Citations Report - Journal of Computer Engineering & Information Technology

Articles published in Journal of Computer Engineering & Information Technology have been cited by esteemed scholars and scientists all around the world. Journal of Computer Engineering & Information Technology has got h-index 9, which means every article in Journal of Computer Engineering & Information Technology has got 9 average citations.

Following are the list of articles that have cited the articles published in Journal of Computer Engineering & Information Technology.

	2024	2023	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013
Total published articles	61	61	55	60	30	13	21	30	25	18	9	13
Research, Review articles and Editorials	1	13	9	55	24	12	20	30	20	18	9	5
Research communications, Review communications, Editorial communications, Case reports and Commentary	49	43	44	0	3	5	4	4	2	1	0	1
Conference proceedings	0	35	39	21	16	15	117	140	71	93	0	0
Citations received as per Google Scholar, other indexing platforms and portals	42	55	66	67	57	64	54	47	65	15	7	6

Journal total citations count	553
Journal Impact Factor	1.12
Journal 5 years Impact Factor	1.72
Journal CiteScore	1.59
Journal h-index	9
Journal h-index since 2017	9

Shaikh, M. S., Hua, C., Jatoi, M. A., Ansari, M. M., & Qader, A. A. (2021). Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system. IET Science, Measurement & Technology, 15(2), 218-231.

Srilatha, B. Research & Reviews: Journal of Pharmaceutics and Nanotechnology A Review on Fluoroquinolone: Antimicrobial Drugs. Stroke, 56, 58.

Rasib, M., Butt, M. A., Khalid, S., Abid, S., Raiz, F., Jabbar, S., & Han, K. (2021). Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles. Mathematical Problems in Engineering, 2021.

Vala, T. M., Rajput, V. N., Geem, Z. W., Pandya, K. S., & Vora, S. C. (2021). Revisiting the performance of evolutionary algorithms. Expert Systems with Applications, 175, 114819.

Shehab, M., & Abualigah, L. (2021). Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems.

Centeno, A., BolÃvar, A., Rey, D., Arteaga, F., SÃ©ijas, C., & Almarza, Ã. D. (2019). ConfiguraciÃ³n de los parÃ¡metros de la bÃºsqueda armÃ³nica mediante lÃ³gica difusa. Revista INGENIERÃA UC, 26(1), 61-71.

Centeno, A., Vega, C., & ReyâLago, D. (2016). Metaâoptimization of harmony search. Revista Ingenier?a UC, 23(1), 81-89.

Centeno, A., Vega, C., & ReyâLago, D. (2016). MetaâoptimizaciÃ³n de la bÃºsqueda armÃ³nica. Revista INGENIERÃA UC, 23(1), 81-89.

Bhale, Y., & Joshi, R. Neuro Fuzzy Q-Learning Optimized Approach for Solving Latent Fingerprint Recognition.

Abbood, A. A., Sulong, G., Kaittan, N. M., & Peters, S. U. (2018, October). Automatic Fingerprint Classification Scheme by Using Template Matching with New Set of Singular Point-Based Features. In International Conference on New Trends in Information and Communications Technology Applications (pp. 226-239). Springer, Cham.

Peters, S. U. (2018, September). Automatic Fingerprint Classification Scheme by Using Template Matching with New Set of Singular Point-Based Features. In New Trends in Information and Communications Technology Applications: Third International Conference, NTICT 2018, Baghdad, Iraq, October 2â4, 2018, Proceedings (Vol. 938, p. 226). Springer.

Kayode, A. B., Babatunde, I. G., & Israel, H. D. (2013, December). DGM approach to network attacker and defender strategies. In 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013) (pp. 313-320). IEEE.

Ryutov, T., Orosz, M., Blythe, J., & von Winterfeldt, D. (2015, September). A game theoretic framework for modeling adversarial cyber security game among attackers, defenders, and users. In International workshop on security and trust management (pp. 274-282). Springer, Cham.

Ramachandran, K., & Stefanova, Z. (2016). Dynamic game theories in cyber security. Proc Dynam Syst Appl, 7, 303-10.

Alese, B. K., Thompson, A. F., & Oni, P. Y. (2017, June). A location privacy system in mobile network using game theory. In 2017 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA) (pp. 1-5). IEEE.

Stefanova, Z. S. (2018). Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems. University of South Florida.

Gutuleac, E., Zaporojan, S., Moraru, V., & Sclifos, A. (2019). Performance modeling of network defense in breadth systems by matrix rewriting SRN with fuzzy parameters.

Milov, O. (2019). Adaptive decision support systems for cyber security.

Munawar, S., Hamid, M., & Lodhi, S. A. (2017). Quantum Bayesian Game Modeling for Intrusion Detection. The Nucleus, 54(4), 214-218.

Milov, O. (2019). Adaptive decision support systems for cyber security.

GU?ULEAC, E., GÃ®rleanu, I., IAVORSCHI, I., & FURTUNA, A. (2017). Uncertainty Analysis of Attacker-Defender Interactions in MANET Based on Game GSPN with Intuitionistic Fuzzy Parameters.

Ahani, S., & Ghaemmaghami, S. (2015). Colour image steganography method based on sparse representation. IET Image Processing, 9(6), 496-505.

Fouad, M. M. (2017). Enhancing the imperceptibility of image steganography for information hiding. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (545-548). IEEE.

Kumar, S. (2015). A comparative study of transform based on secure image steganography. International Journal of Computer and Communication Engineering, 4(2), 107.

Naaz, K. A., Rizvi, I., & Kadam, M. M. (2015). Performance Analysis on Different Images using Reversible Data Hiding Technique and its Application. International Journal of Computer Applications, 975, 8887.

Abdelmotagally, M. F. (2017). Enhancing the Imperceptibility of Image Steganography for Information Hiding. Annals of Computer Science and Information Systems, 11, 545-548.

Phouthavong, T. (2016). Do first responders find Active911 useful?. Arkansas Tech University.

Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017). An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Applications, 68, 163-172.

Ogunleye, A., & Wang, Q. G. (2019). XGBoost model for chronic kidney disease diagnosis. IEEE/ACM transactions on computational biology and bioinformatics, 17(6), 2131-2140.

Samuel, O. W., Omisore, M. O., & Atajeromavwo, E. J. (2014). Online fuzzy based decision support system for human resource performance appraisal. Measurement, 55, 452-461.

Asogbon, M. G., Samuel, O. W., Omisore, M. O., & Awonusi, O. (2016). Enhanced neuro-fuzzy system based on genetic algorithm for medical diagnosis. J Med Diagn Meth, 5(205), 2.

Akhavan, P., & Philsoophian, M. (2018). Designing an expert fuzzy system to select the appropriate knowledge management strategy in accordance with APO model and Bloodgood KM strategies: a case study. VINE Journal of Information and Knowledge Management Systems.

Oguntimilehin, A., Adetunmbi, A. O., & Olatunji, K. A. (2014). A machine learning based clinical decision support system for diagnosis and treatment of typhoid fever. A Machine Learning Based Clinical Decision Support System for Diagnosis and Treatment of Typhoid Fever, 4(6), 1-9.

Ayo, F. E., Awotunde, J. B., Ogundokun, R. O., Folorunso, S. O., & Adekunle, A. O. (2020). A decision support system for multi-target disease diagnosis: a bioinformatics approach. Heliyon, 6(3), e03657.

Samuel, O. W., Yang, B., Geng, Y., Asogbon, M. G., Pirbhulal, S., Mzurikwao, D., ... & Li, G. (2020). A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks. Future Generation Computer Systems, 110, 781-794.

Oguntimilehin, A., Adetunmbi, O., & Osho, I. (2019). Towards achieving optimal performance using stacked generalization algorithm: a case study of clinical diagnosis of malaria fever. Int. Arab J. Inf. Technol., 16(6), 1074-1081.

Oguntimilehin, A., Abiola, O. B., & Olatunji, K. A. (2015). Computer aided diagnostic systems for managing typhoid fever: A review of diagnosis techniques. International Journal of Computer Applications, 126(6).

Ogunleye, A., Wang, Q. G., & Marwala, T. (2019). Integrated learning via randomized forests and localized regression with application to medical diagnosis. IEEE Access, 7, 18727-18733.

Rosnelly, R., & Wahyuni, L. (2018, August). Tropical Diseases Identification Using Neural Network Adaptive Resonance Theory 2. In 2018 6th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.

OM, O., & Samuel, O. W. (2013). Citation of Court Cases with Shift-Or Pattern Matching. American Journal of Information Systems, 1(1), 1-8.

Mohapatro, A., Mahendran, S. K., & Das, T. K. (2021). Clinical Decision Support System for Detection of Dengue: A Case Comparison Using AHP and Fuzzy AHP. Journal of Cases on Information Technology (JCIT), 23(4), 1-29.

Nathaniel, O., Celestine, N., Ibn, U. F., & Akpabio, I. (2017). Fuzzy Expert Framework for Diagnosis of Typhoid Fever.

Mumini, O. O., Adebisi, F. M., Edward, O. O., & Abidemi, A. S. (2016). Simulation of Stock Prediction System using Artificial Neural Networks. International Journal of Business Analytics (IJBAN), 3(3), 25-44.

Chiemeke, S. C., & Omede, E. U. (2014). MAL-TYPHO DIAGNOSIS INTELLIGENT SYSTEM (MATDIS): THE AUTO-DIAGNOSTIC RULE GENERATION ALGORITHM. system, 5(4).

Alabi, B. S., Musa, O. I., Afolabi, O. A., Aremu, S. K., & Adebola, S. O. (2011). Knowledge And Attitude Of Nigerians On Snoring As A Health Problem. The Tropical Journal of Health Sciences, 18(2), 33-37.

Lidasan, J. U., & Tagacay, M. P. (2019). MeDevice: A MobileâBased Diagnosis of Common Human Illnesses using NeuroâFuzzy Expert System.

Ibrahim, A. M., & Adekanye, S. (2021). DESIGN AND IMPLEMENTATION OF INTELLIGENT SYSTEM FOR DETECTION AND ANALYSIS OF EBOLA DISEASE. Theoretical & Applied Science, (5), 1-17.

Singh, H., Navaneeth, N. V., & Pillai, G. N. (2019, October). Multisurface Proximal SVM Based Decision Trees For Heart Disease Classification. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 13-18). IEEE.

Azeez, O. A. (2020). Application of Machine Learning to Medical Diagnosis and Hospitality.

Sultan, B., Kayani, H. U. R., Hussain, F., & ul Haq, H. B. Data-Driven Typhoid Fever Modelling And Computer Simulation.

Onyesolu, M. O., & Okpala, A. C. (2017). Improving security using a three-tier authentication for automated teller machine (ATM). International Journal of Computer Network and Information Security, 11(10), 50.

Ghafari, Z., Abazari, F., & Analoui, M. (2014, November). Atmsec: A secure protocol for atm machines. In Second Computer Science Conference on Computer and Information Technology.

Vaithyasubramanian, S., & Christy, A. (2019). ATM PIN generation-a formal mathematical model to generate PIN using regular grammar, context free grammar and recognition through finite state machine, pushdown automata. International Journal of Internet Protocol Technology, 12(1), 11-15.

Afriyie, O. K., & Arkorful, V. (2019). Enhancing Security of Automated Teller Machines Using Biometric Authentication: A Case of a Sub-Saharan University. Researchgate Publication, 9(7), 7-22.

Sharma, R. (2021). ATM Management System. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 5151-5160.

Rodriguez, M. A., Corsano, G., Vecchietti, A., & Montagna, J. M. (2018). Simultaneous optimization of production planning and inventory management of polyurethane foam plant. Optimization and Engineering, 19(1), 97-123.

Akingbade, K. F. (2018). DEVELOPMENT OF CHEMICAL PROPORTION FORMULATION MODEL FOR QUALITY CONTROL IN FLEXIBLE POLYURETHANE FOAM PRODUCTION. FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 12(2), 211-220.

Alkaht, I. J., & Al-Khatib, B. (2016). Filtering spam using several stages neural networks. Int. Rev. Comp. Softw, 11(2).

Kaur, H., & Sharma, A. (2016, October). Improved email spam classification method using integrated particle swarm optimization and decision tree. In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) (pp. 516-521). IEEE.

Said, W., & Mostafa, A. M. (2020). Towards a hybrid immune algorithm based on danger theory for database security. IEEE Access, 8, 145332-145362.

Al-Anezi, M. M. K. (2017). An Efficient AIS based Feature Extraction Techniques for Spam Detection. International Journal of Computer Applications, 975, 8887.

Awad, W. S., & Rafiq, W. M. (2020). Improving Spam Email Filtering Systems Using Data Mining Techniques. In Implementing Computational Intelligence Techniques for Security Systems Design (pp. 43-72). IGI Global.

MARINHO, D. P., & dos Santos, M. (2019). DETECÃÃO DE SPAM ATRAVÃS DE APRENDIZADO DE MÃQUINA. PROJETOS E RELATÃRIOS DE ESTÃGIOS, 1(1), 1-37.

Goodkind, A. L., Jones, B. A., & Berrens, R. P. (2020). Cryptodamages: Monetary value estimates of the air pollution and human health impacts of cryptocurrency mining. Energy Research & Social Science, 59, 101281.

Skeie, D. R. (2019). Digital currency runs. Available at SSRN 3294313.

Natalya, D., & Abd, R. A. H. (2020). The Ecosystem of Cryptocurrency as an Object of Civil Rights in BRICS Countries. BRICS Law Journal, 7(2).

Hosain, S. (2018). Cryptocurrencies: The ultimate fate.

Sabbagh, A. A. (2019). Blockchain implementations.(c2019) (Doctoral dissertation, Lebanese American University).

Andersson, A., Hansson, E., & SÃ¶dergren, T. (2021). " Det Ã¤r nÃ¥got man pratar om pÃ¥ fikarasten":-En kvalitativ studie om Bitcoin i redovisning.

Ivanova, V., Boneva, A., Doshev, Y., Ivanov, S., & Vasilev, P. (2019, November). Multifunctional Operating Station Based on Tcl/Tk and Its Applications. In 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE) (pp. 1-7). IEEE.

Ivanova, V., Batchvarov, D., Ilcheva, Z., Boneva, A., Ilchev, S., Alexandrov, A., & Andreev, R. (2019). Experimental studies of the structure of biological tissues through mechanical effects with a smart laparoscopic instrument. In MATEC Web of Conferences (Vol. 287, p. 07005). EDP Sciences.

Ilchev, S., Ilcheva, Z., Andreev, R., & Otsetova-Dudin, E. (2020, August). Computer-aided laser projection system for flexible manufacturing. In 2020 IEEE 10th International Conference on Intelligent Systems (IS) (pp. 568-573). IEEE.

Ilchev, S., Andreev, R., & Ilcheva, Z. (2020, June). Display of computer-generated vector data by a laser projector. In Proceedings of the 21st International Conference on Computer Systems and Technologies' 20 (pp. 11-18).

Ilchev, S., Alexandrov, A., & Ilcheva, Z. (2022). Design of a Laser Projection System for Intelligent Learning Environments. In Proceedings of International Conference on Data Science and Applications (pp. 89-103). Springer, Singapore.

Ilchev, S., & Ilcheva, Z. (2021, June). Laser Projection System for Continuous Operation in Manufacturing and Educational Use Cases. In International Conference on Computer Systems and Technologies' 21 (pp. 12-17).

Ivanova, V., & Boneva, A. An Experimental Module with Force Feedback Capabilities for Robot System.

Ilchev, S., Andreev, R., Ilcheva, Z., & Otsetova-Dudin, E. (2021). Software for laser projection of CAD files for the clothing industry. In IOP Conference Series: Materials Science and Engineering (Vol. 1031, No. 1, p. 012040). IOP Publishing.

Ivanova, V., Batchvarov, D., & Boneva, A. (2020). A Smart Laparoscopic Instrument with Different Applications. International Journal Bioautomation, 24(4), 403.

Nesvetaev, G.V., Kardumyan, G.S., & Kolleganov, A.V. (2019). Rational schemes for the control of concrete strength in accordance with GOST 18105. Don's Engineering Bulletin , (2 (53)).

Guazzaroni, G., & Pillai, A. S. (2019). Virtual reality (VR) for school children with autism spectrum disorder (ASD): A way of rethinking teaching and learning. In Virtual and Augmented Reality in Mental Health Treatment (pp. 141-158). IGI Global.

Aljamee, H. K., & Naeem, S. M. (2020, March). The benefits of applying project management methodology on project delay: A study in construction projects in Iraq. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012155). IOP Publishing.

CHIVINGE, J. R. Strategies used to address challenges encountered during website development.

Tesfaye, Y. (2019). Developing an Expert System for Goat Disease Diagnosis and Treatment. Journal of Emerging Technologies and Innovative Research (JETIR), 6(4).

Herrera, L. M., PÃ©rez, J. C., & OrdÃ³Ã±ez, S. J. (2019). Developing spatial mathematical skills through 3D tools: augmented reality, virtual environments and 3D printing. International Journal on Interactive Design and Manufacturing (IJIDeM), 13(4), 1385-1399.

Patkin, D., Shriki, A., & Barkai, R. (2019). Strategies applied by pre-service elementary school mathematics teachers for coping with tasks that require a mental rotation. International Journal of Science and Mathematics Education, 17(8), 1563-1584.

Seymen, B. (2019). Using Visual Research Methods To Understand Perception Of Concrete In Interior (Doctoral dissertation, Institute of Social Sciences).

Frangoudis, H. Tagged: CamOnApp.

Kongsilp, S. (2021). Kris Kolo. Perception.

GARIP, E., & SEYMEN, B. Research for evaluating perception of concrete material by using visual research methods in learning environments.

Zhang, Z., & Xiong, X. (2017). RESEARCH ON THE METHOD OF NEW GENERATION OF MULTI-SENSOR INFORMATION FUSION TECHNOLOGY TO PROMOTE THE DEVELOPMENT OF WISDOM AGRICULTURE IN âINTERNET+âMODE. FEB-FRESENIUS ENVIRONMENTAL BULLETIN, 8030.

Zheng, H. (2017). Chemical enterprise production management system based on ERP. Chemical Engineering Transactions, 62, 763-768.

Haris, M. S., Kurniawan, T. A., & Ramdani, F. (2020). Automated Features Extraction from Software Requirements Specification (SRS) Documents as The Basis of Software Product Line (SPL) Engineering. JITeCS (Journal of Information Technology and Computer Science), 5(3), 279-292.

Luckett, P., Pavelescu, E., McDonald, T., Hively, L., & Ochoa, J. (2019). Predicting state transitions in brain dynamics through spectral difference of phase-space graphs. Journal of computational neuroscience, 46(1), 91-106.

Brown, C., & Benton, R. G. (2019, November). Local gaussian process features for clinical sensor time series. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1288-1295). IEEE.

Khorev, V.S., Ishbulatov, Yu.M., Lapsheva, E.E., Kiselev, A.R., Gridnev, V.I., Bezruchko, B.P., ... & Karavaev, A. S. (2018). Diagnostics of the directional connection of the blood circulation regulation circuits by the time series of the mathematical model of the human cardiovascular system. Information management systems , (1 (92)).

Brown, C. S. (2020). Local Model Feature Transformations (Doctoral dissertation, University of South Alabama).

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Citiation Computer engineering information technology

Shaikh, M. S., Hua, C., Jatoi, M. A., Ansari, M. M., & Qader, A. A. (2021). Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system. IET Science, Measurement & Technology, 15(2), 218-231.

Srilatha, B. Research & Reviews: Journal of Pharmaceutics and Nanotechnology A Review on Fluoroquinolone: Antimicrobial Drugs. Stroke, 56, 58.

Rasib, M., Butt, M. A., Khalid, S., Abid, S., Raiz, F., Jabbar, S., & Han, K. (2021). Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles. Mathematical Problems in Engineering, 2021.

Vala, T. M., Rajput, V. N., Geem, Z. W., Pandya, K. S., & Vora, S. C. (2021). Revisiting the performance of evolutionary algorithms. Expert Systems with Applications, 175, 114819.

Shehab, M., & Abualigah, L. (2021). Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems.

Centeno, A., BolÃ­var, A., Rey, D., Arteaga, F., SÃ©ijas, C., & Almarza, Ã. D. (2019). ConfiguraciÃ³n de los parÃ¡metros de la bÃºsqueda armÃ³nica mediante lÃ³gica difusa. Revista INGENIERÃA UC, 26(1), 61-71.

Centeno, A., Vega, C., & ReyâLago, D. (2016). Metaâoptimization of harmony search. Revista Ingenier?a UC, 23(1), 81-89.

Centeno, A., Vega, C., & ReyâLago, D. (2016). MetaâoptimizaciÃ³n de la bÃºsqueda armÃ³nica. Revista INGENIERÃA UC, 23(1), 81-89.

Bhale, Y., & Joshi, R. Neuro Fuzzy Q-Learning Optimized Approach for Solving Latent Fingerprint Recognition.

Kayode, A. B., Babatunde, I. G., & Israel, H. D. (2013, December). DGM approach to network attacker and defender strategies. In 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013) (pp. 313-320). IEEE.

Ryutov, T., Orosz, M., Blythe, J., & von Winterfeldt, D. (2015, September). A game theoretic framework for modeling adversarial cyber security game among attackers, defenders, and users. In International workshop on security and trust management (pp. 274-282). Springer, Cham.

Ramachandran, K., & Stefanova, Z. (2016). Dynamic game theories in cyber security. Proc Dynam Syst Appl, 7, 303-10.

Alese, B. K., Thompson, A. F., & Oni, P. Y. (2017, June). A location privacy system in mobile network using game theory. In 2017 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA) (pp. 1-5). IEEE.

Stefanova, Z. S. (2018). Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems. University of South Florida.

Gutuleac, E., Zaporojan, S., Moraru, V., & Sclifos, A. (2019). Performance modeling of network defense in breadth systems by matrix rewriting SRN with fuzzy parameters.

Milov, O. (2019). Adaptive decision support systems for cyber security.

Munawar, S., Hamid, M., & Lodhi, S. A. (2017). Quantum Bayesian Game Modeling for Intrusion Detection. The Nucleus, 54(4), 214-218.

Milov, O. (2019). Adaptive decision support systems for cyber security.

GU?ULEAC, E., GÃ®rleanu, I., IAVORSCHI, I., & FURTUNA, A. (2017). Uncertainty Analysis of Attacker-Defender Interactions in MANET Based on Game GSPN with Intuitionistic Fuzzy Parameters.

Ahani, S., & Ghaemmaghami, S. (2015). Colour image steganography method based on sparse representation. IET Image Processing, 9(6), 496-505.

Fouad, M. M. (2017). Enhancing the imperceptibility of image steganography for information hiding. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (545-548). IEEE.

Kumar, S. (2015). A comparative study of transform based on secure image steganography. International Journal of Computer and Communication Engineering, 4(2), 107.

Naaz, K. A., Rizvi, I., & Kadam, M. M. (2015). Performance Analysis on Different Images using Reversible Data Hiding Technique and its Application. International Journal of Computer Applications, 975, 8887.

Abdelmotagally, M. F. (2017). Enhancing the Imperceptibility of Image Steganography for Information Hiding. Annals of Computer Science and Information Systems, 11, 545-548.

Phouthavong, T. (2016). Do first responders find Active911 useful?. Arkansas Tech University.

Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017). An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Applications, 68, 163-172.

Ogunleye, A., & Wang, Q. G. (2019). XGBoost model for chronic kidney disease diagnosis. IEEE/ACM transactions on computational biology and bioinformatics, 17(6), 2131-2140.

Samuel, O. W., Omisore, M. O., & Atajeromavwo, E. J. (2014). Online fuzzy based decision support system for human resource performance appraisal. Measurement, 55, 452-461.

Asogbon, M. G., Samuel, O. W., Omisore, M. O., & Awonusi, O. (2016). Enhanced neuro-fuzzy system based on genetic algorithm for medical diagnosis. J Med Diagn Meth, 5(205), 2.

Akhavan, P., & Philsoophian, M. (2018). Designing an expert fuzzy system to select the appropriate knowledge management strategy in accordance with APO model and Bloodgood KM strategies: a case study. VINE Journal of Information and Knowledge Management Systems.

Oguntimilehin, A., Adetunmbi, A. O., & Olatunji, K. A. (2014). A machine learning based clinical decision support system for diagnosis and treatment of typhoid fever. A Machine Learning Based Clinical Decision Support System for Diagnosis and Treatment of Typhoid Fever, 4(6), 1-9.

Ayo, F. E., Awotunde, J. B., Ogundokun, R. O., Folorunso, S. O., & Adekunle, A. O. (2020). A decision support system for multi-target disease diagnosis: a bioinformatics approach. Heliyon, 6(3), e03657.

Oguntimilehin, A., Adetunmbi, O., & Osho, I. (2019). Towards achieving optimal performance using stacked generalization algorithm: a case study of clinical diagnosis of malaria fever. Int. Arab J. Inf. Technol., 16(6), 1074-1081.

Oguntimilehin, A., Abiola, O. B., & Olatunji, K. A. (2015). Computer aided diagnostic systems for managing typhoid fever: A review of diagnosis techniques. International Journal of Computer Applications, 126(6).

Ogunleye, A., Wang, Q. G., & Marwala, T. (2019). Integrated learning via randomized forests and localized regression with application to medical diagnosis. IEEE Access, 7, 18727-18733.

Ogunleye, A., Wang, Q. G., & Marwala, T. (2019). Integrated learning via randomized forests and localized regression with application to medical diagnosis. IEEE Access, 7, 18727-18733.

Rosnelly, R., & Wahyuni, L. (2018, August). Tropical Diseases Identification Using Neural Network Adaptive Resonance Theory 2. In 2018 6th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.

OM, O., & Samuel, O. W. (2013). Citation of Court Cases with Shift-Or Pattern Matching. American Journal of Information Systems, 1(1), 1-8.

Mohapatro, A., Mahendran, S. K., & Das, T. K. (2021). Clinical Decision Support System for Detection of Dengue: A Case Comparison Using AHP and Fuzzy AHP. Journal of Cases on Information Technology (JCIT), 23(4), 1-29.

Nathaniel, O., Celestine, N., Ibn, U. F., & Akpabio, I. (2017). Fuzzy Expert Framework for Diagnosis of Typhoid Fever.

Mumini, O. O., Adebisi, F. M., Edward, O. O., & Abidemi, A. S. (2016). Simulation of Stock Prediction System using Artificial Neural Networks. International Journal of Business Analytics (IJBAN), 3(3), 25-44.

Chiemeke, S. C., & Omede, E. U. (2014). MAL-TYPHO DIAGNOSIS INTELLIGENT SYSTEM (MATDIS): THE AUTO-DIAGNOSTIC RULE GENERATION ALGORITHM. system, 5(4).

Alabi, B. S., Musa, O. I., Afolabi, O. A., Aremu, S. K., & Adebola, S. O. (2011). Knowledge And Attitude Of Nigerians On Snoring As A Health Problem. The Tropical Journal of Health Sciences, 18(2), 33-37.

Lidasan, J. U., & Tagacay, M. P. (2019). MeDevice: A MobileâBased Diagnosis of Common Human Illnesses using NeuroâFuzzy Expert System.

Ibrahim, A. M., & Adekanye, S. (2021). DESIGN AND IMPLEMENTATION OF INTELLIGENT SYSTEM FOR DETECTION AND ANALYSIS OF EBOLA DISEASE. Theoretical & Applied Science, (5), 1-17.

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