Volume-10 , Issue-6 , Jun - 2022 Go Back

In the last decade, the management of software projects has become a challenging task. The latest published figures on the status of software projects indicate a large failure rate, which has created a crucial challenge for project managers. In software maintenance, the impact of software changes is an important aspect due to the evolving environment of the software development life cycle. Many of the current traceability approaches and tools are devoted to and restricted to high-level objects such as specifications but fewer capabilities are made available to handle lower-level artefacts such as classes and codes. While test effort estimation has been in place for decades, it remains a major challenge for software project management to make accurate estimates and, ultimately, to successfully complete the software project. This article proposed a novel traceability model for test effort estimation to support software change management employing Design and Development Research (DDR), which may assist software project managers in making more informed decisions on software change management. In this paper will show two phase Fuzzy Delphi Method (FDM) and Nominal Group Technique (NGT) result. The both results in FDM and NGT showed that the key components and elements are located at acceptable level and can be applied whilst the score of more than 70% is achieved. Hence, the evaluation results proved that the proposed model and its prototype are acceptable and significant to support software change management.

Design and Development Research (DDR), Traceability Model, Test Effort Estimation, Change Management

[1] Bennett, K. H., & Rajlich, V. T. (May). Software maintenance and evolution: a roadmap. Proceedings of the Conference on the Future of Software Engineering ACM pp 73-87, 2000.
[2] Nurmuliani, N., Zowghi, D., & Williams, S. Requirements volatility & its impact on change effort: Evidence based research n software development projects. In Verified OK. University of South Australia. 2006.
[3] Chen, C.-Y., and Chen, P.-C. A holistic approach to managing software change impact. Journal of Systems and Software, Vol 82 Issue (12), pp 2051-2067, 2009.
[4] Finkelsteiin, A., & Kramer, J. Software engineering: a roadmap. Proceedings of the Conference on the Future of Software Engineering, pp. 3-22, 2000.
[5] Chua, B., Bernardo, D., and Verner, J. Criteria for Estimating Effort for Requirements Changes. In R. O’Connor, N. Baddoo, K. Smolander & R. Messnarz (Eds.), Software Process Improvement Springer Berlin Heidelberg, Vol. 16, pp. 36-46,2008.
[6] Lehtinen, T. O. A., Mäntylä, M. V., Vanhanen, J., Itkonen, J., and Lassenius, C.). Perceived causes of software project failures – An analysis of their relationships. Information and Software Technology, pp 623-643. 2014.
[7] Asl, M. H., and Kama, N. A Change Impact Size Estimation Approach during the Software Development. Paper presented at the Software Engineering Conference (ASWEC),pp 68-77. 2013.
[8] Anooja A, Jameel Ahmad Qurashi, Sanjay Kumar, "A Survey Study of Various Software Cost Effort Estimation in Perspective of India", International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.928-933, 2019.
[9] Chua, B. Rework Requirement Changes in Software Maintenance. Software Engineering Advances (ICSEA), 2010 Fifth International Conference pp 252-258, 2010.
[10] Neeraj kumar, Yogesh kumar, Rahul Rishi, "Software Effort Estimation Techniques", International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.139-142, 2019.
[11] Sharif, B., Khan, S. A., and Bhatti, M. W. Measuring the Impact of Changing Requirements on Software Project Cost: An Empirical Investigation. IJCSI International Journal of Computer Science Issues. pp 170-174. 2012.
[12] Singh, M., and Vyas, R. Requirements Volatility in Software Development Process. International Journal of Soft Computing, 2. 2012
[13] Stammel, J., and Trifu, M. Tool-supported estimation of software evolution effort in service-oriented systems. Paper presented at the First International Workshop on Model-Driven Software Migration (MDSM 2011), pp 56. 2011.
[14] Richey, R., & Klien, J. Design and development research: Method, strategies and issues. London: Erlbaum .2007
[15] Richey, R. C., & Klein, J. D. Research on design and development. Handbook of research for educational communications and technology .pp. 748–757. 2008.
[16] Richey, R. C., & Klein, J. D. Design and development research. In Handbook of research on educational communications and technology pp. 141-150, 2014.
[17] Jamil, M. R. M., Siraj, S., & Hussin, Z. Nurulrabihah Mat Noh., & Ahmad Arifin Sapar Pengenalan Asas Kaedah Fuzzy Delphi Dalam Penyelidikan Rekabentuk dan Pembangunan.(Mohd Ridhuan Mohd Jamil, Ed.). Kuala Lumpur, Malaysia: Minda Intelek Agency. 2017.
[18] Muhammad Imran Yousof. Using experts’ opinions through Delphi technique. Practical Assessment Research & Evaluation, Vol 12 No 4, pp 1-8. 2007.
[19] Ragin, C. C. Qualitative comparative analysis using fuzzy sets (fsQCA). In Configurational comparative analysis. London: Sage Publications. 2007.
[20] Siraj, S. Kurikulum masa depan. Penerbit Universiti Malaya. 2008
[21] Bojadziev, G., & Bojadziev, M.. Fuzzy Set For Business, Finance and Management. Singapore: World Scientific Publishing Co. Pte. Ltd. 2007.
[22] Nasurddin, A. M., & Osman, I. Pengantar pengurusan. Utusan Publications. 2006.
[23] Dobbie, A., Rhodes, M., Tysinger, J. W., & Freeman, J. (). Using a modified nominal group technique as a curriculum evaluation tool. FAMILY MEDICINE-KANSAS CITY-, Vol 36,pp 402-406, 2004.
[24] Perry, J., & Linsley, S. The use of the nominal group technique as an evaluative tool in the teaching and summative assessment of the inter-personal skills of student mental health nurses. Nurse education today, Vol 26 No 4, pp 346-353 2006.
[25] Dang, V. H. The Use of Nominal Group Technique: Case Study in Vietnam. World Journal of Education, Vol 5 No 4, pp 14-25, 2015.
[26] Allen, J., Dyas, J., & Jones, M. Building consensus in health care: a guide to using the nominal group technique. British journal of community nursing, Vol 9 No 3, pp 110-114, 2004.
[27] Harvey, N., & Holmes, C. A Nominal group technique: an effective method for obtaining group consensus. International journal of nursing practice, Vol 18 No 2, pp 188-194, 2012.
[28] Williams, P. L., White, N., Klem, R., Wilson, S. E., & Bartholomew,P. Clinical education and training: jousing the nominal group technique in research with radiographers to identify factors affecting quality and capacity. Radiography, Vol 12 No 3, pp 215-224, 2006.
[29] Cheng, C. H., & Lin, Y. Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European journal of operational research, Vol 142 No 1, pp 174-186, 2002.
[30] Hammons, J. O., & Murry Jr, J. W. Management appraisal systems in community colleges: How good are they?. Community College Review, Vol 24, No 1, pp 19-28, 1996.
[31] Tang, C.W. and , Wu, C.T. Obtaining a picture of undergraduate education quality: a voice from inside the university, Springer. Higher Education, pp 269-286, 2010.
[32] Bodjanova, S. Median alpha-levels of a fuzzy numbe. Fuzzy Sets and Systems, Vol.157 no 7, pp 879 – 891, 2006.
[33] Mack, Z., & Sharples, S. The importance of usability in product choice: A mobile phone case study. Ergonomics, Vol 52 No 12, pp 1514-1528, 2009.
[34] Milano, M., & Ullius, D. Designing powerful training: The sequential-iterative model. 1998.
[35] Deslandes, S. F., Mendes, C. H. F., Pires, T. D. O., & Campos, D. D. S. Use of the Nominal Group Technique and the Delphi Method to draw up evaluation indicators for strategies to deal with violence against children and adolescents in Brazil. Revista Brasileira de Saúde Materno Infantil, (2004b).Vol 10, pp 29-37, 2010.
[36] Swanson, R. A., & Falkman, S. K. Training delivery problems and solutions: Identification of novice trainer problems and expert trainer solutions. Human Resource Development Quarterly, Vol 8 No 4, pp 305-314, 1997.
[37] Berliner, D. C. Expert teachers: Their characteristics, development and accomplishments. Bulletin of Science, Technology and Society, Vol.24 No 3, pp 200-12. (2004a).
[38] Berliner, D. C. Describing the behavior and documenting the accomplishments of expert teachers. Bulletin of Science, Technology & Society, Vol.24 No 3pp 200-212, 2004b.

Mazidah Mat Rejab, Mohd Ridhuan Mohd Jamil, "Employing Design and Development Research (DDR) Approches in Traceability Model For Test Effort Estimation To Support Software Change Management," International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, pp.1-8, 2022.

In almost every homes, having the desired gender of baby present could also foster the joy needed for the coexistence between couples in the family whereas in some instances, not having the desired gender of baby becomes the root cause of every other family problems. This research focuses on: “An Improved Model for Baby Gender Guide Predictive System using KNN classification algorithm”. The model uses the trained dataset for prediction directly. The predictions were made by going through the trained dataset to obtain a new instance (x) for nearest neighbors and displaying the result of K instances. The new system was designed using object oriented analysis and design.methodology and was implemented using Hypertext Preprocessor (PHP) programming language and MySQL as the database software. The result of the new system indicates that the accuracy of the gender of babies predicted prior-to and within the first trimester of conception had a higher degree of accuracy of 92% which is superior to the sonographic system with an accuracy of 54%.

Improved, Model, Computerized System, Effective, Baby, Gender Guide and Validation

[1]. Adhatrao, K., Gaykar, A., Dhawan, A., Jaha, R. & Honrao, V. “Predicting students’ performance using ID3 and C45 classification algorithms”, International Journal of Data Mining and Knowledge Management Process, Vol. 3, Issue 5, pp. 39-52, 2013.
[2]. Antonio, M., Aida, V., David, I., Lucas, M. & Joan, B. “Ontology-based personalized recommendation of tourism and leisure activities”, Journal of Engineering Applications of Artificial Intelligence, Vol. 3, Issue 5, pp. 633-638, 2013.
[3]. Callen, P. W. (2008). Ultrasonography in obstetrics and gynecology (5th Ed.). Saunders Elsevier publisher.
[4]. Chelli, D., Methni, A., Dimassi, K., Boudaya, F., Safar, E., Zouaoui, B., Chelli, H. & Chennoufi, M. B. “Foetal sex assignment by first trimester ultrasound: A Tunisian experience”, Journal of Population Health Metrics, Vol. 29, Issue 3, pp. 145–148, 2009.
[5]. Eze, C. U., Ezugwu, F. O. & Agbo, J. A. “Sonographic determination of foetal gender in the second and third trimesters in a private hospital in Enugu”, International Journal of Ultrasonography, Vol. 16, Issue 5, pp. 292–96, 2010.
[6]. Farideh, G. “The ultrasound identification of foetal gender at the gestational age of 11–12 weeks”, International Journal of Family Medicine and Primary Health Care, Vol. 7, Issue 4, pp. 210-221, 2018.
[7]. Jie, L., Dianshuang, W., Mingsong, M., Wei, W. & Guangquan, Z. “Recommender system application developments for gender decision support system”, 2nd International Conference on Software Eng. and Data Mining, pp. 47-51, 2015.
[8]. Kumar, P. N. V. & Reddy, V. R. “A Survey on Recommender Systems and its applications”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 8, pp. 201-204, 2014.
[9]. Manette, K., Karen, P. & Ian, G. “Accuracy of sonographic foetal gender determination: predictions made by sonographers during routine obstetric ultrasound scans”, Australian Journal of Ultrasound Medicine, Vol. 17, Issue 3, pp. 80-83, 2019.
[10]. Mebarki, M., Kaidi, R., Azizi, A. & Basbaci, M., (2019) Comparative efficacy of two-dimensional mode and color Doppler sonography in predicting gender of the equine foetus, Veterinary World, 12(2) 325-330, 2019.
[11]. Mehrbakhsh, N., Othman, B. I., Norafida, I. & Rozana, Z. “A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques”, International Journal of Basic and Applied Sciences, Vol. 19 Issue 11, pp. 317-320, 2015.
[12]. Ning, L., Ping-Chia, T., Yu-An, C., and Homer, H. C. “Music recommendation based on artist novelty and similarity”, IEEE 16th International Workshop on Multimedia Signal Processing, 1-6, 2014.
[13]. Payal M., Abhishek G., Manoj K. G. “A study on various techniques involved in gender prediction system”, Bulgarian Journal of Cybernetics Sciences and Information Technologies, Vol. 19, Issue 2, pp. 11-14, 2019.
[14]. Rashma, M., & Remesh, B. K. R. “Recommendation system: a big data application”, International Journal of Emerging Trends in Science and Technology Impact Factor, Vol. 3, Issue 9, pp. 39-46, 2016.
[15]. Ricardo, S., Isabel, G., Ana, C., Diego, L., Pilar, P., Paola, M. & Juan L. “Ultrasound measurement learning of foetal sex during the first trimester: does the experience matter?”, Dove Press Journal Research and Reports in Focused Ultrasound. Vol. 15, Issue 4, pp. 201-205, 2015.
[16]. Rudas, I. J. & Fodor Z. “Intelligent systems”, International Journal of Computers, Communications and Control, Vol. 13, Issue 2, pp. 132-138, 2011.
[17]. Seda C. & Gökhan S. “Gender prediction by using local binary pattern, k nearest neighbor and discriminant analysis classifications. International Journal of Advanced information Technology, Vol. 5, Issue 3, pp. 37-40, 2016.
[18]. Tim V. H., Giuseppe G., Enrique A. R., Davy P., and Wouter J. “A Systematic Comparison of Age and Gender Prediction on IMU Sensor-Based Gait Traces”, PMCID, Vol. 19, Issue 13, pp. 294-295, 2019.

Ezikwa Tenas God’swill, Ezikwa Victoria Tenas, "An Improved Model For Baby Gender Guide Predictive System Using K-Nearest Neighbour Algorithm," International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, pp.9-15, 2022.

Due to the high blood sugar or blood glucose, the problem of diabetes will occur, and it`s also referred to as a metabolic disorder. Long-term high blood glucose levels can result in several heart-related disorders, strokes, renal illness, vision difficulties, dental problems, nerve damage, and other problems. The latest recent information about diabetes worldwide may be found in the IDF Diabetes Atlas, ninth edition 2021.There are 537 million adults facing the problem of diabetes according to the measurement of 2021 year. And we are guessing that there will be total diabetes patients will number 643 million by 2030 and 783 million by 2045. To predict the diabetes, we generally use machine learning algorithms. Here we have executed various machine learning algorithms like K-Nearest Neighbor, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Stacking and Stacking with Hyperparameter Tuning. Each model will have different accuracy in compared to other models. The most accurate result can be achieved by the stacking and stacking with hyperparameter tuning.

Machine Learning, Diabetes, Random Forest, Stacking, Hyperparameter Tuning, LogisticRegression

[1] M. Soni, Dr. S. Varma, “Diabetes Prediction Using Machine Learning Techniques”. International Journal of Engineering Research & Technology, Vol.9, Issue. 9, pp. 921-925, 2020.
[2] K. Patil, S.D. Sawarkar, “Designing a Model to Detect Diabetes Using Machine Learning”. International Journal of Engineering Research & Technology, Vol. 8, Issue. 11, pp. 512-515, 2019.
[3] S. Gujral, “Early Diabetes Detection Using Machine Learning”. International Journal for Innovative Research in Science & Technology, Vol. 3, Issue. 10, pp. 57-62 , 2017.
[4] S. Sivaranjani, S. Ananya, J. Aravinth, R. Karthika, “Diabetes Prediction using Machine Learning Algorithms with Feature Selection and Dimensionality Reduction”, International Conference on Advanced Computing and Communication Systems, India, pp. 141-146, 2021.
[5] D. K. Choubey, S. Paul, “A Hybrid Intelligent System for Diabetes Disease Diagnosis”, International Journal of Intelligent Systems and Applications, Vol. 08, Issue. 01, pp. 49-59 , 2016.
[6] M.Shuja, S. Mittal, M. Zaman, “Diabetes Mellitus and Data Mining Techniques: A survey”, International Journal of Computer Science and Engineering, Vol. 7, Issue. 01, pp. 856-862, 2019.
[7] V. Mishra, C. Samuel, S.K. Sharma, “Use of Machine Learning to Predict the Onset of Diabetes”, International Journal of Recent advances in Mechanical Engineering, Vol. 4, Issue 2, pp. 9-14 , 2015.
[8] N.A. Farooqui, Ritika, A. Tyagi, “Prediction Model for Diabetes Mellitus Using Machine Learning Techniques”, International Journal of Computer Science and Engineering, Vol. 6, Issue. 3, pp. 292-296, 2018.
[9] A. Vaghela, G. S. Pandit, “A Fusion Approach for Prediction of Diabetes sing machine learning Techniques”, International Research Journal of Engineering and Technology, Vol. 8, Issue 01, pp. 808-813 , 2021.
[10] R. Manimaran, M. Vanitha, “Prediction of Diabetes Disease Using Classification Data Mining Techniques”, International Journal of Engineering and Technology, Vol 9, Issue 5, pp. 3610-3614 , 2017.

Sadhana Tiwari, Awadhesh Kumar, Aasha Singh, "A Machine Learning Based Diabetes Prediction Using Stacking and Stacking With Hyperparameter Tuning," International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, pp.16-21, 2022.

The authentication techniques deals with the originality of an object. It may be image, text, audio etc. Image authentication widely use watermarking techniques both in spatial and transform domain, especially discrete wavelet based techniques are preferred for features like multilevel analysis and lossless reconstruction. Again, integer wavelets has added advantage of generating only integer coefficients which further make computation simpler and faster. This work initially used forward 5/3 integer wavelet transform (IWT) on cover image to generate sub bands. A watermark image is taken and its SHA256 hash code is generated. The watermark and hash code are embedded in two different sub bands using dynamic random position map and the sub bands are inverse 5/3 IWT transformed to generate stego image. During extraction, the opposite process is adopted and the hash code of extracted watermark is computed and compared with original hash code for verification of authenticity. The experimental observation of the proposed method revealed around 61.5dB peak signal to noise ratio (PSNR), near zero mean square error (MSE) and very high structural similarity index measure (SSIM) with 8448 bit payload and PSNR 55 dB with 33024 bits of payload.

Watermarking, Lifting Scheme, IWT, Randomization, Authentication

[1] B. Li, J. He, J. Huang, Y. Q. Shi, "A Survey on Image Steganography and Steganalysis", Journal of Information Hiding and Multimedia Signal Processing., Vol. 2, Issue.2, pp.142-172, 2011.
[2] J. Kadhim, P. Premaratne, P. J. Vial,B. Halloran , "Comprehensive Survey of Image Steganography: Techniques, Evaluations, and Trends in Future Research", Neurocomputing, Vol.335,Issue.1, pp.300-306,2019
[3] J. Fridrich, M. Goljan, R. Du, “Reliable Detection of LSB Steganography in Color and Grayscale Images”, In the Proceedings of the Workshop on Multimedia and Security: New Challenges, ACM, New York, pp. 27-30, 2001.
[4] A. R. Calderbank, I. Daubechies, W. Sweldens, B.L. Yeo, "Wavelet Transforms that Map Integers to Integers", Applied and Computational Harmonic Analysis,Vol.5,Issue.3,pp.332-369, 1998.
[5] N. Muhammad, N. Bibi, Z. Mahmood,D.G. Kim, "Blind Data Hiding Technique using the Fresnelet Transform", SpringerPlus, Vol.4,Issue.1,pp.1-15, 2015.
[6] H. Daren, L. Jiufen, H. Jiwu,L. Hongmei, "A DWT-Based Image Watermarking Algorithm", In IEEE International Conference on Multimedia and Expo 2001- ICME 2001, IEEE Computer Society, Tokyo, pp.313-316, 2001.
[7] S. Subburam, S. Selvakumar,S. Geetha, “High Performance Reversible Data Hiding Schemethrough Multilevel Histogram Modification in Lifting Integer Wavelet Transform”, Multimedia Tools and Applications. Vol.77,Issue.6,pp.7071-7095, 2018.
[8] W. Sweldens, "The Lifting Scheme: A Construction of Second Generation Wavelets", SIAM journal on Mathematical Analysis, Vol.29,Issue.2,pp.511-546, 1998.
[9] A. Shaik,V. Thanikaiselvan,"Comparative Analysis of Integer Wavelet Transforms in Reversible Data Hiding Using Threshold Based Histogram Modification",Journal of King Saud University-Computer and Information Sciences, Vol.33, Issue.7,pp.878-889, 2021.
[10] S. Fazli,M. Moeini,"A Robust Image Watermarking Method Based on DWT, DCT, and SVD Using a New Technique for Correction of Main Geometric Attacks",Optik Vol.127, Issue.2,pp. 964-972, 2016
[11] Y. He and Y. Hu., "A Proposed Digital Image Watermarking Based on DWT-DCT-SVD," in 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), IEEE, Xian,pp.1214-1218,2018.
[12] M. Islam, A. Roy and L. R. Hussain, "Neural Network Based Robust Image Watermarking Technique in LWT Domain", Journal of Intelligent & Fuzzy Systems, Vol. 34,Issue.3, pp.1691-1700, 2018.
[13] A. A. Khaleel ,O. Serkan,"A Novel Hybrid DCT and DWT Based Robust Watermarking", Multimedia Tools and Applications, Vol.12, Issue.78, pp.17027-17049, 2019.
[14] R. Liu,T. Tan, "An SVD-Based Watermarking Scheme for Protecting Rightful Ownership", IEEE Transactions on Multimedia,Vol.4,No.1,pp.121-128, 2002.
[15] S. Kumar,S. B. Kumar,"DWT Based Color Image Watermarking using Maximum", Multimedia Tools and Applications, Vol.80,Issue.10,pp.15487-15510, 2021.
[16] O.J. Kwon, S. Choi,B. Lee,"A Watermark-Based Scheme for Authenticating JPEG Image Integrity", IEEE Access, Vol. 6,Issue.1,pp. 46194-46205, 2018.
[17] M. J. Barani, M. Y. Valandar,P. Ayubi, "A New Digital Image Tamper Detection Algorithm Based on Integer Wavelet Transform and Secured by Encrypted Authentication Sequence With 3D Quantum Map",Optik-International Journal of Light and Electron Optics,Vol.187,Issue.1, pp.205-222, 2019.
[18] M. D. Adams, F. Kossentini,R. K. Ward, "Generalized S Transform", IEEE Transactions on Signal Processing , Vol. 50, Issue.11, pp.2831-2842, 2002.
[19] I. Ahmad,A. S. Das,"Hardware Implementation Analysis of SHA-256 and SHA-512 Algorithms on FPGAs",Computers & Electrical Engineering,Vol.31,Issue.6,pp.345-360, 2005.
[20] A. Pradhan, A. K. Sahu, G. Swain,K. R. Sekhar, "Performance Evaluation Parameters of Image Steganography Techniques", In 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS), IEEE, Banglore, pp.1-8, 2016.

Bibek Ranjan Ghosh, Siddhartha Banerjee, Deepro Sarkar, "Invisible Watermark based Image Authentication System with 5/3 Integer Wavelet Transform," International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, pp.22-26, 2022.

Although plant diseases pose a major threat to food security, the lack of necessary infrastructure makes it still difficult to quickly identify plant diseases in many parts of the world. The combination of increasing global technology penetration and recent advances in machine vision made possible by machine learning has paved the way for diagnosing illnesses using python. Machine learning technique to identify 14 crop species and 26 diseases (or their absence) using a public dataset of 54,306 diseased and healthy plant leaf images collected under controlled conditions Train the network. The trained model achieved 99.35% accuracy in a sustained test set, demonstrating the feasibility of this approach. Overall, the approach of training machine learning models with increasingly large and publicly accessible image datasets represents a clear path to the diagnosis of global plant diseases.

Digital image processing, Agri-farm plant disease, Machine learning, Plant disease detection.

[1] Rossi, V., Onesti, G., Legler, S.E., “Use of systems analysis to develop plant disease models based on literature data: grape black-rot as a casestudy”, European Journal of Plant Pathology, 141, Issue 3, pp 427–444, March 2015.
[2] R.Pydipati,T.F.Burks,W.S.Lee, “Identification of citrus disease using color texture features and discriminant analysis”, Computers and Electronics in Agriculture, Volume 52, Issues 1–2, pp 49-59, June 2006.
[3] Shanwen Zhang, Xiaowei Wu, Zhuhong You, Liqing Zhang, “Leaf image based cucumber disease recognition using sparse representation classification”, Computers and Electronics in Agriculture, 134, pp 135–141, 2017.
[4] J.L. Hernández-Hernández, G. García-Mateos, J.M. González-Esquiva, D. Escarabajal-Henarejos, A. Ruiz-Canales, J.M. Molina-Martínez, “Optimal color space selection method for plant/soil segmentation in agriculture”, Computers and Electronics in Agriculture, 122, pp 124–132, 2016.
[5] Hrishikesh P. Kanjalkar, S.S.Lokhande, “Feature Extraction of Leaf Diseases”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3, Issue 1, pp 1095-1098, January 2014.
[6] Marian Wiwart, Gabriel Fordonski, Krystyna Zuk-Go?aszewska, Elzbieta Suchowilska, “Early diagnostics of macronutrient deficiencies in three legume species by color image analysis”, Computers and Electronics in Agriculture, Volume 65, Issue 1, pp 125- 132, January 2009.
[7]X.E. Pantazi, D.Moshou, A.A. Tamouridou, “Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers”, Computers and Electronics in Agriculture, Volume 156, pp 96-104, January 2019.
[8] Zahid Iqbal, Muhammad Attique Khan, Muhammad Sharif, Jamal Hussain Shah, Uhammad Habib ur Rehman, Kashif Javed, “Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection”, Computers and Electronics in Agriculture, pp 12-32. 2018.
[9] Konstantinos P.Ferentinos, “Deep learning models for plant disease detection and diagnosis”, Computers and Electronics in Agriculture, Volume 145, pp 311-318, February 2018.
[10] Github.com, “PlantVillage-Dataset”, 2020 [Online]. Available on: https://github.com/spMohanty/PlantVillage-Dataset [Accessed on 03-05- 2020].
[11] C. G. Dhaware and K. H. Wanjale, "A modern approach for plant leaf disease classification which depends on leaf image processing," 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, pp. 1-4, 2017.
[12] Hossam M. Moftah, Ahmad Taher Azar, Eiman Tamah Al-Shammari, Neveen I. Ghali, Aboul Ella Hassanien & Mahmoud Shoman, “Adaptive k-means clustering algorithm for MR breast image segmentation”, Neural Computing and Applications, Volume 24, pp 1917–1928, 2014.
[13] M. Hussain, S. K. Wajid, A. Elzaart and M. Berbar, "A Comparison of SVM Kernel Functions for Breast Cancer Detection," 2011 Eighth International Conference Computer Graphics, Imaging and Visualization, Singapore, pp. 145-150, 2011.
[14] Yookesh, T. L., et al. "Efficiency ofiterative filtering method for solving Volterra fuzzy integral equations with adelay and material investigation."Materials today: Proceedings 47 (2021):6101-6104.
[15] Kumar, E. Boopathi, and V.Thiagarasu. "Segmentation using FuzzyMembership Functions: An Approach."IJCSE, ISSN (2017): 2347-2693.

B. Aishwarya, R. Vadivel, "Python Based Image Processing and Machine Learning for Plant Disease Detection," International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, pp.27-31, 2022.

Images are very easy to understand and remembered by humans. The human brain can understand if the image belongs to a dog or a cat by just having a look at it. Computer vision is one of the subjects in artificial intelligence that made it possible for a machine to visualize objects like a human brain. Although, the machine can visualize objects like human brain but still the path is so long to reach the accuracy of a human brain. The amount of data we collect today, which is subsequently utilised to train and improve computer vision, is one of the driving drivers behind its rise. Computer vision is the science by which various objects can be detected in fraction of time with the help of neural networks. Early computer vision investigations began in the 1950s, and by the 1970s, it was being used commercially to discern between typed and handwritten text. Today, computer vision applications have evolved tremendously. In this paper, a general introduction to computer vision is provided with an understanding of all the concepts of computer vision. A basic neural network is implemented from scratch for the same. The basic neural network developed achieves an accuracy of 89.13% when trained on Fashion MNIST datas¬¬et.

Computer Vision, Image Processing, bounding Boxes, Object Detection, Image Segmentation

[1] L. G. Shapiro, “Computer Vision?: the Last Fifty Years,” pp. 1–8, 2015.
[2] D. Marr, Part I: Introduction and philosophical preliminaries. 2010.
[3] A. A. Khan, A. A. Laghari, and S. A. Awan, “Machine Learning in Computer Vision?: A Review,” pp. 1–11.
[4] A. Bhargava and A. Bansal, “Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review,” Multimed. Tools Appl., vol. 80, no. 13, pp. 19931–19946, 2021, doi: 10.1007/s11042-021-10714-5.
[5] R. Sohail et al., “A review on machine vision and image processing techniques for weed detection in agricultural crops,” Pakistan J. Agric. Sci., vol. 58, no. 1, pp. 187–204, 2021, doi: 10.21162/PAKJAS/21.305.
[6] R. Qin and A. Gruen, “The role of machine intelligence in photogrammetric 3D modeling–an overview and perspectives,” Int. J. Digit. Earth, vol. 14, no. 1, pp. 15–31, 2021, doi: 10.1080/17538947.2020.1805037.
[7] Z. Fan, Y. Zhu, Y. He, Q. Sun, H. Liu, and J. He, “Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview,” ACM Comput. Surv., vol. 1, no. 1, 2021, doi: 10.1145/3524496.
[8] F. Okura, “3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping,” Breed. Sci., vol. 72, no. 1, pp. 31–47, 2022, doi: 10.1270/jsbbs.21074.
[9] C. Gupta and N. S. Gill, “Coronamask: A face mask detector for real-time data,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 5624–5630, 2020, doi: 10.30534/ijatcse/2020/212942020.
[10] A. Esteva et al., “Deep learning-enabled medical computer vision,” npj Digit. Med., vol. 4, no. 1, pp. 1–9, 2021, doi: 10.1038/s41746-020-00376-2.
[11] S. Xu, J. Wang, W. Shou, T. Ngo, A. M. Sadick, and X. Wang, “Computer Vision Techniques in Construction: A Critical Review,” Arch. Comput. Methods Eng., vol. 28, no. 5, pp. 3383–3397, 2021, doi: 10.1007/s11831-020-09504-3.
[12] C. Z. Dong and F. N. Catbas, “A review of computer vision–based structural health monitoring at local and global levels,” Struct. Heal. Monit., vol. 20, no. 2, pp. 692–743, 2021, doi: 10.1177/1475921720935585.
[13] B. H. W. Guo, Y. Zou, Y. Fang, Y. M. Goh, and P. X. W. Zou, “Computer vision technologies for safety science and management in construction: A critical review and future research directions,” Saf. Sci., vol. 135, no. January, p. 105-130, 2021, doi: 10.1016/j.ssci.2020.105130.
[14] J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, “Deep learning in computer vision: A critical review of emerging techniques and application scenarios,” Mach. Learn. with Appl., vol. 6, no. August, pp. 100-134, 2021, doi: 10.1016/j.mlwa.2021.100134.
[15] U. Iqbal, P. Perez, W. Li, and J. Barthelemy, “How computer vision can facilitate flood management: A systematic review,” Int. J. Disaster Risk Reduct., vol. 53, no. January, p. 102030, 2021, doi: 10.1016/j.ijdrr.2020.102030.
[16] I. Nyalala, C. Okinda, C. Kunjie, T. Korohou, L. Nyalala, and Q. Chao, “Weight and volume estimation of poultry and products based on computer vision systems: a review,” Poult. Sci., vol. 100, no. 5, p. 101072, 2021, doi: 10.1016/j.psj.2021.101072.
[17] C. Chen, W. Zhu, and T. Norton, “Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning,” Comput. Electron. Agric., vol. 187, no. January, p. 106255, 2021, doi: 10.1016/j.compag.2021.106255.
[18] G. Li et al., “Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review,” Sensors, vol. 21, no. 4, pp. 1–42, 2021, doi: 10.3390/s21041492.
[19] Z. Wu, Y. Chen, B. Zhao, X. Kang, and Y. Ding, “Review of weed detection methods based on computer vision,” Sensors, vol. 21, no. 11, pp. 1–23, 2021, doi: 10.3390/s21113647.
[20] L. Yang et al., Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review, vol. 28, no. 4. Springer Netherlands, 2021.
[21] T. Saba, “Computer vision for microscopic skin cancer diagnosis using handcrafted and non-handcrafted features,” Microsc. Res. Tech., vol. 84, no. 6, pp. 1272–1283, 2021, doi: 10.1002/jemt.23686.
[22] A. Bhargava and A. Bansal, “Fruits and vegetables quality evaluation using computer vision: A review,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 3, pp. 243–257, 2021, doi: 10.1016/j.jksuci.2018.06.002.
[23] C. M. Louis, A. Erwin, N. Handayani, A. A. Polim, A. Boediono, and I. Sini, “Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF,” J. Assist. Reprod. Genet., vol. 38, no. 7, pp. 1627–1639, 2021, doi: 10.1007/s10815-021-02123-2.
[24] Y. Baid and A. Dhole, “Food Image Classification Using Machine Learning Techniques: A Review,” Int. J. Comput. Sci. Eng., vol. 9, no. 7, pp. 11–15, 2021, doi: 10.26438/ijcse/v9i7.1115.
[25] Y. Baid and A. Dhole, “Food Image Classification Using Deep Learning Techniques,” Int. J. Comput. Sci. Eng., vol. 9, no. 7, pp. 11–15, 2021, doi: 10.26438/ijcse/v9i7.1115.

Kirti Sharma, Chhaya Gupta, "Introduction to Computer Vision: An End-to-End Guide for Beginners," International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, pp.32-36, 2022.

Lack of fast, accurate, reliable and intelligent software solutions that can help healthcare practitioners make decisions that would solve urgent, and in some cases, complex medical problems in real-time. Cost of processing and analyzing large volumes of data in a medical environment is high most especially in terms of time consumption. Application model for enhanced medical intelligence process were developed in this paper and it can be applied in healthcare centers, clinics and maternities in Nigeria. The healthcare centers, clinics and maternities, etc can link the application model developed to their database servers so that the application will connect the platform to the database server in other to carry out disease control procedures using ontology-based (OBDI) and virtual data integration (VDI) techniques as the have the ability to ensure abstraction of data that comes from multiple sources in varying schemas, syntactic accuracy and to have a seamless transition from data into information, then into action. The objective of the design is to develop an application model for enhanced business intelligence process which was achieved using ontology-based data integration (OBDI) system, application model uses intelligent agent to guide doctors accurately by carrying out disease control procedures. Test results on the new system using confusion matrix shows a significant positive impact 88% accuracy in medical intelligence process as against 60% of accuracy by the existing system, and hence a significant improvement on overall operating efficiency. The model is therefore recommended for use by Physicians, government, hospital administrators and patients.

OBDI, Hospital administrators, database, VDI, DV and Physicians

[1].Amineh, A., Hadi, S., Nasser, N. (2008). A RDF-based Data Integration Framework. NEEC 2008 www.1211.6273.pdf/ retrieved on May 23, 2021
[2].Rick, V. D. L., (2012). Data Virtualization for Business Intelligence Systems”, www.r20.nl Retrieved from www.3-s2.0-B978...000010.pdf/ on Dec.7, 2012
[3].Leopoldo, B., (2007). Virtual Data Integration" Carleton University School of Computer Science Ottawa, Canada, www.tutorial-Bertossi.pdf/ retrieved on May 5, 2021
[4].Magali, R. & Michel, S., (2015) .Virtualization in System Biology: Meta Model & Modeling Language for Semantic Data Integration" retrieved on May 29, 2021
[5]. Francesco, D. T., Ezio, L. & Filippo, T., (2015) .Academic Data Warehouse Design Using Hybrid Methodology. Computer Science & Information System 12(1):135-160 DOI: 10.2298/c815140325087D www.csisn3p135-160.pdf/ retrieved on May 5, 2021
[6]. Munmun, B. & Nashreen, N. (2016). Study on Theoretical Aspects of Virtual Data Integration and its Applications. International Journal of Engineering Research and Applications, 6(2), 69-74, 2015.
[7].Virginija, U. & Rimantas, B. (2011) . Ontology-based Foundations for Data Integration. The First International Conference on Business Intelligence and Technology Copyright IARIA, 2011. ISBN: 978-1-61208-160-1.
[8].Longbing, C., Chengqi, Z., & Jiming, L., (2017). Ontology-Based Integration of Business Intelligence. Retrieved from www.w639.pdf/ on Dec.5, 2017.
[9]. Hema, M. S. & Chandramathi, S., (2013). Quality Aware Service Oriented Ontology Based Data Integration. WSEAS Transactions on Computers E-ISSN: 2224-2872 12 (12), 12-16
[10].Mezghani, E., Exposito, E. , Drira, K., Silveira, M. and Pruski, C. (2015). A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare,” Journal Medical System, 39(12), 185, 2015.
[11].Marut, B. (2016). Ontology-based Clinical Reminder System to Support Chronic Disease Healthcare. Article in IEICE Transactions on Information and Systems · DOI: 10.1587/transinf.E94.D.432 · Source: DBLP
[12].Madhura, J., Dinithi, N., Daswin, S., Damminda, A., Brian, D., Kate, E .W. (2020). A data integration platform for patient-centered e-healthcare and clinical decision support. Research Center for Data Analytics and Cognition, La Trobe University, Victoria, Australia b School of Allied Health, La Trobe University. Victoria, Australia
[13].Chih-Lin, C. (2019). Medical decision support systems based on machine learning. PhD (Doctor of Philosophy) thesis, University of Iowa, https://doi.org/10.17077/etd.o5gmwvxk

Asogwa E.C., Amanze B.C., Ngene C.C., Belonwu T.S., Chukwuogo O.E., "Study on Theoretical Aspects of ontology-based and Virtual Data Integration in medical intelligence process and its Applications," International Journal of Computer Sciences and Engineering, Vol.10, Issue.6, pp.37-45, 2022.