speed of the target tracking is between 6km/h and 16km/h. The average recognition accuracy of the medical robot for voice commands is about 0.9, the recognition time is about 0.7s, the normal working rate of each function is more than 0.99, and the test speed is within 2s.

Architecture for Smart Buildings Based on Fuzzy Logic and the OpenFog Standard

The combination of Artificial Intelligence and IoT technologies, the so-called AIoT, is expected to contribute to the sustainability of public and private buildings, particularly in terms of energy management, indoor comfort, as well as in safety and security for the occupants. However, IoT systems deployed on modern buildings may generate big amounts of data that cannot be efficiently analyzed and stored in the Cloud. Fog computing has proven to be a suitable paradigm for distributing computing, storage control, and networking functions closer to the edge of the network along the Cloud-to-Things continuum, improving the efficiency of the IoT applications. Unfortunately, it can be complex to integrate all components to create interoperable AIoT applications. For this reason, it is necessary to introduce interoperable architectures, based on standard and universal frameworks, to distribute consistently the resources and the services of AIoT applications for smart buildings. Thus, the rationale for this study stems from the pressing need to introduce complex computing algorithms aimed at improving indoor comfort, safety, and environmental conditions while optimizing energy consumption in public and private buildings. This article proposes an open multi-layer architecture aimed at smart buildings based on a standard framework, the OpenFog Reference Architecture (IEEE 1934–2018 standard). The proposed architecture was validated experimentally at the Faculty of Engineering of Vitoria-Gasteiz to improve indoor environmental quality using Fuzzy logic. Experimental results proved the viability and scalability of the proposed architecture.

„Der ,Digitale Zwilling‘ kann die MS-Versorgung wirklich verbessern“

Prof. Dr Tjalf Ziemssen sees himself as a digital pioneer with a lot of passion. Alongside and parallel to his day-to-day work at the Centre for Clinical Neurosciences at the Neurological University Hospital Dresden, treating patients with multiple sclerosis, he and his team are developing the concept of the ‚digital MS twin‘. This can be thought of as a virtual copy, a digital dashboard or, in modern parlance, an avatar of a patient that is able to quantitatively describe the individual disease. The beauty of this is that a combination of artificial intelligence, machine learning and data analysis enables digital twins to uncover problems before they occur and optimise processes.