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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： S. Sudha Merlin K. Malathy Ravi S Arun M Raut S.P H. Fawareh Department of Electrical and Electronics Engineering DMI College of Engineering Palanchur Chennai Department of MCA S.A. Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Dept of Computer Science and Engineering Kings College of Engineering Punalkulam Thanjavur Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of consumer data can be attributed to “the Internet of Things (IoT),” which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics.

关键词： Analytical models Consumer behavior Accuracy Computational modeling Neural networks Predictive models data models Internet of Things Logic Business

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Tiny Neural Networks for Environmental Predictions: An Integrated Approach with Miosix

Tiny Neural Networks for Environmental Predictions: An Integ...

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international conference on smart computing (smartCOMP)

作者： Francesco Alongi Nicolò Ghielmetti Danilo Pau Federico Terraneo William Fornaciari DEIB Informazione e Bioingegneria Politecnico di Milano Milan Italy System Research and Applications STMicroelectronics Italy

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169989

Collecting vast amount of data and performing complex calculations to feed modern Numerical Weather Prediction (NWP) algorithms require to centralize intelligence into some of the most powerful energy and resource hungry supercomputers in the world. This is due to the chaotic complex nature of the atmosphere which interpretation require virtually unlimited computing and storage resources. With Machine Learning (ML) techniques, a statistical approach can be designed in order to perform weather forecasting activity. Moreover, the recently growing interest in Edge computing Tiny intelligent architectures is proposing a shift towards the deployment of ML algorithms on Tiny Embedded Systems (ES). This paper describes how Deep but Tiny Neural Networks (DTNN) can be designed to be parsimonious and can be automatically converted into a STM32 microcontroller-optimized C-library through X-CUBE-AI toolchain; we propose the integration of the obtained library with Miosix, a Real Time Operating System (RTOS) tailored for resource constrained and tiny processors, which is an enabling factor for system scalability and multi tasking. With our experiments we demonstrate that it is possible to deploy a DTNN, with a FLASH and RAM occupation of 45,5 KByte and 480 Byte respectively, for atmospheric pressure forecasting in an affordable cost effective system. We deployed the system in a real context, obtaining the same prediction quality as the same DNN model deployed on the cloud but with the advantage of processing all the necessary data to perform the prediction close to environmental sensors, avoiding raw data traffic to the cloud.

关键词： Cloud computing Machine learning algorithms Atmospheric modeling Neural networks Weather forecasting Supercomputers Task analysis

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Predicting examination performance using machine learning approach: A case study of the Grade 5 scholarship examination in Sri Lanka

Predicting examination performance using machine learning ap...

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international Research conference on smart computing and Systems Engineering (SCSE)

作者： U. M. Ranawaka Chathura Rajapakse Faculty of Science University of Kelaniya Sri Lanka

ISBN: (数字)9781728172491

ISBN: (纸本)9781728172507

Universal primary school education is a must requirement and one of the criteria that should be fulfilled by the developing countries according to the international development goals which are also recognized as “Eight Millennium Development Goals” In the context of Sri Lanka, Government is mostly involved in primary education through government-controlled schools. The success of primary education is measured by conducting a scholarship examination. Those who are getting higher results are given opportunities to attend well-facilitated schools for secondary education. Due to that case, there is a massive competition for passing the examination. Limitless pressure for examination provides lots of issues to students. This paper uses data to investigate a model of academic performance as measured by past results of school tests of Grade 4 and Grade 5. 500 students from eight primary schools in the Gampaha district have been selected for collecting data. The data on the above-mentioned students have been collected by conducting questionnaires to the teachers who in-charged the classes. Then the Logistic Regression model and Multiple Linear Regression model have been applied to predict students' performances at the examination. The model depicts the likelihood of a student passing or failing the grade 5 scholarship examination and predicts the range of results that students will obtain in the examination. The accuracy of predictive models is measured using the results of students who have already faced the Grade 5 examination. Revealing the potential of students at the grade 5 examination is heavily benefited by teachers because they can provide personalized education for talented students and provide opportunities to other students to improve their talents. The initial architecture of the Grade 5 examination results' predictive model is being discussed in this paper.

关键词： Education Scholarships Government Predictive models data mining Predictive analytics Training

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Big data analytics-based intelligent Logistic System

Big Data Analytics-based Intelligent Logistic System

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： S. Sankar B. Shadaksharappa N. Ganesh Kumar K. Padmanaban Department of Management Studies Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Computer Science and Engineering GITAM School of Technology Gandhi Institute of Technology and Management (GITAM) (Deemed to be University) Bengaluru Karnataka India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India

Due to Internet and e-commerce data growth, logistics management uses big data. Big data’s key elements and technological components are explored, as well as domestic and international research findings, data mining, and data warehouse technologies. In firm logistics management, a logistics center is chosen to use a time series model to estimate transportation freight volume utilizing big data technologies. Big data-based intelligent logistics will improve productivity, cost, and user experience, according to this article. Big data technology may help logistics businesses manage, decide, engage, and allocate resources. Logistics 4.0 uses Industry 4.0 to handle massive volumes of data between equipment, goods, vehicles, and personnel. 5G Mobile Communication Networks might address these needs and boost smart Logistics growth. This study identifies the primary logistical sectors and activities where 5G may be deployed, stressing its projected benefits and supporting technology for large-scale adoption. The literature review also highlights 5G logistics adoption barriers, suggesting more research.

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Security Challenges and Solutions for 5G HetNet

Security Challenges and Solutions for 5G HetNet

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Aakanksha Sharma Venki Balasubramanian Alireza Jolfaei School of Engineering Information Technology and Physical Science Federation University Australia Ballarat Australia School of Engineering Information Technology and Physical Science Federation University Australia Ballarat VIC Australia Department of Computing Macquarie University Sydney Australia

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The exponential growth of smartphones and other smart communicating devices has led to the proliferation of the Internet of Things (IoT) applications. Literature shows, one person will have more than six intelligent connected devices in future. The existing network infrastructure and bandwidth will be unable to accommodate the growing number of smart connected devices, therefore, achieving the expected Quality of Service (QoS) and Quality of Experience (QoE) remains a challenge. The advent and deployment of 5G network bring a massive number of innovative network services and exceptional user experience by providing superior data rates. Despite numerous benefits that 5G offers, the security and privacy in 5G is a challenge due to the existing large number of heterogeneous networks (HetNet). To harvest the numerous benefits of 5G, it is imperative to provide adequate protection mechanisms to maintain the user and data privacy in growing HetNet. This article comprehensively addresses the existing security issues in 5G HetNet and solutions for the identified problems in the HetNet edge.

关键词： Privacy 5G mobile communication Quality of service User experience Security Quality of experience Internet of Things

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IoT based Classification Techniques for Soil Content Analysis and Crop Yield Prediction

IoT based Classification Techniques for Soil Content Analysi...

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international conference on I-SMAC (IoT in Social, Mobile, analytics and Cloud) (I-SMAC)

作者： R. Reshma V. Sathiyavathi T. Sindhu K. Selvakumar L. SaiRamesh JBAS College for Women Chennai India CEG Campus Anna University Chennai India National Institute of Technology Tiruchirappalli India

ISBN: (数字)9781728154640

ISBN: (纸本)9781728154657

Agriculture aided by IoT is called smart Agriculture and it gives rise to precision farming. Soil Monitoring combined with Internet of Things (IoT) technology aids in the enhancement of agriculture by increasing yield through gauging the exact soil characteristics such as Moisture, Temperature, Humidity, PH, and Nutrition content/Fertility. This data is then gathered in cloud storage and with the appropriate data operations; it enabled us to optimize farming strategies and helped create a trend analysis. This, then, allows us to precisely utilize resources and steer the farming methods in prudent ways to optimize yield. The proposed IoT system is composed of pH sensors, Humidity and temperature sensors, Soil moisture sensors, soil nutrient sensors (NPK) probes, microcontroller/microprocessor equipped with WiFi and Cloud storage. When the sensors are implemented, they measure the corresponding characteristics and transmit time-stamped live data to the cloud server. These sensors work together and provide wholesome data to the analyst. For the recommending system, the SVM and Decision Tree algorithm is proposed to get the crop suitable for the given soil data and helps to enhance the growth using an optimized farming process.

关键词： Temperature sensors Support vector machines Cloud computing Soil Sensor phenomena and characterization Agriculture Sensor systems

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Access Control Model for Google Cloud IoT

Access Control Model for Google Cloud IoT

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and smart computing (HPSC) and intelligent data and Security (IDS)

作者： Deepti Gupta Smriti Bhatt Maanak Gupta Olumide Kayode Ali Saman Tosun Dept. of Computer Science University of Texas at San Antonio San Antonio Texas USA Dept. of Computing and Cyber Security Texas A & M University-San Antonio San Antonio Texas USA Dept. of Computer Science Tennessee Technological University Cookeville Tennessee USA

ISBN: (数字)9781728168739

ISBN: (纸本)9781728168746

The prevalence of Internet of Things (IoT) is growing rapidly with increasing influence on our daily life. IoT has received considerable attention in both academia and industry recently and there has been significant studies on security and privacy aspects of IoT and cyber-physical systems. Researchers in academia have developed novel access control models and mechanisms for IoT. On the industry side, companies including cloud services providers like Microsoft, Amazon and Google have deployed Cloud-Enabled IoT Platforms to ensure wide scale adoption. However, there is a lack of consensus between formal IoT access control models proposed in the literature and real-world Cloud-Enabled IoT deployments. To bridge this gap, in this paper, we first study and develop a formal access control model of Google Cloud Platform, referred to as GCPAC model. We then extend the GCPAC model into a formal Google Cloud Platform IoT Access Control (GCP-IoTAC) model with IoT specific components. We demonstrate two dominant IoT scenarios - E-health use case, and smart home use case, using the GCP-IoT. We also highlight some of the limitations of the current access control capabilities of GCP-IoT and propose attributebased extensions for fine-grained access control in GCP and its IoT platform. We envision that this contribution will help achieving consensus among formal IoT access control models and real-world Cloud-Enabled IoT Platforms.

关键词： Google Cloud Platform Google Cloud IoT Core Internet of Things Attributes Virtual Devices Role-Based Access Control Attribute-Based Access Control. access control Internet of things Google Platform Clouds good clinical practice Academic Settings

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IoT-Enhanced Innovations in Drug Delivery Systems for Targeted Therapy in Autoimmune Disorders

IoT-Enhanced Innovations in Drug Delivery Systems for Target...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： V Bhuvaneshwari R Sadaieswaran P Malathi N Legapriyadharshini T Thirumalaikumari S R Raja Department of Computer Science and Engineering Acharya College of Engineering Achariyapuram Puducherry India Department of Computer Applications Tagore College of arts and science Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The growing number of autoimmune diseases necessitates the needs novel drug delivery system alternatives to improve targeted therapy. The study deals with the immediate requirement for improvements by investigating the limitations of existing systems, highlighting their limitations in precision, patient adherence, and real-time monitoring. The proposed system combines Internet of Things (IoT) technology into medicine distribution, providing an extensive structure that makes use of smart devices, sensors, and networking solutions. The integration attempts to improve patient participation and treatment efficacy by ensuring tailored regimens. The study demonstrates promising discoveries in optimizing therapeutic interventions, cost-effectiveness, and data-driven insights through an in-depth evaluation of case studies and successful implementations. The proposed IoT-enhanced drug delivery system not only addresses the limitations of existing systems but also represents an evolution for healthcare professionals and pharmaceutical businesses. Patient privacy and data security are essential for ethical and regulatory reasons. The study's results and analysis highlight the proposed system's potential for overcoming limitations and enhancing targeted therapy for autoimmune disorders, setting the groundwork for a future in which IoT seamlessly interacts with drug delivery systems to transform patient care.

关键词： data privacy data security Transforms Drug delivery Sensor systems Real-time systems Autoimmune diseases Internet of Things smart devices intelligent sensors

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A Review and Threat Analysis of Machine Learning in Relation to Fog and Edge computing

A Review and Threat Analysis of Machine Learning in Relation...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Chinmoy Bharadwaj Atowar Ul Islam Sippee Bharadwaj Priyanka Sarma Department of Computer Science University of Science and Technology Meghalaya India Department of Business Analytics International School of Business and Research Bengaluru India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Traditional centralized cloud computing is having considerable issues due to the rapid proliferation of mobile internet and Internet of Things applications. These issues include data transfer delays, low efficiency in exploiting available spectrum, and connectivity that is inflexible based on machine type. Driven by the need to overcome these challenges, a new technology is prompting a change in the role of centralized cloud computing to edge devices within networks. Driven by the need to overcome these challenges, a revolutionary technology is prompting a change in the role of centralized cloud computing to edge devices within the networks. Edge computing systems have emerged from a variety of sources, with the goal of reducing latency, improving SE (security and efficiency), and enabling wide connectivity among devices. Given the broad growth and advancement of cloud technologies, the existing cloud computing model is insufficient to address the critical needs for providing services to applications such as smart grid, healthcare systems, and augmented reality. These specifications include low latency, mobility support, and context awareness. Fog and edge computing have developed as technologies that meet these requirements while providing great performance and dependability. Machine learning entails detecting many characteristics such as automated procedures, increased decision support, and diagnostic capabilities within operational settings. This study exposes previous research that focuses on diverse applications of machine learning, identifying potential hazards, and developing solutions for the edge paradigm as a whole. It also underlines the outstanding issues and challenges that may remain in the coming decades.

关键词： Cloud computing Image edge detection Computational modeling Machine learning Medical services Streaming media Real-time systems smart grids Internet of Things Edge computing

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Analysis of future trend and opportunity in the emerging 5G IoT scenario

Analysis of future trend and opportunity in the emerging 5G ...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ramakrishnan Raman Joel Alanya-Beltran Rajesh Singh Snehal Trivedi Biju G. Pillai M. Kalyan Chakravarthi Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Universidad Tecnológica del Perú Uttaranchal Institute of Technology Uttaranchal University India Department of Mech Parul University Department of IT & Business Analytics Sri Balaji University Pune (SBUP) School of Electronics Engineering VIT-AP University Amaravathi India

Nowadays, there are many concepts focused on the Iot devices (IoT), such as virtual reality, elevated streaming media, Automated vehicles, intelligent ecosystems, digital healthcare, etc. The requirements for these applications include high throughput, low latency, increased capacity, higher data rates, and enormous bandwidth. The Internet of Things (IoT) is revolutionary because it allows for interoperability between previously incompatible networks, which has led to a dramatic shift in the way people interact with and perceive the (HetNets). The ultimate goal of IoT is to bring plug-and-play technology to market, providing users with versatility, remote access control, and ease of use. The IoT technology is presented in this article from a high-level perspective, including its statistical and architectural trends, application cases, difficulties, and hopes for the future. A thorough and in-depth assessment of the developing 5G-IoT situation is also included in the study. Cellular networks of for the mainstream use of IoT (internet of things, the networks(5G) offers important advancement of technology. This consist of network function transformation (NFV), operating system smart sensor monitoring (SD-WSN), massive-MIMO (M-MIMO), coordinate multiple access processing (CoMP), equipment (D2D) interactions, cellular technologies, several (MIMO), massive-MIMO, and cognitive radios (CRs). This article gives a thorough assessment of these major enabling capabilities. The last section of the article examines difficulties with 5G-IoT deployment caused by high data rates that need edge computing on IoT devices and platforms that are located in the cloud.

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