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IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Chang, Chin-Chen Chan, Chi-Shiang Wang, Zhi-Hui Li, Ming-Chu Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan Department of Information Science and Applications Asia University Wufeng Taiwan School of Software Dalian University of Technology Dalian China

ISBN: (纸本)9780769537627

In this paper, we propose an information hiding scheme to embed secret information into the compression code of an index table. First, the codewords neighboring the current processed index along with their closest codewords form a state codebook. Two indices of the state codebook are reserved as indicators. Once the current processed index satisfies some pre-defined conditions, the current processed index can be encoded using the state codebook, and the secret bit can be embedded in the compression code. On the other hand, the reserved indices are taken as indicators to denote that the current processed index is encoded by the VQ mechanism. On the basis of our experimental results, the bit rates of the proposed method are lower than those of related works. The improvements in bit rates increase when the quantity of the embedded bits increases. © 2009 IEEE.

关键词： Steganography

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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

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SN computer Science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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Cement Strength Prediction using Regression Techniques 10

Cement Strength Prediction using Regression Techniques

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Sharan, Bhagwati Goyal, Mayank Kumar Balde, Amadou Mouctar Greynoon, Ameen Mohamad Ahmad Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Amaravati India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Cements

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The classification of medical and botanical data through majority voting using artificial neural network

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3271-3283页

作者： Tripathi, Kshitij Khan, Fayaz Ahmed Khanday, Akib Mohi Ud Din Nisa, Khair U. L. Department of Computer Applications The Maharaja SayajiRao University of Baroda Vadodara 390002 India Department of Information Technology S.P. College Cluster University Srinagar 190001 India Department of Computer Science and Software Engineering-CIT United Arab Emirates University Al Ain United Arab Emirates

Data classification has many approaches in data mining and machine learning. The artificial neural network (ANN) is applied to classify the data that might belong to various domains like chemical, botanical, medical, spatial, textual, and image. In this work, an ANN technique is applied to the 7 Life sciences (botanical and medical) data sets extracted from public data repositories. Various optimization approaches like exhaustive validation, cross-validation, and multiple seeding are used to discover the most optimized networks for the given datasets. Finally, voting predicts the class where the whole dataset is used as a test set instead of folds. The results obtained by the proposed approach outperform other approaches on all the datasets. Cleveland’s heart, Statlog heart, Dermatology, Hepatitis, Seeds, Abalone and Vertebral Column data sets (all of UCI) after applying the voting showed the accuracy of 94.61%, 93.7%, 99.73%, 96.77%, 99.05%, 89.37% and 90.32% respectively. In the future deep neural network may be used to improve the results. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Artificial neural network Classification Optimization Validation

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Resource optimization using virtual machine swapping

Proceedings of IEEE International Conference on Circuit, Pow...

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Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2013 2013年 1231-1234页

作者： Suchithra, R. Rajkumar, N. Department of Computer Applications SET Jain University Bangalore India Department of Software Engineering Ramakrishna College of Engineering Coimbatore India

ISBN: (纸本)9781467349222

While Virtualization has gained popularity in recent years;data centers reap the benefits of resource utilization through virtual machine migration. We present a virtual machine placement algorithm in a resource constrained *** first part of the algorithm deals with virtual machine migration. In the second part we implement a virtual machine swapping model that improves the complexity of finding the right neighborhood for swapping virtual machines that are running between two servers. The aim of our approach is to increase the resource utilization and reduce the resource wastage. We have evaluated the applicability of our models through experimentation and proved that our algorithm leads to efficient resource allocation. © 2013 IEEE.

关键词： Virtualization

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Effect of noise on Julia sets genereted by logistic map

Effect of noise on Julia sets genereted by logistic map

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2nd International Conference on computer and Automation engineering, ICCAE 2010

作者： Rani, Mamta Agarwal, Rashi Faculty of Computer Systems and Software Engineering University Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Darul Makmur Malaysia Department of Computer Applications IEC College of Engineering and Technology Knowledge Park I Greater Noida 201306 India

ISBN: (纸本)9781424455850

Julia sets are considered one of the most attractive fractals and have wide range of applications in science and engineering. Jrgyris et al. [4] have studied the ef fect of dynamic noise on Julia sets and concluded that Julia sets are stable for noises of low strength, and a small increment in the strength of noise may cause deterioration in configuration of the Julia sets. Rani and Jgarwal [16] have generate d Julia sets from complex logistic map. We study the effect of dynamic noise on the Julia sets generated by complex logistic map. ©2010 IEEE.

关键词： Set theory

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An automated seamless mosaicing system of multi-charge coupled devices of panchromatic data

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Journal of the Indian Society of Remote Sensing 2004年第1期32卷 103-111页

作者： Ramakrishnan, R. Manthira Moorthi, S. Padmanabhan, N. Gupta, P. Data Products Software Division Signal and Image Processing Group Space Applications Centre (ISRO) Ahmedabad-380 015 India Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur 208 016 India

Panchromatic data of pixel resolution 5.8 m obtained from IRS-IC and IRS-ID satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a combination of 3 CCD line sensors, each with 4096 pixels. Each CCD-line sensor with different imaging times causes geometric problems of mosaicing three strips data together. In this paper, we propose the details of the design elements of system that caters to the need for accurate and automatic multi strip image registration without any second resampling of the data. The systematic geometric correction grid mapping is improved to facilitate accurate mosaicing by automatic image registration task that makes use of the overlap data within image strips and image registration is achieved up to sub-pixel level.

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Node verification to join the cloud environment using third party verification server

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2017年第4期11卷 55-65页

作者： Aljammal, Ashraf H. Bani-Salameh, Hani Alsarhan, Ayoub Kharabsheh, Mohammad Obiedat, Mamoon Department of Computer Science and its Applications Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan

Currently, cloud computing is facing different types of threats whether from inside or outside its environment. This may cause cloud to be crashed or at least unable to provide services to the requests made by clients. In this paper, a new technique is proposed to make sure that the new node which asks to join the cloud is not composing a threat on the cloud environment. Our new technique checks the node before it will be guaranteed to join the cloud whether it runs malwares or software that could be used to launch an attack. In this way the cloud will allow only the clean node to join it, eliminating the risk of some types of threats that could be caused by infected nodes.

关键词： Network security

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Ensemble Method for User Activity classification in Ambient Assisted Living

Ensemble Method for User Activity classification in Ambient ...

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Madhan Kumar, G.S. Shiva Prakash, S.P. Krinkin, Kirill JSS Science and Technology University Mysuru Mysuru India JSS Science and Technology University Mysuru Department of Information Science and Engineering Mysuru India Saint Petersburg Electrotechnical University LETI Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9781665406703

Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%. © 2022 IEEE.

关键词： Decision trees

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Semantic Rules for Service Discovery in Social Internet of Things 4

Semantic Rules for Service Discovery in Social Internet of T...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Mohana, S.D. Prakash, S.P. Shiva Krinkin, Kirill Department of IS&E JSS Science and Technology University Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

ISBN: (纸本)9781665401180

World is moving towards preceding generation Alpha that believes in socializing the objects or devices within the network known as Social Internet of Things(SIoT). In SIoT the services are discovered based on the user preferences by establishing relationship between the objects and user. Thus object intelligent model is necessary to efficiently discover the services based on the context and semantic relationships between the users and objects. SIoT posses certain challenges like service discovery, compositions, etc. To address these challenges, in this work a semantic rules are proposed to discover services and it is evaluated using various machine learning algorithms on SIoT data. Hence, this work focusses on implementing and evaluating the various machine learning algorithms on SIoT device data considering health as an application. The performance is evaluated with dataset ratio of 60:40, 70:30, 80:20 and 90:10. The machine learning classifier techniques like Decision Tree, Naive Bayes, K-nearest neighbour and Artificial Neural Network classifiers gives the average accuracy 100%, 99%, 100%and 100% respectively. The results show that ANN gives best results compared to other algorithms. © 2022 IEEE

关键词： Internet of things

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