Development Of Intelligent Applications Research Articles

Databricks- Data Intelligence Platform for Advanced Data Architecture

Databricks, as a unified analytics platform, has emerged at the forefront of this evolution, offering scalable cloud-based solutions for data science and ML applications. This article explores the journey of Databricks in enabling data-driven decision-making through advanced analytics techniques. From its roots in Apache Spark to its current status as a leading platform for data engineering, data science, and machine learning, Databricks has continuously evolved to meet the growing demands of modern enterprises. This article examines the progression of data science/Machine Learning applications in Databricks, tracing their development from initial implementation to current state-of-the-art techniques and integration within the platform. Initially, the article delineates the inception of Databricks, focusing on its architecture and the early adoption of Apache Spark for big data processing. It explores how the platform's native support for various programming languages and its unified analytics engine facilitated the early stages of intelligent application development. The article further discusses the implications of these advancements for the future of data science and Intelligence within Databricks and the broader analytics ecosystem. It highlights the potential for further integration of AI and ML technologies, such as automated machine learning (AutoML) and real-time analytics, in enhancing decision-making processes and operational efficiencies across industries. The evolution of data science in Databricks has played a pivotal role in advancing big data analytics, offering scalable, efficient, and user-friendly solutions. This study not only charts the historical development of these applications within Databricks but also provides insights into future trends and potential areas for innovation. As data continues to grow in volume and complexity, platforms like Databricks will be instrumental in harnessing the power of data science and ML to drive insights and value across sectors.

An Intelligent Air Monitoring System For Pollution Prediction: A Predictive Healthcare Perspective

Abstract The extensive potential of Internet of Things (IoT) technology has enabled the widespread real-time perception and analysis of health conditions. Furthermore, the integration of IoT in the healthcare industry has resulted in the development of intelligent applications, including smartphone-based healthcare, wellness-aware recommendations and smart medical systems. Building upon these technological advancements, this research puts forth an enhanced framework designed for the real-time monitoring, detection and prediction of health vulnerabilities arising from air pollution. Specifically, a four-layered model is presented to categorize health-impacting particles associated with air pollution into distinct classes based on probabilistic parameters of Health Adversity (HA). Subsequently, the HA parameters are extracted and temporally analyzed using FogBus, a fog computing platform, to identify vulnerabilities in individual health. To facilitate accurate prediction, an assessment of the Air Impact on Health is conducted using a Differential Evolution-Recurrent Neural Network. Moreover, the temporal analysis of health vulnerability employs the Self-Organized Mapping technique for visualization. The proposed model’s validity is evaluated using a challenging dataset comprising nearly 60 212 data instances obtained from the online University of California, Irvine repository. Performance enhancement is assessed by comparing the proposed model with state-of-the-art decision-making techniques, considering statistical parameters such as temporal effectiveness, coefficient of determination, accuracy, specificity, sensitivity, reliability and stability.

Sensor-based optimization multi-decision model for sustainable smart cities

The development of intelligent applications using sensing technology in cooperation with the Internet of Things (IoT). In combination, they allow Internet users to get the necessary data from cloud systems by transmitting information from objects to web servers. The community demands, such as response times and reliable remote connections for accessing the information from IoT networks. Although, the majority of proposed solutions provide the optimal way to direct network traffic toward cloud storage. However, most of them are still needed to manage resources efficiently while handling huge amounts of data. Moreover, distributed IoT services must be intelligent and also be secure against malicious data access. This study introduces an autonomous approach using trusted sensing technologies to provide reliable remote sensing and aggregation for future-generation systems. In the first phase, the proposed model looks at an artificial intelligence-based method for negotiation between IoT devices and mobile stations for transferring the data. The second phase involves the creation of a robust security method that supports digital certificates for peer-to-peer mutual authentication. Additionally, to maintain the integrity of certificates, they are sent securely in the form of a chain. Finally, a combination of trust and privacy is used while storing data on cloud servers. Simulation-based experiments have shown that the proposed model significantly outperforms I-AREOR and MCNA in terms of energy consumption, packet delivery ratio, malicious threats, and overhead.

Joint Optimization of Computation, Communication and Caching in D2D-Assisted Caching-Enhanced MEC System

In the era of intelligent applications, Mobile Edge Computing (MEC) is emerging as a promising technology that provides abundant resources for mobile devices. However, establishing a direct connection to the MEC server is not always feasible for certain devices. This paper introduces a novel Device-to-Device (D2D)-assisted system to address this challenge. The system leverages idle helper devices to execute and offload tasks to the MEC server, thereby enhancing resource utilization and reducing offload time. To further minimize offloading time for latency-sensitive tasks, this paper incorporates edge caching. The problem is formulated by jointly optimizing computation, communication and caching, and a novel Joint Multiple Decision Optimization Algorithm (JMDOA) is proposed to solve the minimum-energy-consumption problem. Specifically, the JMDOA algorithm decomposes the integer-mixed non-convex optimization problem into two subproblems based on distinct properties of discrete variables. These subproblems are solved separately and optimized iteratively, ensuring convergence to a suboptimal solution. Simulations demonstrate the effectiveness and superiority of JMDOA, exhibiting lower energy consumption and reduced time compared to other baseline algorithms, approaching the optimum. This work contributes to the field by presenting a novel approach to optimizing resource allocation in MEC systems, with potential implications for the future development of intelligent applications.

A novel methodology for multi-agent decision-making based on N-soft sets

AbstractMulti-criteria decision analysis with multiple agents provides tools for the research and development of intelligent applications. The solution strategy depends largely on the structure of the data. In this paper we propose a decide-then-merge approach to the problem of multi-agent multi-criteria decision-making when each individual data comes in the form of an N-soft set. To this purpose, we present a methodology that works in two steps. First, with each individual input we associate a ranking. This can be done in several ways, since individual decisions based on N-soft sets are well developed. Then with the help of voting theory, we merge all these rankings and produce a final ranking of the alternatives. The steps of this method are explained with synthetic examples. We argue that the proposed strategy is highly adaptable because both stages of our scheme can be customized by the user. A real case study in the framework of Operational Research illustrates the application of this method in detail. Finally, we perform a comparative analysis with existing methodologies.

Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical data

The phenomenal popularity of social media platforms over the past decade has accelerated the development of intelligent applications that leverage social media data for informed decision-making in diverse domains like finance, education, public policy and healthcare management practices. While understanding the colloquial language of users on social media remains a challenging problem, access to users’ medical perspectives that conversationally divulge healthcare-related experiences and insights can help reshape healthcare ecosystems like chronic disease management, pandemics, public health, pharmacovigilance and more. Most existing models are constrained to a particular dataset while neglecting model adaptability across data sources and domains. Model generalization across variable data sizes also has received very little research attention. Conventional foundation models can be fine-tuned by adding additional model heads or by appending contributing network layers, however, there has been very little focus on effective parameter calibration for adapting neural foundation models to a specific task. In this study, an Adaptive Learning mechanism for Socio-Medical data (AL4SM) built on generic foundation neural models with efficient parameter learning is proposed, to categorize users’ perspectives on prescription drug-related experiences and adapt to diverse socio-medical data sources of variable sizes. AL4SM aims to lighten the over-parameterized mechanisms adopted by existing foundational techniques by efficiently learning latent medical information based on optimized parameter calibration and weight reinitialization techniques. Comprehensive cross-domain and cross-data analyses are undertaken to explore specific user perspectives related to prescription effectiveness and side effects. Validation experiments conducted on standard datasets obtained from Drugs.com and Druglib.com revealed that the proposed AL4SM outperformed state-of-the-art models, achieving an improvement of 6.06% in accuracy and 7.62% in F1-score for 3-class and 2% in F1-score for 10-class drug perspective categorization. The cross-data experiments further emphasized the superiority of the proposed model, with improved accuracy of 17% on Drugs.com and 9% on Druglib.com datasets, respectively.

A Software-Based Remote Attestation Scheme for Internet of Things Devices

With the rapid development of intelligent applications, many Internet of Things (IoT) devices are deployed in various application scenarios, playing an extremely important role. Remote attestation is an important method to ensure the software integrity of these devices and protect them from several attacks. Due to the lack of security hardware and no support of hardware extensions for Class-1 IoT devices, it is particularly important to design a suitable remote attestation scheme for these devices. In this paper, we first propose the delayed observation mechanism to alleviate the problem that the software-based remote attestation scheme is not suitable for wireless networks. At the same time, we propose a "filling memory at attestation-time" mechanism, which solves the problem that attackers hide malicious code through return-oriented programming. Finally, we introduce a reputation mechanism to assist our attestation, and adopt the principle of "making higher-performance verification nodes take on more work" to greatly reduce the time-consuming attestation. We analyze the security of the scheme and implement it on a UNO-R3 development board to prove its practicability and effectiveness. Compared with traditional software-based attestation schemes, our scheme can reduce the attestation time and resist proxy attacks.

Evolutionary Game Theory-Based Optimal Scheduling Strategy for Heterogeneous Computing

With the development of intelligent applications, simply relying on traditional single type of computing unit cannot efficiently satisfy diverse cloud requirements. The emergence of heterogeneous computing can efficiently achieve the adaptation of these intelligent applications by using different types of processing units such as Graphics Processing Unit (GPU) and Field Programmable Gate Array (FPGA). However, the trade-off between profit and costs in the process of scheduling heterogeneous computing resources is also an issue worthy of attention. To address this challenge, this work establishes a heterogeneous computing resource scheduling model based on Stackelberg differential game, which includes three roles Computing Power Trading Platforms (CPTPs), Heterogeneous Computing Service Providers (HCSPs), and Heterogeneous Computing Application Providers (HCAPs). The objective is to maximize utility function of CPTPs and HCSPs subject to rental ratio, pricing strategy and energy consumption of resource scheduling, which has proved that there exists a Stackelberg Nash Equilibrium (NE) solution. The Support Vector Machine based on Artificial Fish (SVM-AF) is proposed to predict the access times of heterogeneous computing applications. In addition, the distributed iteration method and Cauchy distribution is adopted to optimize the computing price strategy and improve its convergence performance. The simulation results show that compared with other strategies, the proposed strategy can effectively improve computing revenue of user experience and while reducing energy consumption in the process of resource scheduling.

Application of Computer Vision in Intelligent Manufacturing under the Background of 5G Wireless Communication and Industry 4.0

With the development of 5G wireless communications and the arrival of Industry 4.0, computer vision has further penetrated the manufacturing sector. This paper makes a comprehensive study on the field of intelligent manufacturing. By constructing a model analysis method, the current situation of computer vision intelligent manufacturing under the background of 5G wireless communication and industry 4.0 is comprehensively analyzed. It is found that today 's computer vision technology is developing rapidly, while the Industry 4.0 stage and 5G communication technology have helped the field of intelligent manufacturing. At the same time, the author deeply analyzes the characteristics of intelligent manufacturing enterprises supported by computer vision and finds that it has the problem of unbalanced distribution of enterprises. After that, the author demonstrates the application of computer vision re-intelligent manufacturing from multiple dimensions. The application of computational optimization methods provides more diverse and comprehensive algorithms for labor saving and resource utilization improvement. Computer vision technology drives and promotes the development of intelligent applications and promotes a more comprehensive development of intelligent manufacturing.

Editorial on decision support system for development of intelligent applications

Editorial on decision support system for development of intelligent applications

Quão experimentais e estratégicas são as aplicações de Business Intelligence (BI) e Data Mining?

Objective: Identify and characterize the methodologies used for the experimental development of intelligent applications aligned with strategic planning.Methodology: A systematic mapping was carried out to characterize the research in the area, considering the last ten years.Originality: No scientific studies were found with the same research object of this article, to identify and characterize the methodologies for the experimental development of intelligent applications aligned with strategic planning, which increases the importance of the results presented here.Main results: As a result, no studies were found that presented any complete approach to discipline strategic alignment and experimentation, providing clear compliance with strategic objectives and an experimental phase in the validation of results. However, some trials of parts of these characteristics could be mapped, such as experimentation found in 28,57% of the studies. Among the countries, China, the United States and Brazil led the ranking of publications on the subject. As for the medium of publication, Journal was the most used option for publication. In addition, the "IEEE International Conference on Advanced Communications, Control and Computing Technologies" and the journal "Expert Systems with Applications" stood out as major publishers.Theoretical Contributions: This research presents results relevant to academia and entrepreneurs, providing evidence that there is a gap in research on a formal method of BI and Data Mining applications experimental and strategy-driven development. In addition, this work is presented as a source of consultation to the existing method standards for the development of intelligent applications, as well as being replicable and extended by the applied systematization. Finally, there is a focus on research that proposes methods of creating experimental applications validated experimentally and aligned with strategy.

High-Sensitivity Pressure Sensors Based on a Low Elastic Modulus Adhesive.

With the rapid development of intelligent applications, the demand for high-sensitivity pressure sensor is increasing. However, the simple and efficient preparation of an industrial high-sensitivity sensor is still a challenge. In this study, adhesives with different elastic moduli are used to bond pressure-sensitive elements of double-sided sensitive grids to prepare a highly sensitive and fatigue-resistant pressure sensor. It was observed that the low elastic modulus adhesive effectively produced tensile and compressive strains on both sides of the sensitive grids to induce greater strain transfer efficiency in the pressure sensor, thus improving its sensitivity. The sensitivity of the sensor was simulated by finite element analysis to verify that the low elastic modulus adhesive could enhance the sensitivity of the sensor up to 12%. The preparation of high-precision and fatigue-resistant pressure sensors based on low elastic modulus, double-sided sensitive grids makes their application more flexible and convenient, which is urgently needed in the miniaturization and integration electronics field.

Potential technologies and applications based on deep learning in the 6G networks

The fifth generation (5G) networks provide high speed, low latency, and reliable communication services, and have been making great differences in our daily life. However, it might be difficult to meet the growing demands of Internet of Things (IoT) devices based on the existing technologies in the future, so the concept of sixth generation (6G) networks were raised to improve the existing 5G networks and promote the development of intelligent applications furthermore. Therefore, we introduce the characteristics, challenges, and potential technologies of 6G networks, and find that deep learning is potential to overcome these challenges with the help of big data and cloud computing. In addition, Deep learning approaches and potential applications in 6G networks are also discussed. We summarize the important viewpoints and studies, and provide some new ideas for developing 6G networks.

Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using Smartphones

There has been an increasing interest in the problem of inferring emotional states of individuals using sensor and user-generated information as diverse as GPS traces, social media data and smartphone interaction patterns. One aspect that has received little attention is the use of visual context information extracted from the surroundings of individuals and how they relate to it. In this paper, we present an observational study of the relationships between the emotional states of individuals and objects present in their visual environment automatically extracted from smartphone images using deep learning techniques. We developed MyMood, a smartphone application that allows users to periodically log their emotional state together with pictures from their everyday lives, while passively gathering sensor measurements. We conducted an in-the-wild study with 22 participants and collected 3,305 mood reports with photos. Our findings show context-dependent associations between objects surrounding individuals and self-reported emotional state intensities. The applications of this work are potentially many, from the design of interior and outdoor spaces to the development of intelligent applications for positive behavioral intervention, and more generally for supporting computational psychology studies.

Label-less Learning for Traffic Control in an Edge Network

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.

Learning for Smart Edge: Cognitive Learning-Based Computation Offloading

With the development of intelligent applications, more and more intelligent applications are computation intensive, data intensive and delay sensitive. Compared with traditional cloud computing, edge computing can reduce communication delay by offloading computing tasks to edge cloud. Furthermore, with the complexity of computing scenarios in edge cloud, deep learning based on computation offloading scheme has attracted wide attention. However, all the learning-based offloading scheme does not consider the where and how to run the offloading scheme itself. Thus, in this paper, we consider the problem of running the learning-based computation offloading scheme for the first time and propose the learning for smart edge architecture. Then, we give the computation offloading optimization problem of mobile devices under multi-user and multi edge cloud scenarios. Furthermore, we propose cognitive learning-based computation offloading (CLCO) scheme for this problem. Finally, experimental results show that compared with other offloading schemes, the CLCO scheme has lower task duration and energy consumption.

A Practical Teacher–Robot Collaboration Lesson Application Based on PRINTEPS

To support elementary school teachers in teaching by encouraging active learning while maintaining the interest of pupils, this study focuses on supporting teaching, learning, and monitoring the progress of students through a Teacher–Robot collaboration lesson application using not only laptops and tablets, but also robots and sensors. Since developing a lesson application is time consuming for teachers, we have developed an integrated intelligent application development platform named PRactical INTElligent aPplicationS (PRINTEPS) to aid Teacher–Robot collaboration. However, several functions and interfaces for education are missing. Therefore, in this study, we extend several functions for education to PRINTEPS. In addition, since it is necessary in learning and monitoring the progress of students to present learning content suitable to each pupil’s level of understanding, we also have provided support through the use of a tablet quiz system based on ontologies and rule bases. In the case study, we developed a Teacher–Robot collaboration lesson application and conducted lessons for sixth-grade pupils at an elementary school. From the case study, we have confirmed the effectiveness of our platform and the application.

PRINTEPS: An Integrated Intelligent Application Development Platform based on Stream Reasoning and ROS

Although AI and service robot applications have become very popular in many domains recently, many of them are specific applications and it is still difficult to develop integrated intelligent applications such as a robot teahouse and teaching assistant robots. To develop such integrated intelligent applications, we need integrated intelligent application platforms that have AI integration and agile process facilities. From the above background, we are currently developing PRactical INTElligent aPplicationS (PRINTEPS), which is a platform for developing integrated intelligent applications by combining only five types of modules, namely knowledge-based reasoning, spoken dialogue, image sensing, motion management, and machine learning. This paper proposes a workflow editor in PRINETPS based on a service-oriented architecture and a Robot Operating System that enables real-time parallel processing for multiple robots and sensors by integrating the five types of modules. The editor also supports not only developers but also domain experts in updating workflows frequently. This paper also proposes a novel method to integrate signals acquired through image sensing with knowledge (ontologies and business rules) using C-SPARQL and Semantic Web Rule Language. To evaluate PRINTEPS, we developed a robot teahouse application including customer reception and guidance to table services using a humanoid robot with PRINTEPS. Through this case study, we demonstrated that the behaviors of the robot can be modified by changing the workflow, the ontology, and the rules.

Interoperability in IoT Through the Semantic Profiling of Objects

The emergence of smarter and broader people-oriented IoT applications and services requires interoperability at both data and knowledge levels. However, although some semantic IoT architectures have been proposed, achieving a high degree of interoperability requires dealing with a sea of non-integrated data, scattered across vertical silos. Also, these architectures do not fit into the machine-to-machine requirements, as data annotation has no knowledge on object interactions behind arriving data. This paper presents a vision of how to overcome these issues. More specifically, the semantic profiling of objects, through CoRE related standards, is envisaged as the key for data integration, allowing more powerful data annotation, validation, and reasoning. These are the key blocks for the development of intelligent applications.

Piecemeal Development of Intelligent Applications for Smart Spaces

Software development is facing new challenges as a result of evolution toward integration and collaboration-based service engineering, which embody high degrees of dynamism both at design time and run-time. Short times-to-market require cost reduction by maximizing software reuse. Openness for new innovations presumes a flexible development platform and fast software engineering practices. User satisfaction assumes situation-based applications of high quality. The main contribution of this paper is the piecemeal service engineering (PSE) approach developed for and tested in application development for smart spaces. The intent of PSE is to maximize the reuse of existing knowledge of business and design practices and existing technical assets in the development of new smart-space applications. Business knowledge is mostly informal and domain-dependent, but architectural knowledge is generic, at least semiformal, and represented in principles, ontologies, patterns, and rules that together form a reusable architectural knowledge base for fast smart-space application development. The PSE facilitates the incremental development of intelligent applications by supporting abstraction, aggregation, and adaptability in smart-space development.