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• Open Access   Article

  An Enhanced Dynamic Scheme for Medical Image Encryption Using Elliptical Curve Cryptography

  Sreekala M., Varghese Paul

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.5 , pp.1-9, Oct-2022

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  In order to increase security when transferring medical photos, this research suggests a revolutionary dynamic encryption technique. The paper addresses the various types of image encryption methods currently in use. Additionally, some early techniques for medical image encryption are reviewed in this study. Different medical image types, including CT, MRI, Ultrasound, X-ray, and ECG images, are taken into consideration for encryption. There is also a performance analysis of the reviewed methods and the suggested method. This work adds greater security than standard elliptic curve encryption because it uses the dynamic message mapping mechanism in elliptic curves.

  Key-Words / Index Term
  Medical Image, Encryption, Elliptic Curve Cryptography, Dynamic Message Mapping

  References
  [1] V. Pavithra and C. Jeyamala, “A Survey on the Techniques of Medical Image Encryption,” in 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, pp. 1–8, Dec. 2018.
  [2] D. Ravichandran, A. Banu S, B. K. Murthy, V. Balasubramanian, S. Fathima, and R. Amirtharajan, “An efficient medical image encryption using hybrid DNA computing and chaos in transform domain,” Med. Biol. Eng. Comput., vol. 59, no. 3, pp. 589–605, Mar. 2021.
  [3] S. Jeevitha and N. Amutha Prabha, “Novel medical image encryption using DWT block-based scrambling and edge maps,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 3, pp. 3373–3388, Mar. 2021.
  [4] A. Banu S and R. Amirtharajan, “A robust medical image encryption in dual domain: chaos-DNA-IWT combined approach,” Med. Biol. Eng. Comput., vol. 58, no. 7, pp. 1445–1458, Jul. 2020.
  [5] P. T. Akkasaligar and S. Biradar, “Selective medical image encryption using DNA cryptography,” Inf. Secur. J. Glob. Perspect., vol. 29, no. 2, pp. 91–101, Mar. 2020.
  [6] S. Kumar, B. Panna, and R. K. Jha, “Medical image encryption using fractional discrete cosine transform with chaotic function,” Med. Biol. Eng. Comput., vol. 57, no. 11, pp. 2517–2533, Nov. 2019.
  [7] H. Nematzadeh, R. Enayatifar, H. Motameni, F. G. Guimarães, and V. N. Coelho, “Medical image encryption using a hybrid model of modified genetic algorithm and coupled map lattices,” Opt. Lasers Eng., vol. 110, pp. 24–32, Nov. 2018.
  [8] C. Lakshmi, K. Thenmozhi, J. B. B. Rayappan, and R. Amirtharajan, “Encryption and watermark-treated medical image against hacking disease—An immune convention in spatial and frequency domains,” Comput. Methods Programs Biomed., vol. 159, pp. 11–21, Jun. 2018.
  [9] S. M. Ismail, L. A. Said, A. G. Radwan, A. H. Madian, and M. F. Abu-Elyazeed, “Generalized double-humped logistic map-based medical image encryption,” J. Adv. Res., vol. 10, pp. 85–98, Mar. 2018.
  [10] Z. Hua, S. Yi, and Y. Zhou, “Medical image encryption using high-speed scrambling and pixel adaptive diffusion,” Signal Process., vol. 144, pp. 134–144, Mar. 2018.
  [11] X. Chen and C.-J. Hu, “Adaptive medical image encryption algorithm based on multiple chaotic mapping,” Saudi J. Biol. Sci., vol. 24, no. 8, pp. 1821–1827, Dec. 2017.
  [12] D. S. Laiphrakpam and M. S. Khumanthem, “Medical image encryption based on improved ElGamal encryption technique,” Optik, vol. 147, pp. 88–102, Oct. 2017.
  [13] S. Haddad, G. Coatrieux, M. Cozic, and D. Bouslimi, “Joint Watermarking and Lossless JPEG-LS Compression for Medical Image Security,” IRBM, vol. 38, no. 4, pp. 198–206, Aug. 2017.
  [14] W. Cao, Y. Zhou, C. L. P. Chen, and L. Xia, “Medical image encryption using edge maps,” Signal Process., vol. 132, pp. 96–109, Mar. 2017.
  [15] D. Bouslimi and G. Coatrieux, “A crypto-watermarking system for ensuring reliability control and traceability of medical images,” Signal Process. Image Commun., vol. 47, pp. 160–169, Sep. 2016.
  [16] Y. Liu, X. Qu, and G. Xin, “A ROI-based reversible data hiding scheme in encrypted medical images,” J. Vis. Commun. Image Represent., vol. 39, pp. 51–57, Aug. 2016.
  [17] J. B. Lima, F. Madeiro, and F. J. R. Sales, “Encryption of medical images based on the cosine number transform,” Signal Process. Image Commun., vol. 35, pp. 1–8, Jul. 2015.
  [18] A. Kanso and M. Ghebleh, “An efficient and robust image encryption scheme for medical applications,” Commun. Nonlinear Sci. Numer. Simul., vol. 24, no. 1–3, pp. 98–116, Jul. 2015.
  [19] M. Ashtiyani, P. M. Birgani, and H. M. Hosseini, “Chaos-Based Medical Image Encryption Using Symmetric Cryptography,” in 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria, pp. 1–5, Apr. 2008.
  [20] J. B. Akan, E. A. Adedokun, G. Onuh, A. Umar, R. I. Nwosu, and Y. Ibrahim, “Medical Image Encryption Scheme Based On Hybrid Chaotic Permutation,” p. 8, 2020.
  [21] S. Sharma, Y. Verma, and A. Nadda, “Information Security: Cyber Security Challenges,” p. 6, 2019.
  [22] C. Chattopadhyay, B. Sarkar, and D. Mukherjee, “Encoding by DNA Relations and Randomization Through Chaotic Sequences for Image Encryption,” p. 16.

  Citation
  Sreekala M., Varghese Paul, "An Enhanced Dynamic Scheme for Medical Image Encryption Using Elliptical Curve Cryptography," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.5, pp.1-9, 2022

• Open Access   Article

  A Novel Approach for RFID Based Object Detection and Locating System

  Sanjay B. Ankali, Ashwini Pharalad, Bahubali Akiwate, S.G. Gollagi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.5 , pp.10-14, Oct-2022

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  It is the human tendency to mess up with finding the miss placed day-to-day objects such as car keys, spectacles, mobile chargers, etc. A survey shows that nearly 68% of working people find it difficult to find these necessary objects. This causes unnecessary wastage of time especially during getting ready for office. In this paper, we propose a low-cost RF-based object-locating system that uses compact RF-based object locators that can be attached to daily-use objects such as keys, spectacles, and chargers. The android-based application is developed to track the proximity of such RF trackers using BLE protocol. The application detects the signal strength to check the proximity of the target object. The proposed methodology uses both one-way and two-way trackers to provide proximity as well as proximity with a remote alarming system respectively to trigger an alarm for easy finding of the object from the developed android application. The proposed system can help in tracking and locating the objects using low energy and efficient Bluetooth transmission protocol. The system also implements GPS-based tracking of the objects on the map which can give the exact geographical coordinates along with the objects` position on the map. w.r.t. to the android application. The results show that the system works with 100% and 92% for indoor and GPS tracking respectively.

  Key-Words / Index Term
  Bluetooth, BLE, RF, Tracking, Objects, android, GPS, Map.

  References
  [1]. Song Chai, Renbo An and Zhongzheng Du “An Indoor Positioning Algorithm Using Bluetooth Low Energy RSSI” Proceedings of the 2016 International Conference on Advanced Materials Science and Environmental Engineering , pp. 274-276, 2016.
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  [7]. Rajashree H S, Sahana B, Surabhi S, T N Apoorva, Dr. Parameshachari B D, “Indoor Localization using BLE Technology”, International Journal Of Engineering Research & Technology (Ijert) Ncesc – Volume 6 – Issue 13, pp. 1-6, 2018.
  [8]. P.Karthika, J. Harriet Rathna Priya , A. Rathinavel Pandian.” Indoor Location Tracking System Using RFID Technology” International Journal of Engineering Research and Reviews ISSN 2348-697X (Online) Vol. 3, Issue 1, pp: 73-80, 2015
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  [10]. K. Yan, H. Zhou, H. Xiao and X. Zhang, "Current status of indoor positioning system based on visible light," 2015 15th International Conference on Control, Automation and Systems (ICCAS), pp. 565-569, 2015.
  [11]. H. -K. Su, Z. -X. L?ao, C. -H. Lin and T. -M. Lin, "A hybrid indoor-position mechanism based on bluetooth and WiFi communications for smart mobile devices," 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB), pp. 188-191, 2015.
  [12]. F. Palumbo, P. Barsocchi, S. Chessa and J. C. Augusto, "A stigmergic approach to indoor localization using Bluetooth Low Energy beacons, "12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-6, 2015.
  [13]. K. Kasantikul, C. Xiu, D. Yang and M. Yang, "An enhanced technique for indoor navigation system based on WIFI-RSSI," Seventh International Conference on Ubiquitous and Future Networks, pp. 513-518, 2015.

  Citation
  Sanjay B. Ankali, Ashwini Pharalad, Bahubali Akiwate, S.G. Gollagi, "A Novel Approach for RFID Based Object Detection and Locating System," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.5, pp.10-14, 2022

• Open Access   Article

  Evolutionary Training of Binary Neural Networks by Particle Swarm Optimization

  Hidehiko Okada

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.5 , pp.15-20, Oct-2022

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  A problem with deep neural networks is that the memory size for recording a trained model becomes large. A solution to this problem is to make the parameter values binary. A challenge for the binary neural networks is that they cannot be trained by the ordinary gradient-based optimization methods. The author previously applied Evolution Strategy (ES), Genetic Algorithm (GA) and Differential Evolution (DE) to the training of binary neural networks and evaluates its performance. In this paper, the author applies Particle Swarm Optimization, an instance of swarm intelligence algorithms, and compares PSO with ES, GA and DE. The experimental results with a classification task revealed that PSO could optimize binary weights so that the trained model classified both trained and untrained data with 90%+ accuracies, but the accuracies were significantly worse than those by the three algorithms. To apply PSO to a binary optimization problem, real-valued particle position vectors need to be binarized. PSO suffers from the design of this binarization. Improving the binarization is a future research challenge.

  Key-Words / Index Term
  Swarm intelligence algorithm; Particle swarm optimization; Neural network; Network quantization; Neuroevolution.

  References
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  [22] H. Okada, “Evolutionary Training of Binary Neural Networks by Differential Evolution,” International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue 1, pp.26–31, 2022.
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  Citation
  Hidehiko Okada, "Evolutionary Training of Binary Neural Networks by Particle Swarm Optimization," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.5, pp.15-20, 2022

• Open Access   Article

  Smart Home Automation Based On Bluetooth with IR Receiver

  Juhina Abdullah Al-Fahdi, Hajar Fadhil Al-Yahyaai, Bayan Rashid Al-Rashdi, Annamalai Muthu

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.5 , pp.21-26, Oct-2022

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  The safety and happiness of life at home are ensured by the current home automation system. As a result, the adoption of home automation technologies is growing in popularity, and as the saying goes, as technology advances, so do homes. Energy conservation is therefore more crucial than ever due to increased energy usage and population. The inability to handle equipment from remote locations is one of the major reasons of energy waste. Thus, by creating the Arduino UNO, the ATMEL has been able to advance Arduino in recent years. Here, we present a really intriguing concept: utilizing an Android application and Bluetooth, users may operate home appliances from distant locations. The basic goal is to build a Bluetooth-enabled, Arduino-powered home automation system that can be operated remotely by any Android smartphone. Therefore, we create an Arduino-based Bluetooth module that includes an IR receiver, a relay module, a Bluetooth module, and manual control switches to control home appliances. Through the Bluetooth module, the Arduino receives data from the smartphone. In order to operate the home appliances, the Arduino will then send a signal to the relay module. Both the manual switches and the IR remote control can be used to control the devices and turn on and off household appliances. This module is simple to use and makes it simple to utilize your home appliances.

  Key-Words / Index Term
  Android app, smartphone, Bluetooth module, IR receiver, 4 channel relay module, and Arduino

  References
  [1] Kumar, M., Shimi, S. L., “Voice recognition-based Home Automation system for paralyzed people”, International Journal of Advanced. Research in Electrical & Communication Engineering, Vol.4, Issue 10, pp. 2508–2515, 2015.
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  [7] Jayashree.M Sabarad and Mahesh Neelagar “Home Automation Using IOT with Fan Speed Control”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol4, Issue9, September 2016.

  Citation
  Juhina Abdullah Al-Fahdi, Hajar Fadhil Al-Yahyaai, Bayan Rashid Al-Rashdi, Annamalai Muthu, "Smart Home Automation Based On Bluetooth with IR Receiver," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.5, pp.21-26, 2022

• Open Access   Article

  Brain Tumor Detection using Cellular Automata based image Segmentation techniques

  Lahcen Elfatimi, Hanifa Boucheneb

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.5 , pp.27-36, Oct-2022

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  A quick and effective diagnosis is critical to the treatment of any disease. In the case of cancer-related diseases, the success of the treatment is typically correlated to the time of accurate diagnosis of the disease. Thus, it is important to make a quick and accurate diagnosis. This work presents a cellular automata-based system capable of diagnosing tumors in medical data from various imaging techniques, including MRI and X-ray. This system is an Automated Cellular (CA) that uses the Moore neighborhood algorithm to detect the area of cancer cells in the image, by segmenting areas of abnormality from the background. We also present an analysis of different parameters of the Moore neighborhood algorithm for optimal detection of cancerous cells; the results of this analysis confirm the proposed method effectiveness on all data sets, with an accuracy of more than 93\% and 95\% precision.

  Key-Words / Index Term
  Cellular Automata; Tumor Detection; Magnetic Resonance Imaging; Segmentation

  References
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  [20] Diwakar, Patel and Gupta, "Cellularautomata based Edge Detection for Brain tumor," In the Proceedings of the 2013 International Conference on Advances in Computing, Communications andInformatics, pp. 53-59, 2013.
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  Citation
  Lahcen Elfatimi, Hanifa Boucheneb, "Brain Tumor Detection using Cellular Automata based image Segmentation techniques," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.5, pp.27-36, 2022

• Open Access   Article

  A Convolutional Neural Networks (CNN) Approach to Music Genre Classification

  Abdulsalam Auwal Jamilu, Lawal Abubakar, Ubaidullah Abdallah

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.5 , pp.37-44, Oct-2022

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  Abstract
  Music is becoming easier to access through the internet, and musical applications like Spotify and Apple Music have common services that help their customers automatically classify music into different genres, the classification of music genres is a fundamental step in developing a powerful music recommendation engine.With the escalating number of music available digitally on the internet, there is a growing demand for the systematic organization of audio files and thus a rise in the interest in automatic music genre classification. Moreover, detecting and grouping music in a similar genre is a keen part of the music recommendation system and playlist that are personalized to soothe listeners’ mood and their unique music taste. However, Convolutional Neural Networks have appeared to be accurate in classifying music into different genres. Over the last decade, Convolutional Neural Networks have achieved breakthroughs in domains ranging from pattern recognition, image processing and voice recognition. For the Convolution Neural Network model to be able to classify music into different genres there would be a need for pre-processing of data by converting the raw audio into Mel-spectrograms. These features that have been extracted would then be used for training and classification. Additionally, Mel-spectrograms are visual, and CNN works better with images. This research focuses on a review, in the identification of music genres. Music Information Retrieval (MIR) can make it easier to identify essential information like trends, popular genres, and performers.

  Key-Words / Index Term
  Convolution Neural Network, Deep learning, Classification, Music genre classification.

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  Citation
  Abdulsalam Auwal Jamilu, Lawal Abubakar, Ubaidullah Abdallah, "A Convolutional Neural Networks (CNN) Approach to Music Genre Classification," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.5, pp.37-44, 2022

• Open Access   Article

  Click Through Rate Prediction Employing Wavelet Tree and Regression Learning

  Meghna Chandel, Sanjay Silakari, Rajeev Pandey, Smita Sharma

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.10 , Issue.5 , pp.45-51, Oct-2022

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Click through rates have proven to be a critical factor in deciding the effectiveness of online advertising models. Sponsored search advertising, contextual advertising, display advertising, and real-time bidding auctions have all relied heavily on the ability of learned models to predict ad click–through rates accurately, quickly, and reliably. Forecasting ad click–through rates (CTR) is a massive-scale learning problem that is central to the multi -billion dollar online advertising industry. Search engine advertising has become a significant element of the web browsing experience. Choosing the right ads for a query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. Accurately estimating the click-through rate (CTR) of ads has a vital impact on the revenue of search businesses; even a 0.1% accuracy improvement in production would yield hundreds of millions of dollars in additional earnings. An ad’s CTR is usually modelled as a forecasting problem, and thus can be estimated by machine learning models. The training data is collected from historical ads impressions and the corresponding clicks. An estimate of click through prior to fetching an add for a query is important for the accurate decision in the context. In this work a recursive binary partitioning algorithm is used along with support vector regression to estimate the bipolar nature of add clicks. A comparative analysis has also been made with exiting baseline techniques and it has been found that the proposed approach attains better performance metrics compared to baseline techniques.

  Key-Words / Index Term
  Online Advertising, Data Mining, Click Through Rates (CTR), Wavelet Tree, Regression Learning, Support Vector Regression, Prediction Accuracy

  References
  [1] JA Choi, K Lim, “Identifying machine learning techniques for classification of target advertising”, ICT Express, Elsevier, Vol. 6, Issue.3, pp.175-180, 2020.
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  [12] J Dhanani, K Rana, “Logistic Regression with Stochastic Gradient Ascent to Estimate Click Through Rate”, Information and Communication Technology for Sustainable Development, Springer, pp.319-326, 2018.

  Citation
  Meghna Chandel, Sanjay Silakari, Rajeev Pandey, Smita Sharma, "Click Through Rate Prediction Employing Wavelet Tree and Regression Learning," International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.5, pp.45-51, 2022