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A Recent Review on IoT Based Techniques and Applications

A Recent Review on IoT Based Techniques and Applications

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Sustainable computing and Integrated Communication in Changing Landscape of AI (ICSCAI), international conference on

作者： Arjun Singh Sandeep Kumar Renu Bahuguna Mahesh Kumar Singh Nikhil Raj Department Of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida India Department Of Electronics and Communication Engineering G.L. BAJAJ Institute of Technology & Management Greater Noida India Department Of Cyber Security COER University Roorkee India Department Of Computer Science and Engineering Dronacharya Group of Institutions Greater Noida India Department Of computer Science and Engineering Greater Noida Institute of Technology Greater Noida India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

IoT known as Internet of Things is an advanced mechanics which has changed the way humans' smart devices, smart things, data, and information are connected throughout the world. IoT is a single concept of embedding everything. IoT evolution is yet in its premature steps, and there is a slew of interconnected hurdles to overcome. The IoT has the potential to boost the globe's accessibility, probity, availability, scalability, secrecy, and interoperability. Although, numerous challenges and concerns must be overcome before the IoT's full promise can be realized. Various components of IoT data are recognized and defined, including data sources, data collection, data processing, and transmitting devices. In terms of analytical and substantial data management, as well as transmission networks, the challenges that arise from the need to manage large amounts of heterogeneous data over their systems are mentioned. Sensors can be placed in isolated or far, huge, low-population localities in some applications, while sensors are used to monitor tight locations in others. Clustering the detectors lattice's nodes in multiple clusters is fundamentally beneficial clearly for scalability grounds, & it can be used to create conservation or utilization schedules that can significantly extend the lattice's life. Current study compares and contrasts common and advanced clustering schemes, as well as size, type of data collected, and performance as a function of non-uniformity and noise level. A combined met heuristic approach, a combined metaheuristic approach, WOA combined with SA, is also used in the proposed work.

关键词： Cloud computing Scalability soft sensors Metaheuristics Detectors Whale optimization algorithms Internet of Things Smart devices Monitoring Noise level

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4th international Workshop on Interpretability of Machine Intelligence in Medical Image computing, iMIMIC 2020 and 1st international Workshop on Topological data Analysis and Its Applications for Medical data, TDA4Medicaldata 2021 held in conjunction with 24th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2021

4th International Workshop on Interpretability of Machine In...

引用

4th international Workshop on Interpretability of Machine Intelligence in Medical Image computing, iMIMIC 2020 and 1st international Workshop on Topological data Analysis and Its Applications for Medical data, TDA4Medicaldata 2021 held in conjunction with 24th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2021

ISBN: (纸本)9783030874438

The proceedings contain 12 papers. The special focus in this conference is on Interpretability of Machine Intelligence in Medical Image computing. The topics include: statistical Modeling of Pulmonary Vasculatures with Topological Priors in CT Volumes;topological Detection of Alzheimer’s Disease Using Betti Curves;soft Attention Improves Skin Cancer Classification Performance;Deep Grading Based on Collective Artificial Intelligence for AD Diagnosis and Prognosis;this Explains That: Congruent Image–Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks;visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions;the Effect of the Loss on Generalization: Empirical study on Synthetic Lung Nodule data;voxel-Level Importance Maps for Interpretable Brain Age Estimation;lattice Paths for Persistent Diagrams.

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Towards Prediction Model for IoT-Wireless Sensor Network Packet Transmission Using soft computing Technique 1

Towards Prediction Model for IoT-Wireless Sensor Network Pac...

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1st international conference on Multidisciplinary Engineering and Applied science, ICMEAS 2021

作者： Agajo, James Thomas, Sadiq Gafai, Najashi Lukman, Ajao Adewale Ali Obadiah, N. Okafor, K.C. Federal University of Technology Computer Engineering Department Minna Nigeria Nile Univerisity of Nigeria Computer Engineering Department Abuja Nigeria Baze University Electrical Electronics/Computer Engineering Department Abuja Nigeria Federal University of Technology Mechatronics Engineering Department Owerri Nigeria

ISBN: (纸本)9781665434935

Packet transmission is a function of signal strength and distance in a wireless sensor network (WSN). The interval between the source node and sink node is of high significance in a wireless sensor network. This work developed a soft computing model that relates distance, signal strength, packet received and packet transmitted. An experiment was carried out using the CC2430 module for data acquisition. A learning process is carried out using the neural network (NN) tool toolbox. The entire datasets for training, testing, and validation are 60%, 25%, and 15% respectively. Regression result from training, testing, and validation was realized with R yielding 0.99972, 0.99974 and 0.99954 respectively. The summation of R gave 0.99869 in the work. The significance of the R value is that when it tends towards 1, the result demonstrates the accuracy of the model. This means that when the output of the trained and the test data are compared, it shows closeness symmetry. The closeness depicts how well the model performs. At convergence, the result shows that the coefficient for transmitted packet, distance and signal strength are predicted as 0.9969, 1.10800, and 0.7 respectively. © 2021 IEEE.

关键词： Machine learning

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1st international conference on Computational sciences - Modelling, computing and soft computing, CSMCS 2020

1st International Conference on Computational Sciences - Mod...

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1st international conference on Computational sciences - Modelling, computing and soft computing, CSMCS 2020

ISBN: (纸本)9789811647710

The proceedings contain 21 papers. The special focus in this conference is on Computational sciences. The topics include: Solution of Variable-Order Space Fractional Bioheat Equation by Chebyshev Collocation Method;an Efficient Numerical Method for Singularly Perturbed Parabolic Problems with Non-smooth data;scheduling of Jobs on Computational Grids by Fuzzy Particle Swarm Optimization Algorithm Using Trapezoidal and Pentagonal Fuzzy Numbers;s-Metacompact Spaces;interval Valued Fuzzy Graph and Complement Number;fermatean Fuzzy soft Sets and Its Applications;a Fast computing Model for Despeckling Ultrasound Images;non-linear Convection in Couple stress Fluid with Non-classical Heat Conduction Under Magnetic Field Modulation;a Characterization for V4 -Vertex Magicness of Trees with Diameter 5;existence and Uniqueness of Time-Fractional Diffusion Equation on a Metric star Graph;data-Driven Regression-Based Compartmental Model to Identify the Dynamical Behavior of Dengue Incidences in Urban Colombo;ball Convergence of Multipoint Methods for Non-linear Systems;learning Numerical Viscosity Using Artificial Neural Regression Network;computational study of Some Numerical Methods for the Generalized Burgers-Huxley Equation;a Local Meshless Method for a Multi-term Time Fractional Non-linear Diffusion Equation;on Space-Fractional Diffusion Equations with Conformable Derivative;compact Finite Difference Method for Pricing European and American Options Under Jump-Diffusion Models;new Exact Solutions for Double Sine-Gordon Equation;Numerical study of Mixed Convection in Single and Double Lid Driven Cavity Using LBM.

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Changing Aspects: Examining Financial Market Forecasting via Textual Representation - A Critical Evaluation

Changing Aspects: Examining Financial Market Forecasting via...

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Advances in computing, Communication and Networking (ICAC2N), international conference on

作者： Ajay Kr. Varshney Ajit Kumar Mishra Surbhi Agarwal Ankit Garg Sanghamitra Das Swati Tripathi Utkal University India GL Bajaj Institute of Technology and Management Greater Noida Ajay Kumar Garg Institute of Management Ghaziabad Ajay Kumar Garg Institute of Management Ghaziabad India Institute of Management Studies Ghaziabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Social media news distribution has a big impact on the financial markets and causes noticeable adjustments. Deep learning-based natural language processing (NLP) methods have recently demonstrated remarkable potential in the analysis of these variations. However, comprehensive textual data must be integrated with financial market information in order to conduct an effective analysis of investor behavior. This research examines more than 150 publications on behavioral finance with an emphasis on combining NLP methods with market data analysis to support financial decision-making. This analysis, in contrast to others, focuses on computer science and artificial intelligence applications, emphasizing the integration of diverse data to assess investor behavior. The paper looks into different approaches to text representation, information retrieval, and sentiment analysis using a variety of data streams. It also looks at the routes that research in text mining and deep learning applications for financial markets is taking right now and in the future. These applications include forecasting, correlation analysis, and recommendation algorithms for a variety of asset classes, such as equities, Forex, and cryptocurrencies. The paper highlights the potential of integrating market data with advanced natural language processing (NLP) to augment our comprehension of investor behavior and streamline financial decision-making procedures by outlining several tactics and methodologies.

关键词： Text mining Computer science Economics Sentiment analysis Social networking (online) soft sensors Media Real-time systems Forecasting streams

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A Comprehensive study and Implementation of the Mock Interview Simulator with AI and Pose-Based Interaction

A Comprehensive Study and Implementation of the Mock Intervi...

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Cognitive, Green and Ubiquitous computing (IC-CGU), international conference on

作者： Balasaheb Jadhav Avadhut Sawant Arnav Shah Pranamya Vemula Abhijeet Waikar Srushti Yadav Artificial Intelligence and Data Science Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

The contemporary job market is increasingly competitive, placing a premium on a combination of technical prowess and essential soft skills. The ability to navigate job interviews effectively has emerged as a decisive factor in securing coveted positions. Recognizing this imperative, our contribution to the field is a pioneering solution - the Mock Interview Simulator. This simulator stands as a transformative tool in the landscape of job preparation, offering users an unprecedented advantage. In the current job market milieu, the Mock Interview Simulator redefines conventional preparation methods, providing a revolutionary approach for job seekers. This innovative platform leverages state-of-the-art technologies to empower users to tailor their interview experiences. The simulator introduces AI-driven interviewers, delivering a personalized and dynamic environment. This not only prepares candidates for a diverse range of interview scenarios but also imparts valuable insights into their performance. This paper delves into the intricate design and functionality of our Mock Interview Simulator, elucidating how cutting-edge technologies converge to create a realistic and highly beneficial interview preparation experience. By amalgamating speech recognition, text-to-speech synthesis, and the OpenAI GPT-3.5 Turbo API, the simulator crafts a lifelike interview setting. Additionally, posture detection using the Mediapipe framework adds a physical dimension, ensuring candidates not only articulate their responses effectively but also present themselves professionally.

关键词： Training Industries Navigation Green products Collaboration Speech recognition Ubiquitous computing

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An Algorithmic Approach Using Fuzzy soft Sets in Decision- Making for Patient-Specific Diagnosis

An Algorithmic Approach Using Fuzzy Soft Sets in Decision- M...

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Emerging Innovations and Advanced computing (INNOCOMP), international conference on

作者： Rashmi Singh Dechen Wangmo Keshav Kaushik Aryan Chaudhary Amity Institute of Applied Science Amity University Noida India Computer Science University of petroleum and energy studies Dehradun India Bio Tech Sphere Research India Kolkata India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

Fuzzy soft set (FSS) theory merges the strength of fuzzy sets (FS) and soft sets (SS) to effectively address uncertainty and vagueness in decision- making (DM) processes. It offers a systematic approach to address complex decision problems and is particularly valuable in today's data driven and uncertain world. There are numerous DM algorithms utilising FSSs. However, no prior work had employed multiple algorithms, provided algorithmic variation at each stage, and conducted a comparison analysis of the algorithms as an approach to a decision-making in a specific scenario. The current article is crafted to address these gaps. This study presents FSS theory-based algorithms for patient-disease diagnosis. In a hypothetical case study, a sequential foundational work had been set up by designating universal patient, symptom, and disease sets. The patient-symptom and symptom-disease relationships are represented using FSSs. The severity weights designated to symptoms are utilised to create a table detailing symptom severity. The sum-product composition is then applied to acquire the patient-disease diagnosis relation table, which is then defuzzified using the maximum membership principle (1st algorithm) to assign patients to the disease based on highest membership value. On the same patient-disease diagnosis output, the next algorithm introduced a modified approach in which three sets of values are constructed for each element. Triangular fuzzy numbers are used for defuzzification, and the disease suffered by each patient is indicated by highest average membership value in the row. The third algorithm incorporated disease severity and intensity-based weights. For defuzzification, the weighted average method is utilized, wherein the weighted average of membership values and allotted weights determines the disease suffered by each individual patient. The fourth algorithm identified the maximum membership value of each patient and used mean of maxima for defuzzification. We,

关键词： Technological innovation Fuzzy sets Accuracy Uncertainty Systematics Decision making Medical diagnosis

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Big data Architectures in a patient-driven healthcare system: Implications, Scrutiny and Analysis for future prospects

Big Data Architectures in a patient-driven healthcare system...

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Innovative Engineering sciences and Technological Research (ICIEstR), international conference on

作者： Kamran Ali Memon Imran Memon Farooque Kumbhar Abu Bakar Bala Naveed Iqbal Khurram Karim Qureshi Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Computer Science Shah Abdul Latif University Pakistan Electrical Engineering Department and Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

The term "big data" describes a significant amount of valuable information. Additionally, a sizable amount of big data related to public health comes from biological research. It is necessary to manage and analyse this data with care to get accurate information. There are several problems with every step of large data management that can only be fixed with sophisticated big data computing solutions. This is the reason health professionals need to be fully equipped with the tools necessary to efficiently collect and analyse big data to deliver appropriate solutions to promote public health. By creating new opportunities for contemporary health, proper big data management, analysis, and interpretation may completely transform the game. For this reason, several industries, including the health industry, are moving quickly to seize this chance to provide improved services and financial gains. Modern healthcare organizations have the potential to transform medical and personal medicine via the effective integration of big data and healthcare services. This article outlines many analytical paths that the patient-driven healthcare system might take, as seen from the viewpoints of different players. We have also examined several big data architectures in terms of their analytical influence, application fields, and underlying data sources and challenges in collecting the big data for healthcare systems.

关键词： Industries soft sensors Medical services Transforms Computer architecture Big data Supercomputers stakeholders Public healthcare Biomedical imaging

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Learning Numerical Viscosity Using Artificial Neural Regression Network 1st

Learning Numerical Viscosity Using Artificial Neural Regress...

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1st international conference on Computational sciences - Modelling, computing and soft computing, CSMCS 2020

作者： Dubey, Ritesh Kumar Gupta, Anupam Jayswal, Vikas Kumar Pandey, Prashant Kumar Research Institute and Department of Mathematics SRM Institute of Science and Technology Chennai India Blockapps AI Bangalore India Department of Mathematics SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9789811647710

Numerical diffusion plays an important role in deciding the characteristics of numerical schemes for flow problems containing discontinuities. In this work, we attempt to learn the numerical viscosity of underlying three-point shock-capturing schemes using non-linear regression neural network in a supervised learning paradigm. Details on network architecture, used data type and training are elaborated. Computed results by underlying schemes using exact numerical diffusion and predicted diffusion by trained network are given and compared. These results show that the network gives a good approximation of numerical diffusion and computed solutions are indistinguishable from the solution using exact numerical diffusion. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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Evaluating Reinforcement Learning Reward Functions for APT Detection in Industrial IoT Systems

Evaluating Reinforcement Learning Reward Functions for APT D...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Mohammad Alauthman Amjad Aldweesh Ahmad Al-Qerem Ibtisal Daoud Mouhammd Alkasassbeh Amjad Gawanmeh Department of Information Security University of Petra Jordan College of computing and IT Shaqra University Shaqra Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan Computer Science Department Princess Sumaya University for Technology Jordan University of Dubai United Arab Emirates

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Advanced Persistent Threats (APTs) pose significant challenges in Industrial Internet of Things (IIoT) environments, as they often employ stealthy and multi-stage attack strategies. The complexity of heterogeneous data sources, including provenance and network logs, further complicates detection. In this paper, we leverage the newly introduced CICAPT-IIoT2024 dataset as a benchmark for evaluating Intrusion Detection Systems (IDS). We focus on three Reinforcement Learning (RL) reward functions—Weighted Precision and Recall, Cost-Sensitive, and Balanced Accuracy—to determine their effectiveness in detecting APTs. Our experiments show that selecting the right reward function greatly influences detection trade-offs such as precision, recall, and false positive rates. The findings highlight the nuanced role of reward tuning and emphasize that a cost-sensitive approach more effectively penalizes false negatives in high-stakes IIoT scenarios, whereas balanced accuracy rewards maintain a more uniform trade-off between detection and false alarms. We discuss the implications of these outcomes for real-world IIoT deployments and propose directions for future research in improving RL-based IDS performance.

关键词： Accuracy soft sensors Intrusion detection Reinforcement learning Benchmark testing Complexity theory Tuning Computational intelligence Industrial Internet of Things

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