Computational Intelligence Research Articles

Data Security Issues in Intelligent Cloud Computing Systems

This project takes the intelligent cloud computing system as the main research object. It mainly uses the literature analysis method to explore the data security problems in the intelligent cloud computing system. By investigating the development background of intelligent cloud computing, the author summarizes the definition of cloud computing and the characteristics of cloud computing. The author also makes a specific analysis of the advantages of cloud computing, and concludes that cloud computing is possible through safe and reliable data, low demand for clients, shared cloud data and unlimited possibilities. These four points are the main reasons major enterprises choose cloud computing to replace traditional computing resources. In the topic, the author studied the data security problems in cloud computing from three aspects: the problems exposed in the development process of cloud computing, the effective measures to protect data security in cloud computing and predicting the future development trend of improving data security. The authors believe that future research should focus on a balance between network security issues in cloud computing and building a secure and reliable data system.

An agent-based simulation modeling framework for Mobility-as-a-Service (MaaS)

This study develops an agent-based intelligent Mobility-as-a-Service (MaaS) simulation model consisting of three types of agents (i.e., MaaS fleet unit, travelers, and central intelligent mobility assignment module) to assess mobility service assignment processes balancing conflicting entities (e.g., demand and supply) within the MaaS ecosystem. The study follows a two-level optimization method (i.e., lower, and upper levels). It employs artificial intelligence and multicriteria decision making to solve two-dimensional mobility assignment problems (demand vs. supply). The novelty of this assignment process is that it implements an intelligence module accounting for the past system performance to make a future assignment decision in favor of both sides of the operation. This study processes 24-hour trip requests extracted from Nova Scotia Travel Activity (NovaTRAC) survey data in real-time. Two scenarios (1-D and 2-D assignments) are compared using the cost criteria, such as total waiting time, empty time, and idle time. The 1-D scenario refers to mobility assignment that emphasizes the demand side only, and the 2-D scenario formulates mobility assignment by balancing the demand and supply sides of the MaaS ecosystem. Experimental results indicate that a MaaS fleet of 350 units is the most balanced fleet in both scenarios. However, the 2-D optimization method reduces the overall supply cost by 25%. Moreover, 2-D operation demonstrates a higher fleet utilization over a considerable period, whereas the 1-D design guarantees a higher fleet utilization only during the peak period. Results of this study provide us with a benchmark for assessing more complex MaaS operation scenarios which will further aid in advancing the operational MaaS ecosystem. Our findings can help policymakers implement cost-effective MaaS solutions supporting sustainable urban mobility and SDG 13: Climate Action.

A preliminary discussion on data privacy and data security issues

With the expeditious evolution of technologies such as the internet, big data, cloud computing, and artificial intelligence, data has ascended to the status of an invaluable and indispensable resource within the intricate tapestry of modern society. Yet, the labyrinthine ethical quandaries that intertwine with the processes of data collection, storage, processing, and application have progressively surfaced, capturing the rapt attention of academia, industry, and government as they grapple with this nascent challenge. However, with the extensive application of data technology, issues of data ethics have become increasingly prominent, posing unprecedented challenges to personal privacy, data security, social equity, and justice. This article aims to delve deeply into the connotation and extension of data privacy and data security, as well as its significant role in current society. By examining relevant research findings at this stage, analyzing the major challenges faced by data ethics and exploring corresponding strategies, this paper delves into the multifaceted challenges faced by data privacy and data security, highlights the inadvertent data collection by smart devices, the potential for re-identification of blurred and anonymized information through big data analysis, as well as the ongoing threats posed by hacker attacks, internal vulnerabilities, and the complexity of data circulation.

Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration

With the development of artificial intelligence (AI) and high-performance computing (HPC), the microelectronic industry is challenged with increased device integration density. Chiplet architecture can break through a variety of limitations on the system-on-chip (SoC) to create a high-computility system. However, chiplet heterogeneous integration suffers from high heat flux and serious thermal interaction problems. The factors affecting thermal interaction are not clear. In this paper, a collective parameter model and a distribution parameter model are developed to clarify the optimization method to mitigate thermal interaction. The trends predicted by the parameter model are consistent with the finite element method (FEM) simulation results. Furthermore, to cool the chiplets, a thermal test vehicle is designed and fabricated, and the cooling performance of the test vehicle with different flow rates, different TIMs (Thermal Interfacial Materials) (DOW5888 vs. liquid metal), and different heat modes is experimentally investigated. Compared with DOW5888, the utilization of liquid metal TIM can mitigate thermal interaction by 56% and 76% at flow rates of 0.2 L/min and 0.8 L/min, respectively. Consequently, at a temperature rise of 60 °C and a flow rate of 0.8 L/min, using liquid metal TIM can achieve heat fluxes of 330 W/cm2 and 520 W/cm2 for two chiplets, respectively.

Advances in liquid biopsy: Computational and artificial intelligence approaches in cancer research

Liquid biopsy analysis has emerged as a promising approach for non-invasive cancer monitoring and diagnosis. This review provides an overview of the current landscape and future potential of liquid biopsy in cancer research, with a particular focus on the computational methodologies and techniques utilized for liquid biopsy data analysis and interpretation. The challenges and opportunities in extracting meaningful insights from the vast array of genomic, epigenomic, transcriptomic, and proteomic data are discussed, as well as the possibilities and current pitfalls of artificial intelligence approaches. In addition, the benefits and limitations of integrating multimodal cancer research data are covered, with a focus on advancing precision oncology and personalized medicine. By providing a critical assessment of the field, this review aims to foster knowledge about the available computational approaches and facilitate the choice of the most appropriate methodology for in silico investigation of liquid biopsy data, ultimately enhancing research endeavors and disease management strategies. Most of the works discussed in this review have emerged within the past 5 years, indicating a rapidly growing interest in this technique.

RESEARCH ON THE CONSTRUCTION OF AI COMPOSITION SYSTEM BASED ON HMM

The Markov chain is an automatic accompaniment algorithm for intelligent computer systems, belonging to the interdisciplinary research field of musicology and computer science. Currently, there are many methods for AI music generation, but research on AI music generation based on the Hidden Markov Model (HMM) is relatively scarce. This paper proposes a method for constructing an AI composition system based on the HMM. This system achieves the goal of automatically generating accompaniment music from score data. The proposed system has achieved relatively stable results in the generation of musical elements such as form and harmony, accompaniment texture, and instrumentation, and has scored well in evaluation experiments.

A Division-of-Labour Approach to Traffic Light Scheduling

Traffic light scheduling is a critical aspect of traffic management with many recently developed solutions that incorporate computational intelligence approaches. This paper presents a traffic light scheduling algorithm based on a task allocation model that simulates the division of labour among insects in a colony, specifically ant colonies. The developed algorithm switches the green light based on a probability calculated every second from the traffic volume around the traffic light. The application of this algorithm to several benchmark simulated traffic scenarios shows optimal performance compared to five other traffic scheduling algorithms.

Digital twins in healthcare: Applications, technologies, simulations, and future trends

AbstractThe healthcare industry has witnessed significant interest in applying DTs (DTs), due to technological advancements. DTs are virtual replicas of physical entities that adapt to real‐time data, enabling predictions of their physical counterparts. DT technology enhances understanding of disease occurrence, enabling more accurate diagnoses and treatments. Integrating emerging technologies like big data, cloud computing, Virtual Reality (VR), and internet‐of‐things (IoT) provides a solid foundation for DT implementation in healthcare. However, defining DTs within the healthcare context still has become increasingly challenging. Therefore, exploring the potential of DTs in healthcare contributes to research, emphasizing their transformative impact on personalized medicine and precision healthcare. In this study, we present diverse healthcare applications of DTs, including healthcare 4.0, cardiac analysis, monitoring and management, data privacy, socio‐ethical, and surgical. Moreover, this paper discusses the software and simulations of DTs that can be used in these applications of healthcare, as well as, the future trends of DTs in healthcare.This article is categorized under: Application Areas > Health Care Technologies > Computational Intelligence

Materials Acceleration Platforms (MAPs): Accelerating Materials Research and Development to Meet Urgent Societal Challenges.

Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economicprosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance Computing through Materials Acceleration Platforms, MAPs. In this perspective, following the short paper, a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome them.

Study of resistive properties and neural response of ZrO2/TiO2 heterojunction nanowire array (NWA) RRAM

Nowadays, the researcher has an eye on enhancing the performance of resistive random-access memory (RRAM) and exploring new demands and applications, particularly in the field of artificial intelligence and neural computing. It is crucially important for accurately replicating the observed plasticity within synapses to achieve superior RRAM performance in low-dimensional nanomaterials. In this work, we focus on addressing certainly unsatisfactory electrical properties of RRAM based on titanium oxide nanowire array (TiO2 NWA), for example, short retention time (∼103 s). Theoretically and experimentally, a novel and solution-based RRAM structure, namely ZrO2/TiO2(NWA), has been conducted. With the help of the ZrO2 insertion layer, the RRAM device shows impressive resistive switching characteristics, including 200 current–voltage(I-V) sweeps without degradation, increased cycling endurance (>105 cycles), and a long retention time (∼9 × 104 s). These findings hold significant implications for digital computing systems. Moreover, the successful implementation of the proposed device enables the emulation of fundamental synaptic biological features such as non-linear transmission properties, learning experiences behavior, short-term potentiation (STP) and long-term potentiation (LTP). This research provides evidence of the immense potential of the Ag/ZrO2/TiO2(NWA)/FTO RRAM device in bipolar non-volatile memory (NVM) and biomimetic neuromorphic systems.

A New Paradigm for Semiconductor Manufacturing: Integrated Synthesis, Delivery, and Consumption of Source Chemicals for IC Fabrication

The semiconductor industry is being radically impacted by the placing of greater emphasis on the development of hetero-devices and systems that will act as essential drivers for a wide spectrum of technological applications. The introduction of new materials and their integration with currently used materials are projected to replace integrated circuitry (IC) design and device scaling as the key enablers to the realization of improved device performance and larger density gains. Yet material selection has been constrained by existing fabrication process technology. To date, fabrication processes have dictated material selection by limiting chemical sources or precursors to those that match existing process tools associated with chemically based vapor phase processes and their variants, which in turn limits material compositions in ICs. The processing and integration of new materials compositions and structures will require the introduction of new deposition and etching processes, and manufacturing worthy tool designs and associated protocols that provide new methods for atomic-level control. To this end, a novel manufacturing paradigm is presented comprising a method and system for real-time, closed-loop monitoring and control of synthesis, supply, and consumption of precursors in process intensification techniques including chemical vapor deposition (CVD), atomic layer deposition (ALD), atomic layer etching (ALE), and other IC manufacturing processes. This intelligent automated manufacturing approach is consistent with a central component of the semiconductor industry’s recent adoption of Industry 4.0., including vertical integration of IC manufacturing through robotization, artificial intelligence, and cloud computing. Furthermore, the approach eliminates several redundant steps in the synthesis, handling, and disposal of source precursors and their byproducts for CVD, ALD, ALE and other chemically based manufacturing processes, and thus ultimately lowers the manufacturing cost for both conventional and new IC materials. Further, by eliminating the issues associated with precursor thermal, chemical, and pyrophoric instabilities, this new paradigm enables the deposition of a myriad of new thin-film materials and compositions for IC applications that are practically unattainable with existing precursors. Preliminary and planned demonstration examples for the generation and deposition of highly toxic and unstable source precursors are provided.

Novel computational intelligence-based model for effective traffic management in intelligent transportation system

<p><span lang="EN-US">The evolution of intelligent transportation system (ITS) is essentially meant for upgrading the driving experience with more safety and accessibility of various analytical information from its extensive network. However, a significant gap is observed that doesn't cater up the complete demands of ITS. It has been also noted that computational intelligence (CI) based approach is slowly gaining pace in solving the transport related problems in ITS as compared to its other counterpart existing methodologies like artificial intelligence. The proposed manuscript introduces a novel computational framework towards assisting in relaying routing and navigational services using CI-based approach. A design of novel navigational controller unit is presented for global ITS scenario towards yielding an optimal decision of routing. The CI-based approach is implemented by integrating fuzzy process with evolutionary searching, learning and probability theory in most simplified form. The study also introduces a novel concept of relaying decision as feedback from navigational controller unit to specific vehicular node discretely unlike existing traffic controller system with an agenda to offer faster and effective clearance of queued vehicular nodes from target area. The study outcome shows higher consistency in its relay with better performance from existing study model in ITS.</span></p>

Knowledge transfer, not just true beliefs: virtue epistemology as a philosophical basis for education

Introduction. Modern pedagogical practice is being reformed due to systemic changes in the ways of social interaction. Knowledge transfer is being significantly modified under the influence of technical innovations, although it remains traditionally one of the main tasks of the educational process. The development of artificial intelligence, neural networks and other analytical computer systems shows that propositional knowledge is not the privilege of the human mind. This state of affairs changes the idea of knowledge as such. In this regard, the participants of the educational process face the task of revising the philosophical basis of modern learning in terms of finding a new definition of knowledge. It is also necessary to develop new methodological recommendations that would correspond to modern epistemological theories of knowledge, belief and understanding. Materials and research methods. The article uses the methods of conceptual analysis and comparative study characteristic of philosophy. The research materials are sources on theory of knowledge, education and also on applied epistemology. Results. Virtue epistemology is one of the most actively developing theories of knowledge in contemporary philosophy and humanities. It is based on the idea that knowledge cannot be an accidental belief, but must be the merit of a cognitive agent. This approach solves many problems of the philosophical theory of knowledge and creates a subjectoriented view of the nature of the cognitive process. This attitude allows us to use virtue epistemology in debates about applied issues, including those related to the explanation of the tasks and content of education. Discussion. A feature of the analysis within virtue epistemology is attention to cognitive skills (reliabilism) and sustained intellectual dispositions (responsibilism) in their relationship to the process of formation of true beliefs. Virtue epistemology defines knowledge through the notion of merit in the manifestation of subjective qualities, so the student's motivation and the context of belief formation should be taken into account when developing specific methodological recommendations. Such an approach explains why standardized testing cannot serve as the sole and primary means of assessing learners. At the same time, the general semantic peculiarity of the notions of virtue allows us to appeal to examples of individuals as ideal types of certain qualities (this is called exemplarism). At the same time, it is necessary to explain why these traits are the excellences that make their bearer better in epistemic terms. Conclusion. In explaining the knowledge transfer, pedagogy can be based on the philosophical principles of virtue epistemology. Such integration enriches both disciplines: pedagogy gets a conceptual scheme adequate to the modern social and technological situation, and epistemology gets a practical application.

Intelligent joint communication and computation scheme of UAV-assisted Offloading in high speed rail scenarios

With the continuous development of communication and computation technologies, large bandwidth services place higher demands on computing capacity and throughput in High-Speed Rail (HSR) scenarios. On the one hand, Millimetre-Wave enables high data transmission rates, but leads to high Doppler frequency deviation as well as large path loss. On the other hand, the development of Mobile Edge Computing greatly alleviates user computing congestion. In this paper, we propose the Adaptive Joint Communication and Computation Resource Allocation scheme to solve the energy optimization problem of a dual-band UAV and Mobile Relay relay-assisted HSR offloading system. This scheme works well to optimize the performance of the system through resource allocation. In addition, since the optimization problem is modeled as a Mixed-Integer Nonlinear Program, we propose the Pre- and Post-state connected Parameterized Deep Q-Network algorithm, which is based on Deep Reinforcement Learning approach, for offloading decision and bandwidth resource allocation. Simulation results show that the proposed algorithms result in lower system energy consumption, while ensuring a high task completion rate as well.

Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks

Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks

Artificial Intelligeence in Construction Industry

Abstract: The construction industry is currently experiencing an increasing skill shortage due to which, many businesses are pressurized. Artificial Intelligence is bringing transformation in construction industry through automation and in finance by implementing business models. Artificial Intelligence is providing novel solutions to the problems faced in construction industry. Artificial Intelligence when implemented in Construction Industry is expected to alter Business Models in logistics, customer relationship management, and support. Artificial Intelligence helps in providing amicable realistic solutions to situations, training, reducing injuries, and costly construction industry. Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Implementation of artificial intelligence in the construction industry and analysis of existing technologies. Here, the research is done on the applications which are done on Artificial intelligence. The study used fuzzy logic methods and machine learning to mark visual data and analyze it for potential threats and to reduce all possible risks. This technology allows users to explore different construction plans in real time and understand the impact of key building decisions on the various factors especially cost and duration of a project. The study highlights the potential of artificial intelligence and machine learning technology in the construction industry. The implementation of the proposed approaches will improve the efficiency of construction, its safety and quality. The results of the study will be useful in the standardization of construction processes and the processing of data on construction projects.

Computing advertising intelligent computing and push based on artificial intelligence in the big data era

In the era of big data, intelligent computation and precise targeting of advertisements rely on artificial intelligence (AI) technology to enhance advertising effectiveness and user experience. As advertising media shift from traditional outlets to the internet and mobile devices, manually filtering and targeting advertisements has become increasingly ineffective due to the vast amount of information and user groups. Consequently, AI-based advertisement computation and targeting technologies have emerged. This paper explores how modern technologies and AI applications can be used to achieve intelligent computation, filtering, and targeting of advertisements, thereby improving ad effectiveness and profitability. The study optimizes ad targeting effectiveness and increases return on investment (ROI) by comparing ad campaigns between Group A, which did not use ad targeting algorithms, and Group B, which employed AI-based targeting algorithms. The experimental results show that the average ROI for tourism, shopping, and rental ads in Group A were 153.03 %, 232.32 %, and 192.57 %, respectively, while Group B's average ROI were 173.96 %, 288.74 %, and 216.12 %. These results indicate that AI-based ad targeting algorithms can significantly improve the ROI of ad campaigns compared to non-algorithmic approaches, suggesting that ad targeting algorithms can help advertisers achieve higher profits.

Quantum Software Engineering: Practical Challenges

Quantum computing is a young discipline that specializes in the construction of hardware and software using the quantum properties of nature, to solve problems of higher complexity that classical computing cannot address, in multiple business areas. The most important computing revolution of the last 60 years has begun because of the integration of classical computing, quantum computing and artificial intelligence. In this paper, we present a pragmatic survey of the main quantum computing areas, focusing in particular on software and the need for real Quantum Software Engineering (QSE) to produce quantum software with sufficient quality and productivity, which is the fundamental axis for the diffusion of quantum computing.

Hybrid raven roosting intelligence framework for enhancing efficiency in data clustering

The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios. In response to these challenges, we propose the "hybrid raven roosting intelligence framework" (HRIF) algorithm. HRIF draws inspiration from the dynamic behaviors of roosting ravens and computational intelligence. What distinguishes HRIF is its effective capacity to adeptly navigate the clustering landscape, evading local optima and converging toward optimal solutions. An essential enhancement in HRIF is the incorporation of the Gaussian mutation operator, which adds stochasticity to improve exploration and mitigate the risk of local minima. This research presents the development and evaluation of HRIF, showcasing its unique fusion of nature-inspired optimization techniques and computational intelligence. Extensive experiments with diverse benchmark datasets demonstrate HRIF's competitive performance, particularly its capability to handle complex data and avoid local minima, resulting in accurate clustering outcomes. HRIF's adaptability to challenging datasets and its potential to enhance clustering efficiency and solution quality position it as a promising solution in the world of data exploration.

Optimization test function synthesis with generative adversarial networks and adaptive neuro-fuzzy systems

This paper presents an approach to synthesizing optimization test functions that couples generative adversarial networks and adaptive neuro-fuzzy systems. A generative adversarial network produces optimization landscapes from a database of known optimization test functions, and an adaptive neuro-fuzzy system performs regression on the generated landscapes to provide closed-form expressions. These expressions can be implemented as fuzzy basis function expansions. Eight databases of two-dimensional optimization landscapes reported in the literature are used to train the generative network. Exploratory landscape analysis over the generated samples reveals that the network can lead to new optimization landscapes with features of interest. In addition, fuzzy basis function expansions provide the best approximation results when compared against two symbolic regression frameworks over several selected landscapes. Examples are used to illustrate the ability of these functions to model complex surface artifacts such as plateaus. The proposed approach can be used as a mathematical collaboration tool that couples generative artificial and computational intelligence techniques to formulate high-dimensional optimization test problems from two-dimensional synthesized functions.