Brain Neoplasm Classification & Detection of Accuracy on MRI Images by ResearchPark

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Brain Neoplasm Classification & Detection of Accuracy on MRI Images Gowthaman R, Jayavignesh K, Jeevanantham V Department of CSE, Adhiyamaan College of Engineering, (Autonomous), Hosur, Tamil Nadu, India gowthamraja321@gmail.com, jayavignesh806@gmail.com, jeevavel243@gmail.com K S. Veeradanya Assistant Professor, Department of CSE, Adhiyamaan College of Engineering, (Autonomous), Hosur, Tamil Nadu, India svenom14@gmail.com --------------------------------------------------------------***-------------------------------------------------------------

Annotation: The abnormal, uncontrolled cell growth in the brain, commonly known n as a brain tumor, can lead to immense pressure on the various nerves and blood vessels, causing irreversible harm to the body. Early detection of brain tumors is the key to avoiding such compilations. Tumour detection can be done through various advanced Machine Learning and Image Processing algorithms. Mind Brain tumors have demonstrated testing to treat, to a great extent inferable from the organic qualities of these diseases, which frequently plan to restrict progress. To begin with, by invading one of the body's most significant organs, these growths are much of the time situated past the compass of even the most gifted neurosurgeon. These cancers are likewise situated behind the blood-cerebrum boundary (BBB), a tight intersection and transport proteins that shield fragile brain tissues from openness to factors in the overall flow, subsequently obstructing openness to foundational chemotherapy [6,7]. Besides, the interesting formative, hereditary, epigenetic and micro environmental elements of the cerebrum much of the time render these tumors impervious to ordinary and novel medicines. These difficulties are accumulated by the uncommonness of cerebrum growths comparative with numerous different types of disease, restricting the degree of subsidizing and interest from the drug business and drawing in a moderately little and divided research local area. Key words: Component, Detection, Diagnosis, MRI, Tumor. INTRODUCTION The various stages of brain tumour detection are image pre-processing, segmentation and feature extraction. Preprocessing includes enhancing the image by using various fitters and removing noise. Segmentation includes methods like thresholding, region growing etc. Features like contrast, skewness, and entropy are calculated for the extruded tumor. Far reaching clinical history joined with an exhaustive general actual assessment, neurologic assessment, pertinent imaging modalities, and the histopathological examine is critical in laying out the determination. While playing out the neurologic assessment, the neurosurgeon surveys vision, coordination, reflexes, hearing, direction, muscle tone and so forth. Beside ordinary MRI, an essential device, other imaging modalities of note incorporate gadolinium improved MRI, useful MRI, perfusion MRI, CT sweep, and PET-CT filter [27]. Contingent on the prerequisites, further demonstrative devices can be lumbar cut/spinal tap, stereotactic biopsy, angiogram, sub-atomic measure, myelogram and craniotomy. X-ray, which utilizes solid attractive fields instead of X-beams/ionizing radiations to deliver a straightforward portrayal, is the ideal imaging method followed by other indicative devices [10]. Despite the fact that MRI is a focal imaging device in patients where MRI isn't alluring (weight, inhabiting inserts, catheters, feeling of dread toward close spaces), a CT output can be utilized as a choice. Other than radiology (MRI/PET-CT examine and so on) that flaunts striking worth in conclusion, histopathology is © 2022, IJOT

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basic for the assessment of an assortment of cancer, making the finding certain. Various classifiers like Artificial Neural Network and Naive Bayer characterize the cancer as harmless or threatening. Despite the fact that clinical indications change in light of classification, size and site of cancer, the most well-known clinical elements detailed are relentless cerebral pain, seizures, queasiness and heaving, loss of awareness, discombobulation, temperament vacillation, mental issues, visual imperfections, shortcoming of body parts, trouble with discourse and language, and hearing hindrance. Aside from neurologic assessment, symptomatic methodologies incorporate Computed Tomography (CT) sweep, MRI and biopsy [79-101]. The advancing technology has had a profound effect on medical imaging. The body‘s building blocks are the various cells which make up the organs. A tumor is a disease arising out of these cells. Tumors can be benign (primary) or malignant (metastatic or secondary). A benign tumor is static, continuing to grow in size, applying enormous pressure on the surrounding tissues of the brain causing problems. On the other hand, malignant tumours can originate somewhere else in the body as a mass of cancerous cells and migrate to the brain [1]. Expert opinion, Human Inspection and Biopsy are a few available methods to diagnose tumours. Some of the drawbacks of these methods are excessive time consumption, inaccurate inspection etc. Hence, image-processing techniques are very helpful in the detection of brain tumours. Some of the current imaging techniques are Computed Tomography, Positron Emission Tomography and Magnetic Resonance Imaging [102-115]. The developing commonness rate in created nations might be ascribed to the accessibility of cutting edge procedures for recognition and analysis contrasted with that of emerging nations, where a great many people have restricted admittance to gear for early identification [116-134]. This outcomes in undiscovered and unregistered cases, diminishing the occurrence and commonness level [4]. Cerebrum growths are analyzed at the age of 3-12 years in kids and 40 - 70 years in grown-ups. Cerebrum growths range from harmless to dangerous and at last to metastatic cancers [135-156]. Metastatic mind growths are more pervasive in grown-ups [5]. The most common imaging technique used to detect and visualize brain tumor formation is magnetic resonance imaging. It gives us a detailed analysis of the unaffected or healthy tissues of the brain and the affected tissues [2]. Computer-aided diagnosis is based on studying the various cerebral tissues of the brain white matter, grey matter and cerebrospinal fluid. Careful analysis of these scans helps plan an effective treatment for every patient [157166]. SURVEY OF EXISTING METHODOLOGY Existing methodology segmentation and classification have been surveyed, and the results have been tabulated in Table 1, given below. The methodology used in the past by various researchers for segmentation of the brain tumour and then extracting the features have some flaws which give low accuracy in the results [167-175]. The most common segmentation methods used are K-means segmentation and watershed segmentation, which lead to over-segmenting the tumour. The commonly used classifier SVM has been found to give inferior results as compared to the newer classification algorithms based on neural networks [176-188]. Table 1. Various methodologies studied Year 20102012

Method Watershed Segmentation

Remark A simplified algorithm, very accurate and has low computational overhead

2012

Bayesian HMM, SVM

Using tumour probability map with SVM classifier or hidden Markov chain

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REF [9] [10] [11] [12] [13] Page 26

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2013

Neural network-based

2013 2012

Support vector machine

2015

K-means clustering, SVM

2016

Convolution Neural Network

Supervised and unsupervised learning for tumour detection. Accuracy is dependent on feature selection, and inputs are given. SVM accuracy depends on the dataset and the application

[14]

Better segmentation effect for low SNR brain MR Images More accurate results and reliable information for clinic treatments

[15] [16] [17] [18] [19]

PROPOSED METHODOLOGY The major steps followed in detecting a tumour from an MRI Scan are preprocessing„ Segmentation, Feature Extraction and classification. The flowchart in Figure 1 shows the major steps with snapshots of the output at each stage [189-199]. The process begins with accumulating a clean data set of either T-1 weighted or T-2 weighted MRI scans.

Figure 1. Flow Diagram Preprocessing of the MRI Images Pre-processing is the stage where noise is removed and any minute details are ameliorated. Clinical MRI scans riddled with noise reduce the accuracy of the segmentation algorithm output [3]. Multiple filters are used to eliminate the various kinds of noise. Median filters eliminate salt and pepper noise, while anisotropic filters preserve the edges. To procure segmentation without any noise, this module is necessary. This refinement revamps the overall image quality. Shows the contrast between a noise riddled image and a preprocessed image (figure 2).

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Figure 2. Preprocess of image SEGMENTATION METHODS Boundary Approach Thresholding is one of the most basic segmentation methods used to isolate the tumour. All the pixels are allocated to a category based on the range that they lie in. That is, for a certain threshold value t, the pixel located at position (i, j) with a grayscale value as shown in equation (1).

Edge-Based Approach In the edge-based segmentation approach, the detected edges are assumed to be the representative boundaries of objects. Obtaining a distinct closed edge from this approach is highly unlikely. Additionally, it is imperative to implement edge linking to join partial edges to gain an object‘s complete closed distinct edge. Region-Based Approach or Clustering Region-based segmentation or Clustering-based segmentation is based on pixel connectivity. A pixel can be connected to four, six, or eight in a 2-D image. It implies that all the pixels in a certain region are congruent or have a somewhat similar value. The focus is more on finding pixels that satisfy the connectivity criteria than the object‘s edges. Clusters are made up of congruent pixels [56-78]. Particle Swarm Optimization Molecule swarm streamlining is an enhancement calculation demonstrated in the wake of recreating the social way of behaving of birds in a group. It is a populace based calculation. A gathering of irregular particles or arrangements frames the underlying arrangement of the calculation. This underlying arrangement then, at that point, looks for the optima by refreshing the ages [6]. Each multitude molecule navigates the hunt space, continually changing its position premise the separation from its very own best situation in the multitude and from that of the best molecule of the multitude. A wellness work estimates the wellness worth of each and every molecule in the multitude. Feature Extraction The intricacy of the mind makes the confinement of the growth a troublesome errand. Different boundaries are considered for include extraction of the divided cancers from the MRI examines. The primary boundaries for the © 2022, IJOT

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area of interest are the outflows of the Gray-Level Co-Occurrence Matrix (GLCM) descriptors — Autocorrelation, Contrast, Correlation, Cluster Prominence, Cluster Shade, Dissimilarity, Energy, Entropy, Homogeneity and Maximum Probability [20-35]. The consequences of component extraction are utilized to additionally characterize the growth as harmless or dangerous [36-55]. Classification Classification is done basis on the results of the feature extraction. Various patterns are mapped according to the extracted features, and then a classification is made for the extracted tumour. Various algorithms used for the classification are Artificial Neural Networks, Tree J84, Naive Bayes and the Lazy IBK [8]. COMPARISON OF SEGMENTATION ALGORITHMS Table 2: Comparison Between Genetic Algorithms and Particle Swarm Optimization Genetic Algorithms

Particle swarm optimization Particle swarm optimization is initialized with a Genetic algorithms operate on populations of group of random solutions (particles)and strings coded to represent the parameter set optimizes it iteratively Genetic Algorithms can implement three Analogies exist in PSO though they do not label main operations, i.e. Selection, crossover and its operations like genetic algorithms mutation From the above table 2 surveyed methodology, theoretically, the best segmentation methods are Genetic Algorithms and Particle Swarm Optimization, giving increased accuracy and efficiency compared to the above described segmentation methods, which gives a brief comparison between the two methods. Theoretically, using either of these two methods for segmentation of the tumour should give better efficiency and accuracy. CONCLUSION This paper gives a concise outline of cerebrum growth division and order procedures. A thorough investigation of the different phases of picture handling is introduced. Different division procedures are explained. It very well may be presumed that the calculations and the boundaries utilized in the proposed framework are completely intended to build the framework's productivity by accomplishing improved results. The limit approach and the edge-based division approach are exceptionally normal, however the locale developing methodology gives improved results. Precision and dependability are of most extreme significance in growth determination, as a patient's life relies upon the outcomes anticipated by the framework. In this manner, the proposed philosophy helps in expanding the precision and getting the ideal outcomes. Acknowledgement The consummation of our task has significant commitments from different individuals with whom we associated inside the association. This review, which is a finished satisfaction of the prerequisite for the level of Bachelor of Engineering, could never have been conceivable without the consistent help and shared coordination of a few group to whom we owe our genuine appreciation. Firstly and most importantly, we would sincerely like to thank our guide Prof. K.S.Veeradanya She has rendered her valuable knowledge of this subject and dedicated guidance with a touch of inspiration, vision, and motivation. She has assisted us through any hurdles that we encountered by giving plenty of early ideas, suggestions, and modifications and encouraged us to complete the project to the best of our abilities.

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54. Rajkumar Rajasekaran, Jolly Masih & K. Govinda (2021) An analysis of mobile pass-codes in case of criminal investigations through social network data, International Journal of Computers and Applications, 43:9, 954-959. 55. Masih, J. , Rajkumar, R. , Matharu, P. and Sharma, A. (2019) Market Capturing and Business Expansion Strategy for Gluten-Free Foods in India and USA Using PESTEL Model. Agricultural Sciences, 10, 202-213. 56. Sharma, V., Rajasekaran, R. K. & Badhrinarayanan, S. (2019). Visualization of Data Mining Techniques for the Prediction of Breast Cancer with High Accuracy Rates. Journal of Computer Science, 15(1), 118-130. 57. Aju, D., & Rajkumar, R. (2016). T1-T2 Weighted MR Image Composition And Cataloguing Of Brain Tumor Using Regularized Logistic Regression. Jurnal Teknologi, 78(9). 58. D. Kem, ―Strengthening online education: Challenges and opportunities in India,‖ International Journal of Humanities and Social Science Invention, vol. 11, no. 05, pp. 01-12, 2022. 59. D. Kem, ―Personalised and adaptive Learning: Emerging learning platforms in the era of digital and smart Learning,‖, International Journal of Social Science and Human Research, vol. 05, no. 2, pp. 385-391, 2022. 60. D. Kem, ―Policy discourse and communication strategies in India.‖, Journal of the Kerala Sociological Society, vol, XXXIII, No. 2, pp. 37-48, 2005. 61. D. Kem, ―‗Adolescents and the Mass Media: Contemporary Issues in the Literature, Journal of the Kerala Sociological Society, Thiruvananthapuram, Kerala, vol. XX no. 2, pp. 43-60, 2006. 62. D. Kem and M. Jena, ―Social responsibility of science.‖, Journal of the Kerala Sociological Society, vol, XXXV, no. 2, pp. 37-48, 2007. 63. F Rabbi, S Bature, M Omari, K Jermsittiparsert, ― The Mediating Effect of University Role in Determining the Relationship between Entrepreneurial Orientation, Entrepreneurial Perception and New Venture Creation: A Thai Case Study‖, International Journal of Innovation, Creativity and Change, Vol. 6 (10), 278-298, 2019. 64. Rabbi, F., & Almutairi, S. S. ―Corporate tax avoidance practices of multinationals and country responses to improve quality of compliance‖. International Journal for Quality Research, 15(1), 21-44, 2021. 65. Alharbi, Yousef; Rabbi, Fazle; Alqahtani, Rabee, ― Understanding University Student‘s Intention To Use Quality Cloud Storage Services‖, International Journal for Quality Research, Vol. 14 Issue 1, p313-324, 2020. 66. F Rabbi, ― A review of the recent trends in the use of machine learning in business‘, International Journal of Artificial Intelligence and Machine Learning Vol.1 (1), 1-6, 2019. 67. F Rabbi, ― A review of the use of machine learning techniques by social media enterprises‖, Journal of Contemporary Scientific Research, Vol.2 (4), pp. 1-14, 2018. 68. M Azeroual, Y Boujoudar, K Bhagat, L El Iysaouy, A Aljarbouh, et al.,, ― Fault location and detection techniques in power distribution systems with distributed generation: Kenitra City (Morocco) as a case study.‖ Electric Power Systems Research, Volume 209, August 2022, 108026. 69. Azeroual M, Boujoudar Y, Iysaouy LE, et al. Energy management and control system for microgrid based wind-PV-battery using multi-agent systems. Wind Engineering. February 2022. doi:10.1177/0309524X221075583

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70. Fazle Rabbi , Nasir Abdul Jalil , S. Suman Rajest , R. Regin, ― An Approximation For Monitoring The Efficiency Of Cooperative Across Diverse Network Aspects‖, Webology, Volume 17, No 2, 2020, Pages: 1234-1247 71. U Kumar, C Khatun, MS Islam, N Kao, F Rabbi, M Maniruzzaman, et al., ― Effect of Drum Pressure on Flow Accelerated Corrosion in Gas Fired Combined Cycle Power Plant: A Case Study and Literature Review‖, Research Communication in Engineering Science & Technology, 2, 17-27, 2019. 72. F Rabbi, ― Recent Trends in the Use of Machine Learning Techniques in Business‖, Asia Pacific Conference on Advances in Applied Science, Engineering and Technology (APCAASET)‘, 2019. 73. Fazle Rabbi, ― A Review of the Recent Trends in the Use of Machine Learning in Business,‖ International Conference on Education, Business and Social Science (ICONFEBSS), 2019. 74. F Rabbi, ― Application of Big Data in Promoting Sustainable Solutions for Business-A Review‖, Global Journal of Applied Sciences and Technology Vol. 3 (11), 2018 75. E. Murugan, S. Arumugam and P. Panneerselvam, ―New nanohybrids from poly (propylene imine) dendrimer stabilized silvernanoparticles on multiwalled carbon nanotubes for effective catalytic andantimicrobial applications,‖ International Journal of Polymeric Materials and Polymeric Biomaterials, vol. 65 no. 3, p. 111, 2016. 76. E. Murugan and I. Pakrudheen, ―Efficient amphiphilic poly (propylene imine) dendrimer encapsulated rutheniumnanoparticles for sensing and catalysis applications,‖ Science of Advanced Materials, vol. 7, no. 5, p. 891, 2015. 77. E. Murugan, and G. Tamizharasu, ―Synthesis and characterization of new soluble multisite phase transfercatalysts and their catalysis in free radical polymerization of methylmethacrylate aided by ultrasoundA kinetic study,‖ Journal of applied polymer science, vol. 125, no. 1, p. 263, 2012. 78. E. Murugan, R. Rangasamy, and I. Pakrudheen, ―Efficient amphiphilic poly (propyleneimine) dendrimer stabilized goldnanoparticle catalysts for aqueous phase reduction of nitrobenzene,‖ Science of Advanced Materials, vol. 4, no. 11, p. 1103, 2012. 79. A. Ramesh, P. Tamizhdurai, S. Gopinath, K. Sureshkumar, E. Murugan and K Shanthi, ―Facile synthesis of core-shell nanocomposites Au catalysts towards abatement of environmental pollutant Rhodamine B,‖ Heliyon, vol. 5, no. 1, p. e01005, 2019. 80. E. Murugan, J. N. Jebaranjitham, K. J. Raman, A. Mandal, D. Geethalakshmi, M. Dharmendira Kumar, and A. Saravanakumar, ―Insoluble dendrimer-grafted poly (vinylimidazole) microbeads stabilized withmono/bimetallic nanoparticle catalysts for effective degradation of malachitegreen,‖ New Journal of Chemistry, vol. 41, no.19, p. 10860, 2017. 81. E. Murugan and I. Pakrudheen, New amphiphilic poly (quaternary ammonium) dendrimer catalyst for effectivereduction of citronellal, Applied Catalysis A: General, vol. 439, p. 142, 2012. 82. Werku Etafa, Getahun Fetensa, Reta Tsegaye, Bizuneh Wakuma, Sundararajan Vasantha Kumari, Getu Bayisa , et al , ―Neonatal sepsis risk factorsin public hospitals in Wollega zones, Ethiopia: case control study ,‖ PAMJ - One Health,vol. 7, no. 2,p.1-13,2022. 83. S.Vasanthakumari , ―Writing research proposal,‖ World Journal of Advanced Research and Reviews,vol. 10, no.01,p.184-190,2021. © 2022, IJOT

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84. S.Vasanthakumari ,―Soft skills and its application in work place,‖ World Journal of Advanced Research and Reviews,vol. 03, no.02,p.66–72,2019. 85. S.Vasanthakumari ,― Mental Health Preparedness for School Children during COVID-19 Pandemic,‖ International Journal of Scientific Research,vol. 10, no.05,p.1-4,2021. 86. Nasser, N. S. (2021). The linguistic structure in the Iraqi civil laws. Qalaai Zanist Scientific Journal, 6(2), 578-598. 87. Nasir, N. S. (2020). The Effect of the Arabic Language on Legal Text Legislation. Journal of Al-Frahedis Arts, 12(42 II), 84-101. 88. Nasir, N. S. (2016). The connotations of the word (light) in the Holy Qur‘an and books of faces and analogies, journal of the college of basic education, 21(92), 1-24. 89. Nasser, N. S. (2021). The meaning of the word and its development in the proverb, Qalaai Zanist Journal, 3(1), 822–845. 90. Jalil, N.A., P Prapinit, M Melan, AB Mustaffa (2019). Adoption of Business Intelligence-Technological, Individual and Supply Chain Efficiency. Proceedings of the 2019 International Conference on Machine Learning, Big Data and Business Intelligence. Year: 2019, Volume: 1, Pages: 67-73. 91. Jalil, N.A., Hwang, H.J. (2019). Technological-centric business intelligence: Critical success factors. International Journal of Innovation, Creativity and Change, Volume 5, Issue 2, August, 2019, Pages 1499 to 1516. 92. Nasir Abdul Jalil and Koay Kian Yeik. 2019. Systems, Design and Technologies Anxieties Towards Use of Self-service Checkout. In Proceedings of the 2019 3rd International Conference on Education and E-Learning (ICEEL 2019). Association for Computing Machinery, New York, NY, USA, 122–127. 93. B. Singh, N. A. Jalil, D. K. Sharma, S. R, K. Kumar and D. Jebakumar immanuel, "Computational systems overview and Random Process with Theoretical analysis," 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp. 1999-2005. 94. Roy Setiawan, Luigi Pio Leonardo Cavaliere, KartikeyKoti, Gabriel Ayodeji Ogunmola, N. A. Jalil, M. Kalyan Chakravarthi, S. Suman Rajest, R. Regin, Sonia Singh, "The Artificial Intelligence and Inventory Effect on Banking Industrial Performance"Turkish Online Journal of Qualitative Inquiry (TOJQI). Volume 12, Issue 6, July, 2021: 8100-8125. 95. Roespinoedji, D., Juniati, S., Hasan, H., Jalil, N.A., Shamsudin, M.F., 2019. Experimenting the long-haul association between components of consuming renewable energy: ARDL method with special reference to Malaysia. Int. J. Energy Econ. Policy 9, 453–460. https://doi.org/10.32479/ijeep.8694. 96. D. K. Sharma, N. A. Jalil, V. K. Nassa, S. R. Vadyala, L. S. Senthamil and T. N, "Deep learning Applications to classify Cross-Topic Natural Language Texts Based on Their Argumentative Form," 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 1580-1586. 97. D. Hemavathi, V. R. Kumar, R. Regin, S. S. Rajest, K. Phasinam and S. Singh, "Technical Support for Detection and Prediction of Rainfall," 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 1629-1634. 98. Jayakumar P., Suman Rajest S., Aravind B.R. (2022) An Empirical Study on the Effectiveness of Online Teaching and Learning Outcomes with Regard to LSRW Skills in COVID-19 Pandemic. In: Hamdan A., © 2022, IJOT

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Hassanien A.E., Mescon T., Alareeni B. (eds) Technologies, Artificial Intelligence and the Future of Learning Post-COVID-19. Studies in Computational Intelligence, vol 1019. Springer, Cham. 99. D. K. Sharma, N. A. Jalil, R. Regin, S. S. Rajest, R. K. Tummala and T. N, "Predicting Network Congestion with Machine Learning," 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 1574-1579, doi: 10.1109/ICOSEC51865.2021.9591897. 100. Nasir Abdul Jalil and Mikkay Wong Ei Leen. 2021. Learning Analytics in Higher Education: The Student Expectations of Learning Analytics. In 2021 5th International Conference on Education and E-Learning (ICEEL 2021). Association for Computing Machinery, New York, NY, USA, 249–254. 101. Abu Sarwar Zamani, Mohammad Jawed Miandad and Shakir Khan, ―Data Center – Based, Service Oriented Architecture (SOA) in Cloud Computing‖, International Journal of Computing Science and Information Technology, Vol. 1, No. 1, pp. 33-37, 2013. http://ijcsit.org/IJCSIT-PaperVI.pdf 102. Shakir Khan and Mohamed F. AlAjmi, ―Impact of Medical Technology on Expansion in Healthcare Expenses‖, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 4, pp. 150152, 2013. 103. Shakir Khan, Mohammed AlAjmi and Arun Sharma, ―Safety Measures Investigation in Moodle LMS. International Journal of Computer Applications (IJCA), Vol. ICNICT, Special Issue No. 4, pp. 41-44, 2012. http://www.ijcaonline.org/specialissues/icnict/number4/9044-1080 104. Mohammed AlAjmi, Shakir Khan and Abu Sarvar Zamani, ―Using Instructive Data Mining Methods to Revise the Impact of Virtual Classroom in E-Learning‖, International Journal of Advanced Science and Technology, Vol. 45, No. 9, pp. 125-134, 2012. 105. Shakir Khan and Arun Sharma, ―Detailed Literature Survey Applying Data Mining Techniques in ELearning. International Journal of Engineering & Science Research‖, Vol. 2, No. 4, pp. 176-203, 2012. http://ijesr.org/admin/upload_journal/journal_shki%205aprl.pdf 106. Geno Peter, Anli Sherine, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, Histogram Shifting based Quick Response Steganography method for Secure Communication‖ Wireless Communications and Mobile Computing. vol. 2022, 10 pages, 2022. 107. Geno Peter, Anli Sherine, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, Design of Automated Deep Learning-based Fusion Model for Copy-Move Image Forgery Detection‖ Computational Intelligence and Neuroscience. vol. 2022, 9 pages, 2022. 108. Hariprasath Manoharan, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, K Venkatachalam, Acclimatization Of Nano Robots In Medical Applications Using Artificial Intelligence System With Data Transfer Approach‖ Wireless Communications And Mobile Computing. vol. 2022, 9 pages, 2022. 109. Ashok Kumar L, Ramya Kuppusamy, Yuvaraja Teekaraman, Indragandhi V, Arun Radhakrishnan, Design and Implementation of Automatic Water Spraying System for Solar Photovoltaic Module‖ Mathematical Problems In Engineering. vol. 2022, 9 pages, 2022. 110. K Veena, K Meena, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, Cybercrime Detection using C SVM and KNN Techniques‖ Wireless Communications and Mobile Computing. vol. 2022, 8 pages, 2022.

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111. Yuvaraja Teekaraman, KA Ramesh Kumar, Ramya Kuppusamy, Amruth Ramesh Thelkar, SSNN Based Energy Management Strategy in Grid-Connected System for Load Scheduling and Load Sharing‖ Mathematical Problems In Engineering. vol. 2022, Article ID 2447299, 9 pages, 2022. 112. M. Bharathidasan, V. Indragandhi, Ramya Kuppusamy, Yuvaraja Teekaraman, Shabana Urooj4,*, Norah Alwadi, ‗Intelligent Fuzzy Based High Gain Non-Isolated Converter for DC Micro-Grids‖ CMC-Computers, Materials & Continua. Vol 71, No.2, 2022. 113. Hariprasath Manoharan, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, A Novel Optimal Robotized Parking System Using Advanced Wireless Sensor Network‖ Journal of Sensors. Volume 2021, Page 1-8, 2021. 114. Kamaleshwar T, Lakshminarayanan R, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, A Self-Adaptive framework for Rectification and Detection of Blackhole and Wormhole attacks in 6LoWPAN‖ Wireless Communications And Mobile Computing. Volume 2021, 2021. Page 1-8. 115. Pavan Babu Bandla, Indragandhi Vairavasundaram, Yuvaraja Teekaraman, Srete Nikolovski, ―Real Time Sustainable Power Quality Analysis of Non-Linear Load under Symmetrical Conditions‖ Energies 2022, 15(01). 116. Hariprasath Manoharan, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, A Prognostic Three-Axis Coordination Model for Supply Chain Regulation Using Machine Learning Algorithm‖ Scientific Programming. Volume 2021, 2021. Page 1-9. 117. Hariprasath Manoharan, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, An Intellectual Energy Device for Household Appliances Using Artificial Neural Network‖ Mathematical Problems In Engineering. Volume 2021, 2021. Page 1-9. 118. Nagarajan Manikandan, Rajappa Muthaiah, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, A Novel Random Error Approximate Adder-Based Lightweight Image Encryption Scheme for Secure Remote Monitoring of Reliable Data‖ Security and Communication Networks. Vol 2021, 2021. Page 1-14. 119. Senthilselvan Natarajan, Subramaniyaswamy Vairavasundaram, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan, Schema-Based Mapping Approach for Data Transformation toEnrich Semantic Web‖ Wireless Communications and Mobile Computing. Vol 2021, 2021. Page 1-15. 120. Yuvaraja Teekaraman, Hariprasath Manoharan, Ramya Kuppusamy, Fadwa Alrowais, Shabana Urooj, Energy Efficient Multi-Hop Routing Protocol for Smart Vehicle Monitoring Using Intelligent Sensor Networks‖ International Journal Of Distributed Sensor Networks. Vol 17, Issue 12. 2021. Page 1-11. 121. Yuvaraja Teekaraman, Ramya Kuppusamy, V. Indragandhi, ‗Modeling and Analysis of PV System with Fuzzy Logic MPPT Technique for a DC Microgrid under Variable Atmospheric Conditions‖ Electronics. (20) 2541, 2021. 122. Yuvaraja Teekaraman, Ramya Kuppusamy, V. Indragandhi, ‗Investigations on the effect of micro-grid using improved NFIS-PID with hybrid algorithms‖ Computing. Springer 2021. DOI: 10.1007/s00607-021-010069. 123. Yuvaraja Teekaraman, Jasmin Pamela, V. Indragandhi, R. Saranya, Shabana Urooj, V. Subramaniyaswamy, Norah Alwadi ‗‘2D Finite Element Analysis of Asynchronous Machine Influenced under Power Quality Perturbations‖ CMC-Computers, Materials & Continua. Volume 70. Number 03, pp. 5745-5763, 2021. © 2022, IJOT

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124. Ratnam Kamala Sarojini, Palanisamy Kaliannan, Yuvaraja Teekaraman, Srete Nikolovski, Hamid Reza Baghaee, ''An Enhanced Emulated Inertia Control for Grid-Connected PV Systems with HESS in a Weak Grid''‖ Energies 2021, 14(06), 1455 (1-21); 125. Subramanian Vasantharaj, Indragandhi Vairavasundaram, Subramaniyaswamy Vairavasundaram, Yuvaraja Teekaraman, Ramya Kuppusamy, Nikolovski Srete, Efficient Control of DC Microgrid with Hybrid PV— Fuel Cell and Energy Storage Systems‖ Energies 2021, 14(06), 3234 (1-18); 126. Yuvaraja Teekaraman, Hariprasath Manoharan, "Implementation of Cognitive Radio Model for Agricultural Applications using Hybrid Algorithms". Wireless Personal Communications, Accepted. 2021. 127. Rahul Gopi, Soundarya S, Kavitha P, Yuvaraja Teekaraman, Ramya Kuppusamy, Shabana Urooj ―Enhanced Model Reference Adaptive Control Scheme for Tracking Control of Magnetic Levitation System‖ Energies 2021, 14(05), 1455 (1-13). 128. Shabana Urooj, Fadwa Alrowais, Yuvaraja Teekaraman, Hariprasath Manoharan, Ramya Kuppusamy, ―IoT Based Electric Vehicle Application Using Boosting Algorithm for Smart Cities‖ Energies 2021, 14(04), 1072 (1-15). 129. Shabana Urooj, Fadwa Alrowais, Ramya Kuppusamy, Yuvaraja Teekaraman, Hariprasath Manoharan, ―New Gen Controlling Variable using Dragonfly Algorithm in PV Panel‖ Energies 2021, 14(04), 790 (1-14). 130. Hariprasath Manoharan, Yuvaraja Teekaraman, Pravin R Kshirsagar, Shanmugam Sundaramurthy, Abirami Manoharan, Examining the effect of Aquaculture using Sensor based Technology with Machine Learning Algorithm. Aquaculture Research, 13(15), pp.1-16. 2020. 131. Hariprasath Manoharan, Yuvaraja Teekaraman, Irina Kirpichnikova, Ramya Kuppusamy, Srete Nikolovski, Hamid Reza Baghaee., Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression. Energies, 13(15), pp.1-16. 2020. 132. Yuvaraja Teekaraman, Hariprasath Manoharan., Adam Raja Basha, Abirami Manoharan., Hybrid Optimization Algorithms for Resource Allocation in Heterogeneous Cognitive Radio Networks. Neural Processing Letters. http://link.springer.com/article/10.1007/s11063-020- 10255-2. 2020. 133. Yuvaraja.T, KA Ramesh Kumar, ―Enhanced Frequency Shift Carrier Modulation for H Bridge Multilevel Converter to Conquer the Impact of Instability in Deputize Condenser Voltage‖ International Journal Of Electrical Engineering Education, Volume 57 Issue 2, April 2020. 134. Yuvaraja Teekaraman, K Ramya, Srete Nikolovski, ―Current Compensation in Grid Connected VSCs using Advanced Fuzzy Logic Based Fluffy Built SVPWM Switching‖ Energies 2020, 13(05), 1259. 135. Yuvaraja Teekaraman, Pranesh Sthapit, Miheung Choe, Kiseon Kim, ―Energy Analysis on Localization Free Routing Protocols in UWSNs‖ International Journal of Computational Intelligence System, Atlantis Press, Vol.12, Issue 2, pp. 1526-1536, 2019. 136. Yuvaraja.T, KA Ramesh Kumar, ―Fuzzy Control in H-Bridge MLI for Solar PV System to Enhance Load Sharing‖ International Journal of Electrical Engineering Education, Sage Publication, Volume: 57, Issue: 1, pp. 64-72. 2020. 137. K Ramya, Yuvaraja.Teekaraman, K A Ramesh Kumar, ―Fuzzy- Based Energy Management System with Decision Tree Algorithm for Power Security System‖ International Journal Of Computational Intelligence System, Atlantis Press. Vol.12, Issue 2, pp. 1173-1178, 2019. © 2022, IJOT

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138. Yuvaraja.T, K Ramya, ―Hierarchical Distributed Model Scheme Implementation in Dc- Microgrid for Numerous Ground Faults Condition‖ International Journal Of Electrical Engineering Education, Sage Publication, Vol. 56(4), pp. 348-363, 2019. 139. Yuvaraja.T, K Ramya, ―Statistical Data Analysis for Sung Reduction in 3Ǿ Fragmented Source Using Novel Fuzzy Digital Logic Switching Techniques‖ in Circuit World, Vol. 45, Issue No. 3, pp. 148-155. 2019. Emerald Publishing. DOI information: 10.1108/CW-12-2018-0107. 140. Yuvaraja.T, K Ramya, Hariprasath Manoharan, Abirami, ―State Approximation in Power System by using Quasi Derived Originating Procedure‖ in Measurement, 146 (2019) 924-929. Elsevier. 141. Yuvaraja Teekaraman, K Ramya, Srete Nikolovski, ―Solution for Voltage and Frequency Regulation in Stand Alone Micro Grid using Hybrid Multi Objective Symbiotic Organism Search Algorithm‖ Energies 2019, 12(14), 2812; 142. M V Tejeswini, I Jacob Raglend, T Yuvaraja, B N Radha, ―An Advanced Protection coordination technique for Solar in Feed Distribution Systems‖ AIN Shams Engineering Journal, Elsevier 10 (2019) 379-388. 143. Yuvaraja.T, Ramya.K, ―Discretionary Controller for Hybrid Energy Storage System Based on Orderly Control Considering Commercial Value in Decentralized Microgrid Operation‖ Compel: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering. ISSN: 0332-1649 Volume 37, Issue 6, 2018. Page No. 1969- 1980. 144. Yuvaraja.T, Ramya.K, ―Analysis of Wind Turbine Modeling using TSMC Techniques‖ Compel: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering. Volume 37, Issue 6, 2018. Page No. 1981- 1992. 145. Yuvaraja.T, K.Ramya, ―Vector Control of PMSM Take Over by Photovoltaic Source‖ Aces Journal, VOL. 33, NO. 2, FEB 2018. ISSN: 1054-4887. 146. Yuvaraja.T, Gopinath Mani, ―New Gen Algorithm for Detecting Sag and Swell Voltages in Single Phase Inverter System for Micro grid‖. Automatika, Online, DOI: 10.7305. Vol 57, No.3 (2016). 147. Gayathri Devi S, Subramaniyaswamy Vairavasundaram, Yuvaraja Teekaraman, Ramya Kuppusamy, Arun Radhakrishnan. A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts. Computational Intelligence And Neuroscience, Volume 2022 https://doi.org/10.1155/2022/3432330. 148. Yuvaraja Teekaraman, Hariprasath Manoharan, Abirami Manoharan, Diagnoses of reformed responses in curative applications using wireless sensors with dynamic control, Sustainable Computing: Informatics and Systems, Volume 35, 2022, 100677. 149. K. S. Archana, B. Sivakumar, Ramya Kuppusamy, Yuvaraja Teekaraman, and Arun Radhakrishnan, Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models‖ Computational And Mathematical Methods In Medicine. Article ID 9797844, vol. 2022, 08 pages, 2022. 150. G. Uganya, D. Rajalakshmi, Yuvaraja Teekaraman, Ramya Kuppusamy, and Arun Radhakrishnan, A Novel Strategy for Waste Prediction Using Machine Learning Algorithm with IoT Based Intelligent Waste Management System‖ Wireless Communications And Mobile Computing. Article ID 2063372, vol. 2022, 10 pages, 2022.

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151. Hariprasath Manoharan, Yuvaraja Teekaraman, Ramya Kuppusamy, Naveenkumar Kaliyan, Amruth Ramesh Thelkar, Examining the Effect of Cyber Twin and Blockchain Technologies for Industrial Applications Using AI. Mathematical Problems In Engineering Volume 2022 |Article ID 3048038. 152. Manikandan Nagarajan, Muthaiah Rajappa, Yuvaraja Teekaraman, Ramya Kuppusamy, Amruth Ramesh Thelkar. Renovated XTEA Encoder Architecture-Based Lightweight Mutual Authentication Protocol for RFID and Green Wireless Sensor Network Applications. Wireless Communications And Mobile Computing. Volume 2022 |Article ID 8876096. 153. Pavan Babu Bandla, Indragandhi Vairavasundaram, Yuvaraja Teekaraman, Srete Nikolovski, ―Real-Time Sustainable Power Quality Analysis of Non-Linear Load under Symmetrical Conditions‖ Energies 2022, 15(01), 57. 154. D.S. Hooda, Keerti Upadhyay and D.K. Sharma, ―On Parametric Generalization of ‗Useful‘ R- norm Information Measure‖ British Journal of Mathematics & Computer Science, Vol. 8(1), pp. 1-15, 2015. 155. D.S. Hooda, Keerti Upadhyay and D.K. Sharma, ―A Generalized Measure of ‗Useful R-norm Information‖, International Journal of Engineering Mathematics and Computer Sciences, Vol 3(5), pp.1-11, 2014. 156. D.S. Hooda, Keerti Upadhyay and D.K. Sharma, ―Bounds on Cost Measures in terms of ‗Useful‘ R-norm Information Measures‖ Direct Research Journal of Engineering and Information Technology, Vol.2 (2), pp.11-17, 2014. 157. D.S. Hooda and D.K. Sharma, ―Lower and Upper Bounds Inequality of a Generalized ‗Useful‘ Mean Code Length‖ GAMS Journal of Mathematics and Mathematical Biosciences, Vol. 4(1), pp.62-69, 2013. 158. D.S. Hooda, Keerti Upadhyay and D.K. Sharma, ‗Useful‘ R-Norm Information Measure and its Properties‖ IOSR Journal of Electronics and Communication Engineering, Vol. 8, pp. 52-57, 2013. 159. D.S. Hooda, Sonali Saxena and D.K. Sharma, ―A Generalized R-Norm Entropy and Coding Theorem‖ International Journal of Mathematical Sciences and Engineering Applications, Vol.5(2), pp.385-393, 2011. 160. D.S. Hooda and D.K. Sharma, ―Bounds on Two Generalized Cost Measures‖ Journal of Information & System Sciences, Vol. 35(3-4), pp. 513-530, 2010.

Combinatorics,

161. D.K. Sharma and D.S. Hooda, ―Generalized Measures of ‗Useful‘ Relative Information and Inequalities‖ Journal of Engineering, Management & Pharmaceutical Sciences, Vol.1(1), pp.15-21, 2010. 162. D.S. Hooda and D.K. Sharma (2010) ―Exponential Survival Entropies and Their Properties‖ Advances in Mathematical Sciences and Applications, Vol. 20, pp. 265-279, 2010. 163. D.S. Hooda and D.K. Sharma, ―Generalized ‗Useful‘ Information Generating Functions‖ Journal of Appl. Math. and Informatics, Vol. 27( 3-4), pp. 591-601, 2009. 164. D.S. Hooda and D.K. Sharma, ―Non-additive Generalized Measures of ‗Useful‘ Inaccuracy‖ Journal of Rajasthan Academy of Physical Sciences, Vol. 7(3), pp.359-368, 2008. 165. D.S. Hooda and D.K. Sharma, Generalized R-Norm information Measures-Journal of Appl. Statistics & informatics (JAMSI), Vol. 4 No.2 , 153-168, 2008.

Math,

166. Dilip Kumar Sharma, ―Some Generalized Information Measures: Their characterization and Applications‖, Lambert Academic Publishing, Germany, 2010. ISBN: 978-3838386041.

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167. Aakanksha Singhal and D.K. Sharma, ―Seven Divergence Measures by CDF of fitting in Exponential and Normal Distributions of COVID-19 Data‖, Turkish Journal of Physiotherapy and Rehabilitation, Vol.32(3), pp. 1212 - 1222, 2021. 168. D.K. Sharma and Haldhar Sharma, ―A Study of Trend Growth Rate of Confirmed cases, Death cases and Recovery cases in view of Covid-19 of Top Five States of India‖, Solid State Technology, Vol.64(2), pp. 4526-4541, 2021. 169. D.K. Sharma, ―Information Measure Computation and its Impact in MI COCO Dataset‖, IEEE Conference Proceedings, 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Vol.1, pp. 2011-2014, 2021. 170. Aakanksha Singhal and D.K. Sharma, ―Keyword extraction using Renyi entropy: a statistical and domain independent method‖, IEEE Conference Proceedings, 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Vol.1, pp. 1970-1975, 2021. 171. Aakanksha Singhal and D.K. Sharma, ―Generalization of F-Divergence Measures for Probability Distributions with Associated Utilities‖, Solid State Technology, Vol.64(2), pp. 5525-5531, 2021. 172. Aakanksha Singhal and D.K. Sharma, ―A Study of before and after Lockdown Situation of 10 Countries through Visualization of Data along With Entropy Analysis of Top Three Countries‖, International Journal of Future Generation Communication and Networking, Vol.14(1), pp. 496-525, 2021. 173. Aakanksha Singhal and D.K. Sharma, ―Generalized ‗Useful‘ Rényi & Tsallis Information Measures, Some Discussions with Application to Rainfall Data", International Journal of Grid and Distributed Computing, Vol. 13(2), pp. 681-688, 2020. 174. Reetu Kumari and D. K. Sharma, ―Generalized Ùseful non-symmetric divergence measures and Inequalities", Journal of Mathematical Inequalities, Vol. 13(2), pp. 451-466, 2019. 175. D.S. Hooda and D.K. Sharma, ―On Characterization of Joint and Conditional Exponential Survival Entropies", International Journal of Statistics and Reliability Engineering, Vol. 6(1), pp. 29-36, 2019. 176. Reetu Kumari and D. K. Sharma, ―Generalized Ùseful' AG and Ùseful' JS-Divergence Measures and their Bounds", International Journal of Engineering, Science and Mathematics, Vol. 7 (1), pp. 441-450, 2018. 177. D.S. Hooda, Reetu Kumari and D. K. Sharma, ―Intuitionistic Fuzzy Soft Set Theory and Its Application in Medical Diagnosis‖, International Journal of Statistics in Medical Research, Vol. 7, pp. 70-76, 2018. 178. D.K. Sharma and Sonali Saxena, ―Generalized Coding Theorem with Different Source Coding Schemes‖, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 5(6), pp. 253 – 257, 2017. 179. Suman Rajest S, P. Suresh, ―An Analysis of Chetan Bhagat‘s Revolution -2020: Love, Ambition, Corruption‖ in International Journal of English Language, Literature in Humanities, Volume: V, Issue IX, September 2017, Page No.: 52-62. 180. Suman Rajest S, P. Suresh, ―Galapagos: Is Human Accomplishment Worthwhile‖ in Online International Interdisciplinary Research Journal, Vol. VII, SI-II, September 2017, Page No.: 307-314. 181. Suman Rajest S, P. Suresh, ―The white Tiger by Aravind Adiga: Depiction of Fermentation in Society‖ in International Journal of Information Movement, Volume: II, Special Issue VI, October 2017, Page No.: 189194. © 2022, IJOT

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182. Suman Rajest S, P. Suresh, ―Confrontation on Modernism or Postmodernism Changes after the World War‖ in New Academia: An International Journal of English Language, Literature and Literary Theory, Volume: VII, Special Issue I, January 2018, Page No.: 50-76. 183. Suman Rajest S, P. Suresh, ―The Post-War Novel as Catch-22: The Chronology and Ex-P.F.C Winter Green‖ in International Journal of Research Culture Society, Volume: II, Special Issue II, February 2018, Page No.: 64-68. 184. S. Suman Rajest; Anbarasi, ―The Postwar Novel as Postmodern: Billy Pilgrim‘s Imagination and the Critical Tendency towards Teleology, Slaughterhouse – Five‖, International Journal of Advance Research, Ideas and Innovations in Technology, Volume 3, Issue 4, pp.37-41 (2017). 185. Suman Rajest S, P. Suresh, ―Necessary Heads Which are Used for Writing a Scholarly Journal‖ in New Man International Journal of Multidisciplinary Studies, Volume: V, Issue III, March 2018, Page No.: 5-21. 186. Suman Rajest S, P. Suresh, ―Impact of 21st century‘s different heads of learning skills for students and teachers‖ in International Journal of Multidisciplinary Research and Development, Volume: V, Issue IV, April 2018, Page No.: 170-178. 187. Suman Rajest S, P. Suresh, ―21st Century Learners‘ Student-Centered Learning Various Stages‖ in International Conference, Age and Content in Journey of Language by VISTAS (Tamil Department), Volume: I, Issue I, April 2018, Page No.: 474-492. (International Conference Paper) 188. Suman Rajest S, P. Suresh, ―American Postmodern Novelist Thomas Pynchon‘s The Crying of Lot 49: Structure and Absurd Realism‖ in Proceedings of the IOSRD, 73rd International Conference on Future Trends in Engineering and Business, Volume: 73, May 2018, Page No.: 32-41. 189. Suman Rajest S, P. Suresh, ―The ―Four Cs‖ Education For 21st Century‘s Learners‖ in Research Guru Online Journal of Multidisciplinary Subjects, Volume: XII, Issue I, June 2018, Page No.: 888-900. 190. Jerusha Angelene Christabel G, Suman Rajest S, ―A Short Review on Fragmented Narration in Select Works of Sarnath Banerjee‖, American Journal of Social and Humanitarian Research, Vol. 3 No. 4, pp. 12-31, (2022). 191. Rajest, D. S. S., & G, J. A. C. (2022). A Brief on Past and Present a Tug of War in the Select Works of Kurt Vonnegut. Central Asian Journal of Literature, Philosophy And Culture, 3(4), 59-79. 192. G, J. A. C., & Rajest, D. S. (2022). Fragmented Narration in Corridor‘s Thematic, Language and Imagery. Central Asian Journal Of Arts And Design, 3(4), 15-37. 193. Steffi. R, D.K. Sharma, S. Suman Rajest, R. Regin, A. J. Obaid, and G. Jerusha Angelene Christabel, ―Perceptron in Supervised, Semi-Supervised, Unsupervised Learning and Artificial Neural Network‖, CAJOTAS, vol. 3, no. 5, pp. 176-199, May 2022. 194. Manaa, Mehdi Ebady; Obaid, Ahmed J; Dosh, Mohammed Hussein, 2021. Unsupervised Approach for Email Spam Filtering using Data Mining, EAI Endorsed Transactions on Energy Web, DOI: 10.4108/eai.9-32021.168962. 195. Azmi Shawkat Abdulbaqi, Ahmed J. Obaid & Alyaa Hashem Mohammed (2021) ECG signals recruitment to implement a new technique for medical image encryption, Journal of Discrete Mathematical Sciences and Cryptography, 24:6, 1663-1673. 196. Obaid A.J., Sharma S. (2021) Data-Mining Based Novel Neural-Networks-Hierarchical Attention Structures for Obtaining an Optimal Efficiency. In: Favorskaya M.N., Peng SL., Simic M., Alhadidi B., Pal S. (eds) © 2022, IJOT

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Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2020. Learning and Analytics in Intelligent Systems, vol 21. Springer, Cham. 197. Das A., Ghosh A., Sahana S., Singh D., Obaid A.J. (2021) An Approach to Self-reliant Smart Road Using Piezoelectric Effect and Sensor Nodes. In: Favorskaya M.N., Peng SL., Simic M., Alhadidi B., Pal S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2020. Learning and Analytics in Intelligent Systems, vol 21. Springer, Cham. 198. Abdulbaqi, A., Abdulhameed, A., Obaid, A. (2021). A secure ECG signal transmission for heart disease diagnosis. International Journal of Nonlinear Analysis and Applications, 12(2), 1353-1370. 199. R. Regin, A. J. Obaid, A. Alenezi, F. Arslan, A. K. Gupta and K. H. Kadhim, "Node Replacement Based Energy Optimization Using Enhanced Salp Swarm Algorithm (Es2a) in Wireless Sensor Networks," Journal of Engineering Science and Technology, vol. 16, no. 3, pp. 2487 - 2501, 2021

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