Volume-9 Issue-4S2, March 2020 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

D10010394S220

IJITEE Journal — Sat, 19 Aug 2023 06:39:00 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. Challenges of Industry and their Solutions, Communications #Advanced Manufacturing Technologies #Artificial Intelligence #Autonomous Robots #Augmented Reality #Big Data Analytics and Business Intelligence #Cyber Physical Systems (CPS) #Digital Clone or Simulation #Industrial Internet of Things (IIoT) #Manufacturing IoT #Plant Cyber Security #Smart Solutions – Wearable Sensors and Smart Glasses #System Integration #Small Batch Manufacturing #Visual Analytics #Virtual Reality #3D Printing # Internet of Things (IoT) #Internet of Things (IoT) & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT #Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Block Chain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for 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Analyzing Global Feature for Duplicate Video Retrieval using CNN and PCA
N. Gayathri¹, K. Mahesh²

">¹N. Gayathri, Ph.D. Scholar, Department of Computer Applications, Alagappa University, Karaikudi (Tamil Nadu), India.

²Dr. K. Mahesh, Professor, Department of Computer Applications, Alagappa University, Karaikudi (Tamil Nadu), India.

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: At present, Duplicate video retrieval has attained the interest of researchers because of the vast amount of online videos. This video retrieval has extensive applications like online video monitoring, copyright protection, and automatic video tagging. Some local features constitute primary building blocks in this video retrieval algorithms; with this, most researchers use local information for feature representation. Moreover, this local knowledge-based representation eliminates more prominent information regarding global distribution. However, the discriminative power of local descriptors is diminished by feature quantifiers. The ultimate goal is to use universal features to categorize similar keyframes into the same class that is essential to enhance video retrieval performance. Here, CNN features acquire global geometric distribution of video, from which discrete features are considered for computation. Discretization is performed with principal component analysis. These kinds of features maintain geometry transformation with reduced noise. Next, an integration strategy known as k-NN is used to merge these features with global VR features for enhancing recognition accuracy. Experimentation has been carried out with available datasets to show that the anticipated model outperforms existing approaches in VR applications.

Keywords: Video Retrieval, Video Tagging, CNN, Global Geometric Features, Discretization, k-NN.
Scope of the Article: Knowledge Representation and Retrievals

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D10200394S220

IJITEE Journal — Sat, 19 Aug 2023 06:32:34 +0000

A Conceptual Framework for Intelligent Power Distribution Transformers
K. Somasena Reddy

">K. Somasena Reddy, Assistant Professor Ad Hoc, Department of CSE, JNTUACEA, Ananthapuramu (Andhra Pradesh), India.

Abstract: With the agenda of developing smart cities there is huge demand for continuous power supply. Power distribution transformers play avital role in providing a reliable power supply. Failure of a transformer will lead to interruptions in power supply. Many parameters lead to transformer failures. Health monitoring of transformer using IoT technology may help take proactive maintenance steps instead of reactive maintenance. When we combine IoT with AI it will more effective and IoT devices will take decision on their own. This paper presents a conceptual framework of this concept which makes the IoT devices in the transformers to make real-time decisions with the use of AI.

Keywords: Smart Cities, Uninterrupted Power Supply, Distribution Transformer IoT, AI.
Scope of the Article: Patterns and Frameworks

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D10190394S220

IJITEE Journal — Sat, 19 Aug 2023 06:26:53 +0000

Optimizing Consumption of Energy in Multicast Model in -Constrained Mobile Wireless Networks
Yonas Melaku Waktola¹, Kokeb Abera²

¹Yonas Melaku Waktola, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.

²Kokeb Abera Workneh, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.

Abstract: This paper addresses the issue of saving energy consumption in a multicast model in mobile wireless networks with time limit, where source information must be sent to all destinations within an self imposed time limit. Many of the existing works focuses on getting transmission schemes with minimal energy transmission, bypassing the use of energy on the receiver side. Therefore, in this ConMap paper, a new and novel framework is proposed for an efficient design of the scheme employed in transmission that increases the efficiency of the transmission and reception energy together. Thus, the issue of designing a scheme of minimal energy transmission, DeMEM, will be given as one of combination in optimization and will show approximately ratio of polynomial time algorithm of DeMEM can not be better than (1/4 ) ln k. In order to give very efficient approximation schemes, the proposed model ConMap converts DeMEM into an equivalent oriented direct shaft issue by creating helpdesk of graphic devices to capture the use of energy, maps the calculated tree into a scheme of transmission I There are three advantages of ConMap: 1) General: ConMap shows a Clear application to a wide range of power models. 2) Flexibility:Any Steiner tree problem algorithm can be incorporated into our ConMap system to obtain spe cific guarantees and performance complexity. Efficiency: The ConMap model retains the approximation ratio of the built-in Steiner tree algorithm, which will only tolerate a minor overload. These three features are empirically validated and in contrast with an exact brute force algorithm, ConMap also provides near-optimal transmission schemes. In this paper, energy transmission and reception are jointly considered in the design of multicast transmission schemes.

Keywords: Multicast, Mobile Wireless Networks, ConMap.
Scope of the Article: Mobile & Broadband Wireless Internet

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D10140394S220

IJITEE Journal — Sat, 19 Aug 2023 06:21:11 +0000

General Regression Neural Network Versus Back Propagation Neural Network for Prediction of Reheater and Super Heater Sprays in Thermal Power Plants
K.S. Madhavan

K.S Madhavan, Sr. DGM SBE, BHEL Corporate R&D, Vikasnagar, Hyderabad (Telangana), India.

Abstract: Neural Network models are used for Reheater and Super heater spray prediction in Thermal Power Plants. This paper makes a comparative study of the General Regression Neural Network (GRNN) model versus the Back propagation Neural Network (BPNN) model for the quality and accuracy of prediction of Reheater and Super heater Sprays in Thermal Power Plants. It proves that GRNN is better and gives more stable prediction within range; the glitches between the predicted and actual values being less in number as well as value.

Keywords: Back Propagation Neural Network, General Regression Neural Network, Reheater Spray, Super Heater Spray.
Scope of the Article: Network Architectures

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D10080394S220

IJITEE Journal — Sat, 19 Aug 2023 06:15:34 +0000

Applications of Multi-Label Classification
Ayesha Mariyam¹, SK Althaf Hussain Basha², S Vishwanadha Raju³

">¹Ayesha Mariyam, Department of CSE, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.

²SK Althaf Hussain Basha, Department of CSE, A1 Global Institute of Engineering and Technology, Markapur (Andhra Pradesh), India.

³S Vishwanadha Raju, Department of CSE, Jawaharlal Nehru Technological University Hyderabad (Telangana), India.

Abstract: The absence of labels and the bad quality of data is a prevailing challenge in numerous data mining and machine learning problems. The performance of a model is limited by available data samples with few labels for training. These problems are ultra-critical in multi-label classification, which usually needs clean data. Multi-label classification is a challenging research problem that emerges in several applications such as multi-object recognition, text categorization, music categorization and image classification. This paper presents a literature review on multi-label classification, various evaluation metrics used for analyzing performance and research hchallenges.

Keywords: Multi-label Classification, Deep Learning, Machine Learning, Feature Extraction.
Scope of the Article: Classification

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D10040394S220

IJITEE Journal — Sat, 19 Aug 2023 06:07:36 +0000

Deep Learning based Effective Steganalysis
John Babu G¹, Sridevi Rangu²

¹Mr. John Babu, Ph.D, Department of Computer Science & Engineering, JNTUH College of Engineering, Hyderabad (Telangana), India.

²Dr. Sridevi Rangu, Professor and Head, Department of Computer Science & Engineering, JNTUH College of Engineering, Hyderabad (Telangana), India.

Abstract: There is an evident paradigm shift in steganalysis techniques with discovery of deep learning networks. As steganalysis is a classification task, it is done by machine learning classifiers and ensembles of them. But with the proliferation of deep learning and Convolutional Neural Networks in many areas, the performance of steganalysis techniques have jumped up to a another high, because of the application of Convolutional Neural Networks. The traditional steganalysis techniques consists two important steps, i.e., feature extraction and classification; where as deep learning networks learn the features automatically, eliminating the need of extraction of handcrafted features. Because of this feature CNNs were highly successful in image recognition and image classification techniques. In addition to that, feature extraction and classification are combined together in deep learning hence classification would be more effective because of the learning of the features which are really important for classification. But in Steganalysis the task is to detect very subtle and weak noise created by the hidden data with steganography techniques. We have designed a deep CNN architecture customized for steganalysis task based on existing residual neural networks frame. We have introduced a descriptor to capture the inter pixel dependencies and which acts as an indicator for weightage of a particular feature maps. Thus the classifier can give more weightage to effective feature maps instead of treating all the feature maps equally. We have also used a gating mechanism by using sigmoid function after nonlinear activation function sandwiched between two fully connected layers. This enhancement to the existing deep residual neural networks has given better results in terms of error detection rate compared to the other deep learning based steganalysis techniques.

Keywords: Classification, Convolutional Neural Networks, Deep Learning, Steganalysis.
Scope of the Article: Deep Learning

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D10180394S220

IJITEE Journal — Sat, 19 Aug 2023 06:00:39 +0000

Issues in Design and Development of Reliable Configurable Distributed Checkout and Launch System for Mission Critical Aero-Space Flight Vehicles
Shivpal Singh¹, Bhogendra Rao PVRR²

¹Shivpal Singh, M.Tech, Department of Computer Science, IIT, Madras India.

²Dr. PVRR Bhogendra Rao, Ph.D, Department of Computer Science, JNTU, Hyderabad (Telangana), India.

Abstract: Role of Configurable Distributed Checkout and Launch System (CDCLS) is pivotal in carrying out quick health checks and launching of Aerospace Flight Vehicles. Configurable Distributed Architecture provides flexibility for connecting nodes and scaling Distributed System. Different configurations can be derived from the Master Configuration. Since, Ultra high reliability and infallible performance of the CDCLS is of paramount importance, Safety criticality and Mission criticality analysis needs to be carried out for determination of mission critical parameters. These critical parameters need to be addressed by required fault tolerant architecture, which can be implemented in Hardware and Software for achieving system reliability objective (Say, 0.99).

Keywords: Distributed System, Launch System, Reliability, Fault Tolerance, Automated Testing, System Validation.
Scope of the Article: Optical Link Design

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D10170394S220

IJITEE Journal — Sat, 19 Aug 2023 05:53:28 +0000

Secure Data Aggregation Scheme for IoT Applications in Cloud
Mallareddy A¹, Sridevi R², Prasad Ch G V N³

¹Mallareddy A, Associate Professor, CVR College of Engineering and Research Scholar, JNTUH Hyderabad (Telangana), India.

²Dr. Sridevi R, Professor and HOD, Department of CSE, JNTUH College of Engineering, Hyderabad (Telangana), India.

³Dr. Prasad Ch G V N, Professor and HOD, Department of CSE, Sri Indu College of Engg & Tech., Hyderabad (Telangana), India.

Abstract: While Internet of Things (IoT) technology comprises of nodes that are self-configuring and intelligent which are interconnected in a dynamic network, utilization of shared resources has been revolutionized by the cloud computing effectively reducing the cost over head among the cloud users. The major concerns of IoT infrastructure are reliability, performance, security and privacy. Cloud computing is popular for its unlimited storage and processing power. Cloud computing is much more matured with the capability to resolve most of the issues in IoT technology. A suitable way to address most of the issues in IoT technology is by integrating IoT paradigm into the Cloud technology. In this regard, we propose a methodology of applying our EPAS scheme for IoT applications. In our previous work[2] , we have proposed an Enhanced Privacy preserving gene based data Aggregation Scheme (EPAS) for private data transmission and storage by utilizing Enhanced P-Gene erasable data hiding approach. Enhanced P-Gene scheme ensures secure transmission and storage of private data by relying on a data aggregation scheme fully dependent on erasable data hiding technique. In the current work we analyse the applicability of the EPAS scheme for IoT applications. Experimental results show the suitability of the proposed scheme for application involving numeric data and also demonstrates performance improvement with existing proposals for data aggregation in cloud.

Keywords: Cloud Computing, IoT, Data Hiding, Data Security, Data Aggregation, Enhanced P-Gene.
Scope of the Article: Cloud Computing

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D10160394S220

IJITEE Journal — Sat, 19 Aug 2023 05:24:18 +0000

Efficient Software Architecture Pattern for Accelerator Based Computing
Bhogendra Rao PVRR

Dr. PVRR Bhogendra Rao, Ph.D, JNTU, Hyderabad (Telangana), India.

Abstract: Graphics Accelerators are increasingly used for general purpose high performance computing applications as they provide a low cost solution to high performance computing requirements. Intel also came out with a performance accelerator that offers a similar solution. However, the existing application software needs to be restructured to suit to the accelerator paradigm with a suitable software architecture pattern. In the present work, master-slave architecture is employed to convert CFD grid free Euler solvers in CUDA for GPGPU computing. The performance obtained using master-slave architecture for GPGPU computing is compared with that of sequential computing results.

Keywords: Software Architectures, Architecture Patterns, Parallel and Distributed Computing, GPGPU, CUDA Computing.
Scope of the Article: Software Engineering & Its Applications

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D10150394S220

IJITEE Journal — Sat, 19 Aug 2023 05:16:30 +0000

A Mining Frame Work of CO-PO Attainment using Deep Learning Techniques
P Vasanth Sena¹, P Sammulal², Suresh Pabboju³, D L Srinivasa Reddy⁴

¹P.Vasanth Sena, Assistant Professor, Department of IT, CBIT, Hyderabad (Telangana), India.

²Sammulal Porika, Professor, Department of CSE, JNTU CEJ, Hyderabad (Telangana), India.

³Suresh Pabboju, Professor, Department of IT, CBIT, Hyderabad (Telangana), India.

⁴Dr. D L Srinivasa Reddy, Department of IT, CBIT, Hyderabad (Telangana), India.

Abstract: Student performance measured in CO-PO (Course Outcome and Program Outcome) attainment for OMR based answer sheet automation playing very curtail role in pupil concert analysis in this approach. In the proposed work, marks evaluation sheet is consider as input image, then apply frame cropping technique to extract the marks filled table by subdividing into cells as individual images by frame cropping technique. In order to recognition of hand written digit in each frame, various machine learning models are adopted, trained. Experimental results from proposed work show that convolutional neural network excels higher in identification digits from frames. The outputs are then converted to CSV version, which is used to evaluate CO-PO attainment for each learner. The experiments have been conducted and tested in proposed work on various machine learning techniques and compared the results to pick the optimal model.

Keywords: Hand Written Digit, Object Detection, Classification, Deep Learning.
Scope of the Article: Deep Learning

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