International Journal on Recent and Innovation Trends in Computing and Communication

Journal Information
EISSN : 2321-8169
Published by: Auricle Technologies, Pvt., Ltd. (10.17762)
Total articles ≅ 747
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Ritu Aggarwal, Suneet Kumar
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 67-76; https://doi.org/10.17762/ijritcc.v10i9.5702

Abstract:
Heart diseases that occur due to the blockage of coronary arteries, which causes heart attack, are also commonly known as myocardial infarction. Rapid detection and acute diagnosis of myocardial infarction avoid death. The electrocardiographic test or ECG signals are used to diagnosis myocardial infarction with the help of ST variations in the heart rhythm. ECG helps to detect whether the patient is normal and suffering from myocardial infarction. In blood, when the enzyme value increases, after a certain time pass occurs, heart attack. For ECG images, the manual reviewing process is a very difficult task. Due to advancements in technology, computer-aided tools and software are used to diagnosis myocardial infarction,because manual ECG requires more expertise .so that automatic detection of myocardial infarction on ECG could be done by different machine learning tools. This study detects the normal and myocardial infarction patients by selecting the feature with their feature weights by selecting from the model and by Random forest classifier selecting the index value using DenseNet-121, ResNet_50, and EfficientNet_b0 deep learning techniques .This proposed work used the real dataset from Medanta hospital (India) at the time of covid 19. The dataset is in the form of ECG images for Normal and myocardial infarction (960 samples). With an end-to-end structure, deep learning implements the standard 12-lead ECG signals for the detection of normal and myocardial infarction..The proposed model provides high performance on normal and myocardial infarction detection. The accuracy achieved by the proposed model for Efficientnet_b0 Random Forest to Select from Model Accuracy 84.244792, Precision 84.396532, Recall 84.227410, F-Measure 84.222295.
Alka Londhe, P. V. R. D. Prasada Rao
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 112-124; https://doi.org/10.17762/ijritcc.v10i9.5714

Abstract:
User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings.
Jyoti S. Raghatwan, Sandhya Arora
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 104-111; https://doi.org/10.17762/ijritcc.v10i9.5713

Abstract:
Generating a photographic face image from given input sketch is most challenging task in computer vision. Mainly the sketches drawn by sketch artist used in human identification. Sketch to photo synthesis is very important applications in law enforcement as well as character design, educational training. In recent years Generative Adversarial Network (GAN) shows excellent performance on sketch to photo synthesis problem. Quality of hand drawn sketches affects the quality generated photo. It might be possible that while handling the hand drawn sketches, accidently by touching the user hand on pencil sketch or similar activities causes noise in given sketch. Likewise different styles like shading, darkness of pencil used by sketch artist may cause unnecessary noise in sketches. In recent year many sketches to photo synthesis methods are proposed, but they are mainly focused on network architecture to get better performance. In this paper we proposed Filter-aided GAN framework to remove such noise while synthesizing photo images from hand drawn sketches. Here we implement and compare different filtering methods with GAN. Quantitative and qualitative result shows that proposed Filter-aided GAN generate the photo images which are visually pleasant and closer to ground truth image.
Tandu Rama Rao, P.V. Nageswara Rao
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 86-94; https://doi.org/10.17762/ijritcc.v10i9.5708

Abstract:
The Ad hoc on-demand multipath distance vector (AOMDV) routing protocol is one type of reactive routing protocol used in MANET. It is designed on top of the AODV routing protocol, so it utilizes the features of the AODV protocol. The MANET is a wireless ad hoc network without any physical infrastructure; all nodes can be moved across the network, and connections are made between them as needed simply with the help of RREQ, RREP, and RERR packets. Because the network is dynamic, nodes can quickly join and depart anytime. So far, no security threats have been caused by this feature. The blackhole attack is one type of active and dangerous attack in MANET. In this attack, the attackers use the AOMDV flaw to demonstrate their bad intent, causing data loss and decreasing network performance. Many studies have been done on various detection and prevention methods to prevent blackhole attacks. But it still goes on. To improve network performance against black hole attacks, this study offers a dynamic threshold value with multiple paths technique approach on AOMDV; it will be demonstrated in Network Simulator 2.
Priya Yadav, Sunil Kumar, Dilip Kumar J Saini
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 34-39; https://doi.org/10.17762/ijritcc.v10i9.5649

Abstract:
Cloud computing is frequently alluded to as a model that furnishes boundless information handling conveniences with compensation for each utilization framework. Present-day cloud foundations resources as virtual machines (VMs) to actual machines utilizing virtualization innovation. All VMs works their involved structure and exhaust resources from their actual machine which behaves like a host. For load adjusting, Cloud moves VMs from exceptionally troubled real machines to low troubled actual machines. The delay of this calculation expansions in the organization as virtual machines are relocated. This work puts forward a new algorithm, namely o for VM migration. The proposed optimization algorithm has been implemented in MATLAB software. A comparative analysis is performed between the outcomes of the preceding and the new algorithm. The proposed algorithm has been evaluated over three performance parameters including delay, bandwidth used and space used.
Praveena Akki, Gitanjali J, Celestine Iwendi, Sumathy. S, Amtul Waheed
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 95-103; https://doi.org/10.17762/ijritcc.v10i9.5709

Abstract:
Data compression plays a vital role in data security as it saves memory, transfer speed is high, easy to handle and secure. Mainly the compression techniques are categorized into two types. They are lossless, lossy data compression. The data format will be an audio, image, text or video. The main objective is to save memory of using these techniques is to save memory and to preserve data confidentiality, integrity. In this paper, a hybrid approach was proposed which combines Quotient Value Difference (QVD) with Huffman coding. These two methods are more efficient, simple to implement and provides better security to the data. The secret message is encoded using Huffman coding, while the cover image is compressed using QVD. Then the encoded data is embedded into cover image and transferred over the network to receiver. At the receiver end, the data is decompressed to obtain original message. The proposed method shows high level performance when compared to other existing methods with better quality and minimum error.
D. Kiranmayee, Jana Shafi, Amtul Waheed, P. Venkata Krishna
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 77-85; https://doi.org/10.17762/ijritcc.v10i9.5707

Abstract:
Accurate and complete vaccine volunteer’s data are one valuable asset for clinical research institutions. Privacy protection and the safe storage of vaccine volunteer’s data are vital concerns during clinical trial services. The advent of block-chain technology fetches an innovative idea to solve this problem. As a hash chain with the features of decentralization, authentication, and resistibility, blockchain-based technology can be used to safely store vaccine volunteer clinical trial data. In this paper, we proposed a safe storage method to control volunteer personal /clinical trial data based on blockchain with storing on cloud. Also, a service structure for sharing data of volunteer’s vaccine clinical trials is defined. Further, volunteer blockchain features are defined and examined. The projected storage and distribution method is independent of any third person and no single person has the complete influence to disturb the processing..
Sachin Vanjire, Sanjay Patil
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 125-132; https://doi.org/10.17762/ijritcc.v10i9.5715

Abstract:
The car industry is currently preoccupied with the issue of safety. The increasing number of accidents occurring around the world as a result of automobile problems is a major contributing factor to these incidents. The amount of complicated electronics that is used in vehicles is becoming more prevalent every day. A great effort has been made in evaluating vehicle features in relation to vehicle components. Through such systems, a smart architecture and complex function designs are involved. During all of this vehicle transformation and evolution, the automotive industry recognises a high demand for vehicle safety. While designing and manufacturing this system, automotive experts understand a need for a strict monitoring and feedback system for complex vehicle architecture, which can notify the end user if there is any indication of a failure ahead of time. In order to effectively participate in vehicle design activities, it is critical to grasp the significance of safety features. Tire system failures and braking system failures have played a large role in several recent traffic accidents. The failures of the tyre system and the braking system in the vehicle are addressed in this study. While investigating this system, it is discovered that it is supported by complex electrical systems, which include an ECU (electronic controller unit), sensors, and a wire system. Through the use of these technologies, censored data can be processed in a timely manner and made available for diagnostic purposes. Nevertheless, car diagnostics is needed after any vehicle failure but that does not serve the aim of maintaining vehicle safety. As a result, predictive analysis or predictive diagnostics may be a viable option for informing the driver about the health of a particular vehicle component in advance. In this study, the author discusses the concepts of vehicle prognostics for the tyre pressure monitor system and the antilock braking system, which are accomplished using a statistical method of machine learning. In today's world, machine learning is expanding in breadth, and the world is becoming more aware of its enormous potential in the field of data analytics. It is the purpose of this study to introduce methodologies by which machine learning can assist vehicle predictive analytics to attain the intended goal of vehicle safety.The author of this article discusses how Bayesian statistics may be used to produce predictions in the form of probability estimation. The prediction's outcome is thoroughly analysed.
Rajesh R Sharma, A. Bagubali, K. Sasikumar
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 07-14; https://doi.org/10.17762/ijritcc.v10i9.5640

Abstract:
In this paper, Physical Layer Network coding (PLNC)-Spatially Modulated Full-Duplex (SMFD) nodes based two-way/bidirectional cooperative wireless relay network is proposed. The PLNC-SMFD-based system is a viable technology in the field of next-generation wireless networks to enhance spectral efficiency. In the proposed system model, both the source nodes and relay nodes are employed with 2 × 2 antenna configurations where 2 bits of information are exchanged between the source nodes through a relay node. Transmit antenna selection at the source nodes is based on the incoming bitstreams. For instance, the transmit antenna is selected at PLNC-SMFD nodes based on the data symbols of the Most Significant Bit (MSB). Whereas the selected transmit antenna sends the Least Significant Bit (LSB) bit of data symbol at any time instance. Further, the self-interference at the transmitting and receiving nodes is modeled as Gaussian with the thermal noise power as a variance. The Bit Error Rate (BER) analytical expressions for both the upper and lower bound are derived in a Rayleigh Fading channel background. It has been graphically shown that the BER performance of the proposed system analyzes the effect of self-interference.
Bagubali Annasamy, Rajesh R, Sasikumar K, A. Baradeswaran, Kishore V Krishnan
International Journal on Recent and Innovation Trends in Computing and Communication, Volume 10, pp 15-25; https://doi.org/10.17762/ijritcc.v10i9.5641

Abstract:
Next-generation heterogeneous wireless networks provide flexibility to access IP multimedia services (IMS) anywhere at any time and with always-on connectivity in various networks. IMS service allows to include various networks to provide seamless data services and helps to reduce the delay that occurred during the handoff process. Long-term evolution (LTE) is the fastest emerging mobile communication technology in the current networking field. We proposed an interworking architecture of LTE and Worldwide Interoperability for Microwave Access (WiMAX). The key goal is to provide seamless services in heterogeneous networks. We proposed an algorithm to reduce this IMS delay using Cross-layer and Context Transfer Prior –SIP (P-SIP) scheme using Media Independent Handover (MIH) services. Performance metrics of Integrated LTE- WiMAX architecture in terms of VHO latency, signaling overhead cost, and packet loss are analyzed
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