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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain Technology

Fog Computing based Privacy Preservation via Distributed Acc...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Ramya Thatikonda Priya Nandihal Priyanka M S Mahantesh Mathapati PhD in Information Technology University of the Cumberlands Williamsburg USA Dept of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore Dept. of AIML Nitte Meenakshi Institute of Technology Bengaluru Dept.of CSE Amruta Institute of Engineering and Management Sciences (AIeMS) Bengaluru

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices.

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Lowering the Number of Live-Page Copies on Solid State Drives through Trim-Assisted Space Allocation

Lowering the Number of Live-Page Copies on Solid State Drive...

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International SoC Design Conference, ISOCC

作者： Yong-Cheng Liaw Shuo-Han Chen Hsin-Yun Su Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung Uni. Hsinchu Taiwan Dept. of Computer Science Inst. of Artificial Intelligence Innovation National Yang Ming Chiao Tung Uni. Hsinchu Taiwan Dept. of Computer Science& Information Engineering National Taipei Uni.of Technology Taipei Taiwan

Due to the inherent limitations of solid state drives (SSDs), the garbage collection policy is a performance-critical design within the flash translation layer (FTL), designed to manage the internal limitations of SSDs and emulate the functionalities of conventional storage devices. To assist garbage collection, the concept of trim command has been designed and deployed by marking storage space as invalid and reducing the number of live-page copies during garbage collection. Nevertheless, conventional approaches usually utilize trim information during garbage collection, and the possibility of utilizing trim information during data allocation receives much less attention. Such an observation motivates this paper to propose a trim-assisted space allocation mechanism to lower the number of live-page copies from the source where the invalid space is produced. The experiments show promising results.

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Intelligent Heart Rate Classification with Adaptive Neuro-Fuzzy Inference System Approach

Intelligent Heart Rate Classification with Adaptive Neuro-Fu...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Banothu Seva Bhupchand Kumhar Jyoti Gupta Sharda Patel Shaik Khaja Mohiddin Basha Archi Jain Department of CSE MLR Institute of Technology Hyderabad Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Dept. of Mathematics Indore Institute of Science and Technology Indore Madhya Pradesh India Dept of CSE Gyan Ganga Institute of Technology & Sciences Jabalpur Dept.of CSE Narasaraopeta Engineering College GGITS Jabalpur MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The human heart is considered among the most crucial internal organs found in the body. An ECG signal is defined as the widespread name given to any electrical signal that is generated by the circulatory system*. By using the ECG signal, one can estimate anomalies present in a heart. This study is aimed at grouping the electrocardiogram (ECG) signals, and thereby using an adaptive neuro fuzzy inference system (ANFIS) algorithm. In this study, the ANFIS which is associated with the back propagation approach is used in order to perform the categorising task. ANFIS can be developed from the integration of fuzzy logic which is a qualitative technique with the neural network aspect in allowing adaptability. Attribute selection is done before the classification exercise is conducted. Four different types of ECG beats can be retrieved from the datasets available at PhysioBank. Classification of the cardiac signals is done employing four ANFIS classifiers. The fifth ANFIS decoder is used to get a higher discrimination ratio in order to classify the electrocardiograms accurately.

关键词： Fuzzy logic Wavelet transforms Adaptive systems Accuracy Sensitivity Electrocardiography Wavelet analysis Real-time systems Signal detection Testing

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Machine Learning Approach to Implement Mine Fire Predicting for Underground Coal Mines 2

Machine Learning Approach to Implement Mine Fire Predicting ...

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2nd Global Conference for Advancement in Technology, GCAT 2021

作者： Dogra, Surendra Kumar Jayanthu, Singam Samal, Abhaya Kumar Pramanik, Jitendra Pani, Subhendu Kumar National Institute of Technology Department of Mining Engineering Rourkela India Trident Academy of Technology Dept.of Comp. Science and Engineering Bhubaneswar India Centurion University of Technology and Management Odisha India Krupajal Computer Academy Bhubaneswar India

ISBN: (纸本)9780738132150

Mine fire due to auto oxidation of coal leading to spontaneous combustion of coal is the leading unsolved problem worldwide. Though the exact reasons of self-heating of coal are many and unclear, the accumulated heat generated in exothermic reaction due to continuous interaction of coal with the atmospheric oxygen is the major reason of triggering mine fire. Since the chemical composition of coal is not yet fully recognized, therefore, the exact mechanism of the coal-oxygen reaction in the atmosphere in presence of water, vapour and moisture is not completely established. However, irrespective of the reason, the outcome of spontaneous fires in the coal mines is the liberation of huge quantity of poisonous and combustible gases causing heavy loss of miners as well as mining machinery in addition to loss of valuable coal reserve;most disastrous is the fact that in most of the cases of outbreak of mine-fire, it is observed that occurrence of such events leaves rarely any scope of scape causing heavy loss of miners as well as machineries. Over the last decade, researchers all over world have shown tremendous interest in Machine Learning (ML) techniques - a branch of Artificial Intelligence (AI) that have demonstrated their success in providing trustworthy solutions in a variety of real-world applications to autonomously learn the input-output characteristics. Though there are variety of methods in use to predict spontaneous heating of coal leading to catastrophic mine-fire, in this study, we propose a novel approach of integrating machine learning technique with Graham's ratio data to predict the possibility of occurrence event of mine-fire from various environmental parameters responsible for triggering spontaneous heating of coal, so that an early warning system can be designed to take precautionary steps in advance. Our simulation study establishes validity of the proposed approach. © 2021 IEEE.

关键词： Coal

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Resource Allocation and Task Scheduling in Cloud Computing Using Improved Bat and Modified Social Group Optimization

Resource Allocation and Task Scheduling in Cloud Computing U...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Thirusubramanian Ganesan Muntather Almusawi K Sudhakar B R Sathishkumar Komuravelly Sudheer Kumar Cognizant Technology Solutions Texas USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept.of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE Sri Ramakrishna Engineering College Coimbatore India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Nowadays, Cloud computing is growing quickly with a maximum number of users and clients that are reaching from single users to huge business organizations. The cloud is facing significant challenges in managing large volumes of data and several cloud resources due to its rapid growth. Several optimization algorithms faced a challenge in scalability when working with more complicated scheduling scenarios. A combination of Resource Allocation and Task Scheduling using Improved Bat Optimization Algorithm (IBOA) by using dynamic weights with the speed update formula and increasing the random searchability and Modified Social Group Optimization (MSGO) by modifying the acquiring phase is introduced in this research. The developed IB and MSGO methods are simulated using a various simulation setup and the obtained results like response time, utilization of resources and energy consumption are compared against existing methods to measure the efficiency. For 100 tasks, the proposed IB and MSGO method achieved 32.5 watts of energy. The obtained results exposed that IB and MSGO method has better performance compared with the existing method namely Multi-Objective Task Scheduling Grey Wolf Optimization (MOTSGWO) in terms of energy consumption.

关键词： Cloud computing Energy consumption Social groups Processor scheduling Heuristic algorithms Scalability Dynamic scheduling Time measurement Resource management Optimization

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Comparison of Novel Machine Learning Algorithms for Predicting Chronic Renal Disease

Comparison of Novel Machine Learning Algorithms for Predicti...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： G B Hima Bindu Dinesh Valluru Harshita Vachhani M. Sunil Kumar D. Ganesh P. Neelima Department of of CSE School of Technology The Apollo University Chittoor Andhra Pradesh India Department of IT MLRITM Engineering College Hyderabad Telangana India Dept.of Computer Science Pratibha College of Commerce and Computer Studies Chinchwad Pune Maharshtra School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Department of CSE School of Engineering & Technology SPMVV Tiruapti

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

Chronic Renal Disease (CRD) is a serious sickness that affects the entire world and is the primary cause of mortality for many other illnesses. Due to the lack of visible symptoms at the beginning of CRD, people usually neglect the illness. Early CRD diagnosis enables individuals to receive prompt therapy to slow the course of their illness. Because it quickly and accurately identifies potentials, model-based machine learning can help doctors accomplish this aim. In this paper, an ML strategy for CRD forecasting. The UCI machine learning repository provided the CRD dataset, which has a high number of uncertain data values. To address unknown values or unknown data, KNN estimation was employed, which picks multiple instances that contain the most comparable measures to manage the undetermined data value for every insufficient sample. Missing data is prevalent in real therapy situations, therefore individuals may skip particular measurements for a variety of reasons. The technique is generated using five machine learning algorithms (SVM, Logistic Regression, k-nearest neighbor, Random Forest, and Neural Network). Random forest outscored the other models of machine learning regarding accuracy.

关键词： Support vector machines Machine learning algorithms Accuracy Medical treatment Nearest neighbor methods Predictive models Prediction algorithms Random forests Diseases Logistics

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Particle Swarm Optimization based Routing Method for Vehicular Ad-hoc Network 9

Particle Swarm Optimization based Routing Method for Vehicul...

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9th IEEE International Conference on Communication and Signal Processing, ICCSP 2020

作者： Yelure, Bhushan Sonavane, Shefali Walchand College of Engineering Dept.of Computer Science Engineering Sangli India Walchand College of Engineering Dept. of Information Technology Sangli India

ISBN: (纸本)9781728149882

Vehicular Ad-hoc Network (VANET) is a special subcategory of MANET and has a prominent role in the deployment of Intelligent Transportation System (ITS) applications. Most of the applications in the VANET achieve communication between vehicles and vehicles to road side unit through routing by using IEEE 802.11p standard. To make the routing feasible in the VANET is a challenge due to the fast movement of the vehicles, way of moving the vehicles and failure in the communication link. Swarm Intelligence (SI) methods are used in the process of routing due to similarity in the behavior of swarms and the routing. These techniques are used to achieve optimized solutions that ensure flexibility and robustness in the economical way. In this paper, PSOR routing protocol is proposed that uses the distance and speed of the vehicle to determine next forwarding vehicle. Relaying Quality of Service (QoS) of road scenarios is evaluated using Message delivery ratio (MDR), overhead, delay and throughput. The results are compared with other SI techniques such as AntHocNet and Adaptive QoS-based Routing for VANETs (AQRV). The simulation results related to PSOR are found better than AntHocNet and AQRV. © 2020 IEEE.

关键词： Quality of service

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Bi-Directional Gated Recurrent Unit Approach for Detecting and Classifying High Impedance Fault in Power Distribution

Bi-Directional Gated Recurrent Unit Approach for Detecting a...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： MD. Mujahid Irfan Supriya R P. Malleswara Reddy Haider Alabdeli Sasikumar Gurumoorthy Department of Electrical and Electronics Engineering SR University Warangal India Dept.of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of EEE GATES Institute of Technology (Autonomous) Gooty Ananthapuramu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In the recent days, detecting the High Impedance Faults (HIF) is an extremely difficult task because it is unpredictable, asymmetric, and nonlinear. Due to the size of the fault current is typically much lower than the normal load current, these faults are typically undetectable and impossible to isolate using traditional over-current approaches. Therefore, this research introduced a novel technique name called Bi-directional Gated Recurrent Unit (Bi-GRU) for power distribution-related HIF detection and isolation. The devised technique makes use of voltage as well as data from current sensors. In order to clarify the procedure, many types of algorithms were utilized during the detecting phase. For classification purposes, the algorithm Bi-GRU is applied on IEEE 13- distribution of node networks to test this method's ability to recognize and detect HIF with both damaged and unbroken wires. The suggested Bi-GRU provides fast and flawless output when troubleshooting high impedance issues. Bi-GRU considerably increases input processing and enhances classification accuracy. From the overall analysis, suggested Bi-GRU combines the forward and reverse GRUs in reverse order, which allows it to complete the operation more quickly and accurately than existing Morphological Fault Detector by achieving 95.63 % accuracy rate.

关键词： Fault detection Wires Power distribution Bidirectional control Voltage Logic gates Electrical fault detection

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Expression of Concern for: A Convenient Machine Learning Model for Cyber Security

Expression of Concern for: A Convenient Machine Learning Mod...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Thanuja N Deepak N R Dept of Computer Science & Engineering Bangalore Inistitute of Technology (VTU) Bangalore Karnataka India Dept.of Computer Science & Engineering HKBK College of Engineering (VTU) Bangalore Karnataka India

ISBN: (数字)9781665403603

ISBN: (纸本)9781665447751

In recent years, deep neural network approaches have been generally embraced for AI undertakings, including characterization. Nonetheless, they were demonstrated to be helpless against antagonistic assaults. This research work proposes a GAN based model, another system that utilizes the expressive ability of generative models to protect profound neural organizations against such assaults. Security assaults are getting progressively predominant as digital aggressors abuse framework vulnerabilities for monetary benefit. The digital interruptions imperil our gadgets constantly, they have numerous extreme results, for example, the unapproved divulgement of data, the altering, decimation, and expungement of information. Consequently, unsupervised and viable identification is required to react to these malevolent interruptions against systems and PCs. Many investigations have been finished with both measurable learning strategies and neural networks. Present day arranged frameworks are getting enormous measured and dynamic. Therefore, existing security models experience the ill effects of adaptability issue, where it gets infeasible to utilize them for present day arranged frameworks that contain hundreds and thousands of hosts and vulnerabilities. The objective of this examination is to build up a repeatable procedure to distinguish digital assaults that is quick, exact, and adaptable. The procedure ought to assess various information sources so as to increase a far-reaching image of client action over different frameworks. Client action designs experience typical changes for the duration of the day, and frequently those examples contrast from designs that happen on ends of the week. The model is required to separate between typical changes and anomalous client exercises. A profound learning calculation is utilized to prep

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Bidirectional Laser Based OCDMA-VLC System For Smart Home Applications

Bidirectional Laser Based OCDMA-VLC System For Smart Home Ap...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vivek Arya Meet Kumari Rahul Chauhan Neha Sharma Dept. of ECE FET Gurukula Kangri (Deemed to be University) Haridwar India Dept.of ECE Chandigarh University Gharuan Mohali India Computer Science and Engineering Graphic Era Hill University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

A bidirectional symmetric 3 x35Gbps optical code division multiple access (OCDMA) with visible light communication (VLC) system is presented. The performance is measured using multi-diagonal (MD) code for different users with channel impairments. The simulation results indicate that faithful VLC range of 45m is achieved successfully. Also, the system employing MD code can supports 80 numbers of end users in both downlink as well as upstream transmission. Also, the system shows that best performance over other existing ones for smart home applications.

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