Field Of Intelligence Research Articles

A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection

Protecting data from management is a significant task at present. Digital images are the most general data representation. Images might be employed in many areas like social media, the military, evidence in courts, intelligence fields, security purposes, and newspapers. Digital image fakes mean adding infrequent patterns to the unique images, which causes a heterogeneous method in image properties. Copy move forgery is the firmest kind of image forgeries to be perceived. It occurs by duplicating the image part and then inserting it again in the image itself but in any other place. If original content is not accessible, then the forgery recognition technique is employed in image security. In contrast, methods that depend on deep learning (DL) have exposed good performance and suggested outcomes. Still, they provide general issues with a higher dependency on training data for a suitable range of hyperparameters. This manuscript presents an Enhancing Copy-Move Video Forgery Detection through Fusion-Based Transfer Learning Models with the Tasmanian Devil Optimizer (ECMVFD-FTLTDO) model. The objective of the ECMVFD-FTLTDO model is to perceive and classify copy-move forgery in video content. At first, the videos are transformed into distinct frames, and noise is removed using a modified wiener filter (MWF). Next, the ECMVFD-FTLTDO technique employs a fusion-based transfer learning (TL) process comprising three models: ResNet50, MobileNetV3, and EfficientNetB7 to capture diverse spatial features across various scales, thereby enhancing the capability of the model to distinguish authentic content from tampered regions. The ECMVFD-FTLTDO approach utilizes an Elman recurrent neural network (ERNN) classifier for the detection process. The Tasmanian devil optimizer (TDO) method is implemented to optimize the parameters of the ERNN classifier, ensuring superior convergence and performance. A wide range of simulation analyses is performed under GRIP and VTD datasets. The performance validation of the ECMVFD-FTLTDO technique portrayed a superior accuracy value of 95.26% and 92.67% compared to existing approaches under GRIP and VTD datasets.

AIoT: The Integrating of Artificial Intelligence and the Internet of Things

At present, artificial intelligence has emerged as a significant field all over the world, offering diverse enhancements across many industries when combined with it. This study primarily examines the combination of the Internet of Things and artificial intelligence, highlighting practical applications and areas requiring enhancement. Mainly from a literature review utilising the three keywords: artificial intelligence, the Internet of Things, and application for search and screening, it was discovered that there are several issues in the application of this field. There are issues with delayed data transmission, compatibility, and anomaly detection in the field of autonomous driving. In the field of wearable devices, there are data security issues. In the field of industrial Internet of Things, there are problems such as low transmission efficiency, high latency, and inability to effectively resist external network attacks. In addition, there are also trust issues in the field of artificial intelligence where decisions and predictions cannot be understood by people. In besides this, this study provides several of recommendations for improvements in further research.

Reusability of Bayesian Networks case studies: a survey

Bayesian Networks (BNs) are probabilistic graphical models used to represent variables and their conditional dependencies, making them highly valuable in a wide range of fields, such as radiology, agriculture, neuroscience, construction management, medicine, and engineering systems, among many others. Despite their widespread application, the reusability of BNs presented in papers that describe their application to real-world tasks has not been thoroughly examined. In this paper, we perform a structured survey on the reusability of BNs using the PRISMA methodology, analyzing 147 papers from various domains. Our results indicate that only 18% of the papers provide sufficient information to enable the reusability of the described BNs. This creates significant challenges for other researchers attempting to reuse these models, especially since many BNs are developed using expert knowledge elicitation. Additionally, direct requests to authors for reusable BNs yielded positive results in only 12% of cases. These findings underscore the importance of improving reusability and reproducibility practices within the BN research community, a need that is equally relevant across the broader field of Artificial Intelligence.

TGNF-Net: Two-Stage Geometric Neighborhood Fusion Network for Category-Level 6D Pose Estimation

The main goal of six-dimensional pose estimation is to accurately ascertain the location and orientation of an object in three-dimensional space, which has a wide range of applications in the field of artificial intelligence. Due to the relative sparseness of the point cloud data captured by the depth camera, the ability of models to fully understand the shape, structure, and other features of the object is hindered. Consequently, the model exhibits weak generalization when faced with objects with significant shape differences in the new scene. The deep integration of feature levels and the mining of local and global information can effectively alleviate the influence of the above factors. To solve these problems, we propose a new Two-Stage Geometric Neighborhood Fusion Network for category-level 6D pose estimation (TGNF-Net) to estimate objects that have not appeared in the training phase, which strengthens the fusion capacity of feature points within a specific range of neighborhoods, enabling the feature points to be more sensitive to both local and global geometric information. Our approach includes a neighborhood information fusion module, which can effectively utilize neighborhood information to enrich the feature set of different modal data and overcome the problem of heterogeneity between image and point cloud data. In addition to this, we design a two-stage geometric information embedding module, which can effectively fuse geometric information of the multi-scale range into keypoint features. This way enhances the robustness of the model and enables the model to exhibit stronger generalization capabilities when faced with unknown or complex scenes. These two strategies enhance the expression of features and make NOCS coordinate predictions more accurate. Many experiments show that our approach is superior to other classical methods on the CAMERA25, REAL275, HouseCat6D, and Omni6DPose datasets.

The optimization of consensus decision-making for a multi-microwave source system based on composite leader-follower clustering for intelligent agent-based joint heating temperature field

The optimization of consensus decision-making for a multi-microwave source system based on composite leader-follower clustering for intelligent agent-based joint heating temperature field

Philosophical Analysis of Consciousness as an Intersection Point of Philosophy, Culture and Artificial Intelligence

Consciousness, as a fundamental aspect of human experience, has been a subject of profound inquiry across philosophy, culture, and the rapidly evolving field of artificial intelligence (AI). This paper explores the multifaceted nature of consciousness as a nexus where these domains intersect. By examining philosophical theories of consciousness, cultural interpretations of self-awareness, and the implications of AI advancements, the study addresses the challenges of defining consciousness, its diverse cultural interpretations, and the ethical and technical questions surrounding its replication or simulation in machines. The paper argues that consciousness is not only a philosophical puzzle but also a cultural construct and a technological frontier, with significant implications for our understanding of humanity and the future of intelligent systems. Through an interdisciplinary lens, this analysis highlights the need for continued dialogue between philosophy, culture, and AI research to navigate the complexities of consciousness in an increasingly technologically driven world.

On Translation of Artificial Intelligence Terms: A Study of the Manual “Make Your Own Neural Network” by T. Rashid

The article is devoted to the issue of translation of artificial intelligence terms. The manual “Make Your Own Neural Network” by T. Rashid, which describes in detail the process of creating a neural network, its design and functioning, was chosen as the material for analysis due to the accessibility of the information for a non-specialist in this field. Artificial intelligence is a rapidly developing field, and therefore the terminological f ield of this science is also expanding and diversifying. One of the distinctive features of artificial intelligence as a scientific field is its cross-disciplinary nature as it deals with mathematics, physics, robotics as well as biology and medicine. The terms were divided into two major groups: terms directly related to the field of artificial intelligence and terms borrowed from adjacent fields. The terms were then categorized according to whether they had one, two or more equivalents or no equivalents in order to identify the pattern of equivalence in the target language and the difficulties encountered when the required variant was not available. Often, due to the novelty of AI terms, the translator does not find an equivalent in the target language and therefore resorts to calque, transliteration, transcription or their hybrid. In this article, the author suggests the variants of translation of the terms that have caused the greatest difficulty in translation, which, in our opinion, will better meet the requirements of the term as a lexical unit.

Advances in diffusion models for image data augmentation: a review of methods, models, evaluation metrics and future research directions

Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.

Reflecting on Diversity and Gender Equality in Artificial Intelligence in Africa

Many ethical issues plague the field of AI, and several ethical solutions, mainly from the Global North, have been proposed. Among the issues inherent in ethical AI are bias and lack of diversity. Openair Africa reports, for example, an enormously low participation/visibility of women in today’s digital world. World Economic Report states that worldwide, only about 22% of women are in the field of artificial intelligence compared to 78% of men. In the 2022 Cybersecurity Workforce Report, women account for just 24%. The 2020 Gender Equality Index: Digitalisation and Future of Work also indicates that only one out of two women, 54%, perceive robots and AI positively compared to 67% of men. Thus, this paper discusses diversity and gender equality in AI from the African context. How should we safeguard AI systems from rehashing extant inequality? To what extent can we ensure AI eliminates biasand fosters equality? To this end, this paper proposes a communal approach to the conception, design, development, and deployment of AI systems to address this abysmal situation towards a gender-smart and truly inclusive AI in Africa.

Artificial Intelligence in Education: A Future Vision

The world we live in has been radically changed by technology in recent decades. The field of artificial intelligence (AI) in education (AIEd) has developed into a sizable literature collection with a variety of viewpoints. The use of artificial intelligence in educational applications is growing in popularity, posing both benefits and difficulties for the classroom. This study aims to provide a comprehensive understanding of the current conceptual framework of AIEd and explore the future vision for this technology. The study aims to investigate the opportunities that AI technology offers to enhance teaching and learning, identify challenges in this field and outline the future vision for AI technology, and examine the learning outcomes for teachers and students influenced by AI technology. This method can offer a thorough understanding of the conceptual framework and the long-term goals for this area of technology. For a comprehensive literature evaluation, we chose 35 empirical research publications that included AIEd applications, study themes, and other aspects of the research design, including the general AIEd research field, AIED applications, research topics, and future vision including future benefits, opportunities, threats, and challenges. Although AIEd-based settings are improving student learning, research indicates that tailored learning is still in its early stages. Lack of money and moral dilemmas are obstacles. AIEd's benefits include the fact that AI chatbots and applications facilitate learning, but they also have drawbacks. Additionally, AI apps improved engagement through interactive features, promoted well-being with components and continual availability, and spurred creativity by offering new ideas and problem-solving strategies. While encouraging creativity and increasing participation have many advantages, there are also important obstacles that need to be overcome, such as limits on creativity and moral dilemmas. To maximize the use of AI in education, these elements must be balanced through careful deployment and ongoing assessment.

Report from the Summer School on Software Engineering andArtificial Intelligence

This report summarizes the curriculum and academic outcomes of the Summer School on Software Engineering and Artificial Intelligence (AI) held at the Universidad de Los Andes in Bogot´a, Colombia. The summer school offered an in-depth introduction to the fields of Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP); their role and applications in Software Engineering (SE) and the software development process. The feedback we received from the participants indicates that the program successfully enhanced their knowledge and the skills needed for them to navigate the role of AI in the current landscape of software engineering. The students of the summer school were engaged in the development of a full software system using AI-based tools as part of the development process. We found that the project was successful in providing the students with experience regarding how to incorporate AI-based tools as part of their software development process but not all students showed the same level of proficiency when leveraging AI tools.

Microstructural Evolution and Mechanical Properties of Cu–Ag Alloy via Different Severe Plastic Deformation Processes

The field of artificial intelligence and integrated circuits is experiencing rapid development, particularly in the area of highly integrated and miniaturized components, in which Cu-Ag alloys, as a typical lead frame material, play a crucial role. However, current research is primarily focused on low Ag content alloys, and there are few studies on the regulation of the microstructure and mechanical properties of high Ag content Cu-Ag alloys. This limitation hindered the development and utilization of the high Ag content Cu-Ag alloys. In this study, the microstructure and mechanical properties of Cu-28Ag (wt. %) alloy after room temperature and cryogenic rolling were investigated. It was demonstrated that the cryogenic rolling yielded better surface quality, an enhanced dendrite refinement effect, and a more distinct layer structure compared to room temperature rolling. The conductivity of the alloy decreased after cryogenic rolling due to increased electron scattering within the Cu matrix. The tensile strength improved, but the elongation decreased. Specifically, at a deformation amount of 95%, the alloy exhibited an ultimate tensile strength, yield strength, and elongation of 640 MPa, 631 MPa, and 1.9%, respectively. This strengthening was mainly attributed to the refinement of grains, the presence of dislocations, and precipitation. Furthermore, the samples subjected to liquid nitrogen rolling at a deformation amount of 95% exhibited improved homogeneous deformation capacity, which was attributed to grain size refinement, uniform distribution of high-density dislocations, deformation structure, and heterogeneity-induced deformation enhancement.

Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments

In the intelligent transportation field, the Internet of Things (IoT) is commonly applied using 3D object detection as a crucial part of Vehicle-to-Everything (V2X) cooperative perception. However, challenges arise from discrepancies in sensor configurations between vehicles and infrastructure, leading to variations in the scale and heterogeneity of point clouds. To address the performance differences caused by the generalization problem of 3D object detection models with heterogeneous LiDAR point clouds, we propose the Dual-Channel Generalization Neural Network (DCGNN), which incorporates a novel data-level downsampling and calibration module along with a cross-perspective Squeeze-and-Excitation attention mechanism for improved feature fusion. Experimental results using the DAIR-V2X dataset indicate that DCGNN outperforms detectors trained on single datasets, demonstrating significant improvements over selected baseline models.

Artificial intelligence, collaboration and BIM in the methodological process of architectural design: using the Architechtures tool

The field of artificial intelligence (AI) has been considered as a means capable of promoting significant changes in the dynamics of the architectural design process. This paper presents methodological questions and reflections, as well as cultural and financial challenges related to the feasibility of adopting the Architechtures generative AI tool, which is aimed at collaboratively proposing volumetric architectural parties and floor plans for preliminary studies. The focus is limited to the Brazilian design field, which has traditionally been peripheral and/or outdated compared to foreign technological advancements and applications. Integration with the BIM environment is sought to address local challenges and explore design possibilities. The assessments include the development of high-rise residential building projects, positioning AI as an active partner or co-pilot in the design process, as well as the Avaliação Pré-Projeto methodology for the qualitative dimension. The results highlight both positive aspects and shortcomings, but they suggest a promising future given the possibilities in the field of AI.

Governing artificial intelligence means governing data: (re)setting the agenda for data justice

The field of data justice has been evolving to take into account the role of data in powering the field of artificial intelligence (AI). In this paper we review the main conceptual bases for governing data and AI: the market-based approach, the personal–non-personal data distinction and strategic sovereignty. We then analyse how these are being operationalised into practical models for governance, including public data trusts, data cooperatives, personal data sovereignty, data collaboratives, data commons approaches and indigenous data sovereignty. We interrogate these models' potential for just governance based on four benchmarks which we propose as a reformulation of the Data Justice governance agenda identified by Taylor in her 2017 framework. Re-situating data justice at the intersection of data and AI, these benchmarks focus on preserving and strengthening public infrastructures and public goods; inclusiveness; contestability and accountability; and global responsibility. We demonstrate how they can be used to test whether a governance approach will succeed in redistributing power, engaging with public concerns and creating a plural politics of AI.

Structural and Nanomechanical Properties of Silicon Single Crystals Grown by the Czochralski Method

The rapid advancements in the fields of artificial intelligence, cloud computing, big data analysis and internet of things have expanded the use of electronic devices and increased the demand for semiconductors. The Czochralski method, the most common and effective production technique for these materials, allows the production of single-crystal forms of elements such as Silicon (Si), Germanium (Ge), and various semiconductor compounds. In this study, the crystal structure, surface morphology and mechanical properties of a Si wafer, prepared by slicing a single crystal Si ingot grown by the Czochralski method, were determined by x-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM) and nanoindentation methods, respectively. XRD analysis of the silicon wafer confirmed its single-crystal structure, showing a cubic lattice structure and a single peak on the (100) plane, while SEM and AFM analyses determined that the surface is smooth, even, and undamaged. Hardness and elastic modulus values for the Si wafer, calculated from indentation tests using a Berkovich tip under different loads, were determined to be an average of 19 GPa and 255 GPa, respectively. These results hold vital importance for the advancement of semiconductor technologies. The design and production of electronic devices with superior performance and durability can be achieved with a comprehensive understanding of the mechanical properties of the materials.

Convolutional neural networks in diabetic eye disease detection: A survey on retinopathy and macular edema

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are notable eye disorders affecting the retina’s inner side. This retina helps a person see and distinguish between colors, which is crucial for performing daily activities. It has been observed in long-term diabetic patients, and the count gradually increases. Manually identifying its presence can be time-consuming and may not yield accurate results, as it can lead to various stages that, if delayed, can cause visual Impairment. Emerging technologies and advancements in medical care have enabled automated mechanisms to perform this task. Machine learning (ML) and Deep learning (DL) are two emerging fields of Artificial Intelligence that help identify grading severity through retinal fundus images at early stages and properly treat patients. The paper reviews convolutional neural networks (CNN) with hybrid models of ML and DL algorithms to implement and achieve it, along with the assets, limitations, and gaps of each mechanism, and helps improve further research.

Artificial Intelligence In Health And Health Care: Priorities For Action.

The field of artificial intelligence (AI) has entered a new cycle of intense opportunity, fueled by advances in deep learning, including generative AI. Applications of recent advances affect many aspects of everyday life, yet nowhere is it more important to use this technology safely, effectively, and equitably than in health and health care. Here, as part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2025 initiative, which is designed to provide guidance on pressing health care issues for the incoming presidential administration, we describe the steps needed to achieve these goals. We focus on four strategic areas: ensuring safe, effective, and trustworthy use of AI; promotion and development of an AI-competent health care workforce; investing in AI research to support the science, practice, and delivery of health and health care; and promotion of policies and procedures to clarify AI liability and responsibilities.

Wearable Pressure Sensor Based on Triboelectric Nanogenerator for Information Encoding, Gesture Recognition, and Wireless Real‐Time Robot Control

AbstractWearable sensor has attracted a broad interesting in application prospect of human‐machine interaction (HMI). However, most of sensors are assembled in the shape of gloves to accurately capture complex hand motion information, thereby seriously blocking the hand to complete complex tasks. Herein, a wearable pressure sensor based on drum‐structured triboelectric nanogenerator (DS‐TENG) is developed to capture subtle pressure signals for physiological signal detection, information encoding, gesture recognition, and wireless real‐time robot control. The DS‐TENG enables a limit of detection down to 3.9 Pa pressure, which can sensitively capture human micromotion signals of pulse, throat sounds, and wrist muscles contraction. Especially, combined with a microprocessor and Morse code, the DS‐TENG worn on wrist can detect single‐finger motion signals to translate into regular voltage signals, which is employed to encode information of 26 letters and subsequently decode into the corresponding letters. Furthermore, with an aid of machine learning, the DS‐TENG array (2 × 2) can successfully achieve gesture recognition with high accuracy of 92% to wirelessly perform real‐time robot control. Consequently, the wearable pressure sensor based on DS‐TENG can capture subtle pressure signals for information encoding and wireless real‐time robot control, which demonstrates extreme potential in the field of HMI and artificial intelligence.

Adaptive Particle Swarm Optimization with Landscape Learning for Global Optimization and Feature Selection

Particle swarm optimization (PSO), an important solving method in the field of swarm intelligence, is recognized as one of the most effective metaheuristics for addressing optimization problems. Many adaptive strategies have been developed to improve the performance of PSO. Despite these advances, a key problem lies in defining the configuration criteria of the adaptive algorithm. This study presents an adaptive variant of PSO that relies on fitness landscape analysis, particularly via ruggedness factor estimation. Our approach involves adaptively updating the cognitive and acceleration factors based on the estimation of the ruggedness factor using a machine learning-based method and a deterministic way. We tested them on global optimization functions and the feature selection problem. The proposed method gives encouraging results, outperforming native PSO in almost all instances and remaining competitive with state-of-the-art methods.