Citiation 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 | |
---|---|---|---|---|---|
Total published articles |
51 | 61 | 55 | 60 | 30 |
Research, Review articles and Editorials |
1 | 13 | 9 | 55 | 24 |
Research communications, Review communications, Editorial communications, Case reports and Commentary |
49 | 43 | 44 | 0 | 3 |
Conference proceedings |
0 | 35 | 39 | 21 | 16 |
Citations received as per Google Scholar, other indexing platforms and portals |
42 | 55 | 66 | 67 | 57 |
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 |
Kareem MH (2014) Transient Stability Assessment of Multi-Machine Power System Using Swallowtail Catastrophe Theory. J Comput Eng Inf Technol 3:1. |
Tyanev D, Petkova Y (2016) Handshake Controller for 3-alternative Conditional Transition. 17th International Conference on Computer Systems and Technologies ACM. |
Rodriguez, M. A., Montagna, J. M., Vecchietti, A., & Corsano, G. (2017). Generalized disjunctive programming model for the multi-period production planning optimization: An application in a polyurethane foam manufacturing plant. Computers & Chemical Engineering, 103, 69-80. |
Tyanev, D., & Petkova, Y. (2016, June). Handshake Controller for 3-alternative Conditional Transition. In Proceedings of the 17th International Conference on Computer Systems and Technologies 2016 (pp. 159-166). |
Idris I, Selamat A (2015) A Swarm Negative Selection Algorithm for Email Spam Detection. J Comput Eng Inf Technol 4:1 |
Dimitar T, Petkova Y (2015) Early zero 4 phase micro-pipeline controller with protection. 16th International Conference on Computer Systems and Technologies ACM. |
Bhowmik, S., Sen, S., Hori, N., Sarkar, R., & Nasipuri, M. (2017). Handwritten Devanagari numerals recognition using grid based Hausdroff distance. In Computer, Communication and Electrical Technology (pp. 15-18). CRC Press. |
Upasani, K., & Baviskar, P. V. DEVNAGRI SCRIPT RECOGNITION USING ARTIFICIAL NEURAL NETWORK CLASSIFIER. |
Agarwal M, Rawat TK (2016). VLSI Implementation of Fixed-Point Lattice Wave Digital Filters for Increased Sampling Rate.Ã?ÃÂ RadioengineeringÃ?ÃÂ 25:4. |
Hussain, K., Salleh, M. N. M., Cheng, S., & Shi, Y. (2019). Metaheuristic research: a comprehensive survey. Artificial Intelligence Review, 52(4), 2191-2233. |
Agarwal, P., & Mehta, S. (2014). Nature-inspired algorithms: state-of-art, problems and prospects. International Journal of Computer Applications, 100(14), 14-21. |
Wijayanto, A. W., Purwarianti, A., & Son, L. H. (2016). Fuzzy geographically weighted clustering using artificial bee colony: an efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population. Applied Intelligence, 44(2), 377-398. |
Morales-Castañeda, B., Zaldivar, D., Cuevas, E., Fausto, F., & RodrÃguez, A. (2020). A better balance in metaheuristic algorithms: Does it exist?. Swarm and Evolutionary Computation, 54, 100671. |
Ahangaran, M., & Ramezani, P. (2016). Harmony search algorithm: strengths and weaknesses. Journal of Computer Engineering & Information Technology, 2013. |
Tzanetos, A., & Dounias, G. (2017, August). A new metaheuristic method for optimization: sonar inspired optimization. In International Conference on Engineering Applications of Neural Networks (pp. 417-428). Springer, Cham. |
Bansal, S., Gupta, N., & Singh, A. K. (2017). Nature inspired metaheuristic algorithms to find near OGR sequences for WDM channel allocation and their performance comparison. Open Mathematics, 15(1), 520-547. |
Shukla, A. (2015, May). A modified bat algorithm for the quadratic assignment problem. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 486-490). IEEE. |
Adewumi, A. O., & Arasomwan, A. M. (2016). An improved particle swarm optimiser based on swarm success rate for global optimisation problems. Journal of Experimental & Theoretical Artificial Intelligence, 28(3), 441-483. |
Ramachandran, A., Rustum, R., & Adeloye, A. J. (2019). Review of anaerobic digestion modeling and optimization using nature-inspired techniques. Processes, 7(12), 953. |
Bindiya, T. S., & Elias, E. (2015). Design of totally multiplier-less sharp transition width tree structured filter banks for non-uniform discrete multitone system. AEU-International Journal of Electronics and Communications, 69(3), 655-665. |
Al Mamun, A., Sohel, M., Mohammad, N., Sunny, M. S. H., Dipta, D. R., & Hossain, E. (2020). A comprehensive review of the load forecasting techniques using single and hybrid predictive models. IEEE Access, 8, 134911-134939. |
Shu, T., Gao, X., Chen, S., Wang, S., Lai, K. K., & Gan, L. (2016). Weighing efficiency-robustness in supply chain disruption by multi-objective firefly algorithm. Sustainability, 8(3), 250. |
Bansal, S. (2019). A comparative study of nature-inspired metaheuristic algorithms in search of near-to-optimal Golomb rulers for the FWM crosstalk elimination in WDM systems. Applied Artificial Intelligence, 33(14), 1199-1265. |
Bansal, S. (2018). Nature-inspired-based multi-objective hybrid algorithms to find near-OGRs for optical WDM systems and their comparison. In Handbook of research on biomimicry in information retrieval and knowledge management (pp. 175-211). IGI Global. |
Soto, R., Crawford, B., Olivares, R., Taramasco, C., Figueroa, I., Gamez, A., & Paredes, F. (2018). Adaptive black hole algorithm for solving the set covering problem. Mathematical Problems in Engineering, 2018. |
Bansal, S. (2018). Nature-inspired-based multi-objective hybrid algorithms to find near-OGRs for optical WDM systems and their comparison. In Handbook of research on biomimicry in information retrieval and knowledge management (pp. 175-211). IGI Global. |
Odili, J. B., Kahar, M. N. M., & Noraziah, A. (2016). Convergence analysis of the African buffalo optimization algorithm. International Journal of Simulations: Systems, Science and Technology, 17(44), 44-41. |
Odili, J. B., & Noraziah, A. (2018). African buffalo optimization for global optimization. Current Science, 114(03), 627-636. |
Bansal, S. (2020). Performance comparison of five metaheuristic nature-inspired algorithms to find near-OGRs for WDM systems. Artificial Intelligence Review, 53(8), 5589-5635. |
Halim, A. H., & Ismail, I. (2014). Bio-Inspired optimization method: A review. NNGT Journal: International Journal of Information Systems, 1, 1-6. |
Wijayanto, A. W., & Purwarianti, A. (2014, November). Improvement design of fuzzy geo-demographic clustering using Artificial Bee Colony optimization. In 2014 International Conference on Cyber and IT Service Management (CITSM) (pp. 69-74). IEEE. |
Bindiya, T. S., & Elias, E. (2016). Meta-heuristic evolutionary algorithms for the design of optimal multiplier-less recombination filter banks. Information Sciences, 339, 31-52. |
Tzanetos, A., & Dounias, G. (2020). Sonar inspired optimization (SIO) in engineering applications. Evolving Systems, 11(3), 531-539. |
Safarinejadian, B., Bagheri, B., & Ghane, P. (2015). Fault detection in nonlinear systems based on type-2 fuzzy sets and bat optimization algorithm. Journal of Intelligent & Fuzzy Systems, 28(1), 179-187. |
Tosun, O. (2014). Cuckoo search algorithm. In Encyclopedia of Business Analytics and Optimization (pp. 558-564). IGI Global. |
Cobo, A., Llorente, I., & Luna, L. (2015). Swarm intelligence in optimal management of aquaculture farms. In Handbook of Operations Research in Agriculture and the Agri-Food Industry (pp. 221-239). Springer, New York, NY. |
Fagan, F., & Van Vuuren, J. H. (2013). A unification of the prevalent views on exploitation, exploration, intensification and diversification. International Journal of Metaheuristics, 2(3), 294-327. |
Fagan, F., & Van Vuuren, J. H. (2013). A unification of the prevalent views on exploitation, exploration, intensification and diversification. International Journal of Metaheuristics, 2(3), 294-327. |
Odili, J. B., Noraziah, A., Ambar, R., & Abd Wahab, M. H. (2018). A critical review of major nature-inspired optimization algorithms. The Eurasia proceedings of science technology engineering and mathematics, (2), 376-394. |
Odili, J. B. (2017). Implementation analysis of cuckoo search for the benchmark rosenbrock and levy test functions. Journal of Information and Communication Technology, 17(1), 17-32. |
Tzanetos, A., & Dounias, G. (2020). A comprehensive survey on the applications of swarm intelligence and bio-inspired evolutionary strategies. Machine Learning Paradigms, 337-378. |
Abdullahi, I. M., Mu'azu, M. B., Olaniyi, O. M., & Agajo, J. (2019). An investigative parameter analysis of Pastoralist Optimization Algorithm (Poa): a novel metaheuristic optimization algorithm. |
Kumar, S., Banerjee, S., & Jana, N. D. (2015). Particle swarm optimization using blended crossover operator. International Journal on Advanced Trends in Computer Science and Engineering (IJATCSE), 4(1), 06-09. |
Kumar, S., Banerjee, S., & Jana, N. D. (2015). Particle swarm optimization using blended crossover operator. International Journal on Advanced Trends in Computer Science and Engineering (IJATCSE), 4(1), 06-09. |
Khanduja, N., & Bhushan, B. (2021). Recent advances and application of metaheuristic algorithms: A survey (2014â2020). Metaheuristic and Evolutionary Computation: Algorithms and Applications, 207-228. |
Wijayanto, A. W. (2015). Improvement Of Fuzzy Geo-Demographic Clustering Using Metaheuristic Optimization On Indonesia Population Census. Institut Teknologi Bandung. |
Odili, J. B., & Fatokun, J. O. (2020, March). The mathematical model, implementation and the parameter-tuning of the African buffalo optimization algorithm. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) (pp. 1-8). IEEE. |
Yang, X. S. (2013). Engineering optimization and industrial applications. In Surrogate-Based Modeling and Optimization (pp. 393-412). Springer, New York, NY. |
Kader, M. A., & Zamli, K. Z. (2020, February). Adopting Jaya Algorithm for Team Formation Problem. In Proceedings of the 2020 9th International Conference on Software and Computer Applications (pp. 62-66). |
Malik, S., Sharma, K., & Bala, M. (2021). Reliability analysis and modeling of green computing based software systems. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 14(4), 1060-1071. |
Swayamsiddha, S., Singhal, C., & Roy, R. (2018). Nature-inspired-algorithms-based cellular location management: scope and applications. In Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms (pp. 346-362). IGI Global. |
Sharma, K., & Bala, M. (2019). A Quantitative Testing Effort Estimate for Reliability Assessment of Multi Release Open Source Software Systems. Journal of Computational and Theoretical Nanoscience, 16(12), 5089-5098. |
Sachan, R. K., & Kushwaha, D. S. (2021). Inspirations from Nature for Meta-Heuristic Algorithms: A Survey. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 14(6), 1706-1718. |
Murugasamy, K., & Murugasamy, K. (2016). Hybrid clustering using firefly optimization and fuzzy c-means algorithm. Circuits and Systems, 7(09), 2339. |
Abdullahi, I. M., & Muhammad, H. K. (2019). Teaching-Learning-Based Optimization (TLBO) Algorithm for Enhanced Curriculum Evaluation: A Feasibility Study. |
Hussain, K., Salleh, M. N. M., Cheng, S., Shi, Y., & Naseem, R. (2018). Computer and Information Sciences. |
Senjyu, T., Alkhalaf, S., & Mohamed, A. A. Nature-Inspired Algorithms Applications to Power System Optimization. |
WAHID, D. N. STATUS CONFIRMATION FOR MASTERâS THESIS A GENETIC SIMPLIFIED SWARM ALGORITHM FOR OPTIMIZING n-CITIES OPEN LOOP TRAVELLING SALESMAN PROBLEM ACADEMIC SESSION: 2015/2016. |
Chand, V., Prasad, A., Chaudhary, K., Sharma, B., & Chand, S. (2020, December). A face-off-classical and heuristic-based path planning approaches. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE. |
Swayamsiddha, S. (2020). Bio-inspired algorithms: principles, implementation, and applications to wireless communication. In Nature-Inspired Computation and Swarm Intelligence (pp. 49-63). Academic Press. |
Audee, S. Y., Muâazu, M. B., Man-Yahaya, S., Haruna, Z., Tijani, S. A., & Oyibo, P. (2019). Development of a Dynamic Cuckoo Search Algorithm. Covenant Journal of Informatics and Communication Technology, 7(2). |
Moyo, E. (2018). Accelerated cooperative co-evolution on multi-core architectures (Master's thesis, Faculty of Science). |
AZIZ, N. A. A. (2017). An adaptively switching iteration strategy for population based metaheuristics (Doctoral dissertation, UNIVERSITY OF MALAYA KUALA LUMPUR). |
Cuevas, E., Diaz, P., & Camarena, O. (2021). Metaheuristic Computation: A Performance Perspective (Vol. 195, pp. 1-269). Springer. |
Odili, J. B., & Romli, A. (2017, May). Implementation evaluation of Cuckoo search for the benchmark Rosenbrock test function. In 2017 8th International Conference on Information Technology (ICIT) (pp. 334-337). IEEE. |
Bansal, S. (2021). Nature-Inspired Hybrid Multi-objective Optimization Algorithms in Search of Near-OGRs to Eliminate FWM Noise Signals in Optical WDM Systems and their Performance Comparison. Journal of The Institution of Engineers (India): Series B, 1-27. |
Dhruve, K., & Kaur, D. (2021, August). Nature-Inspired Algorithms for Image Enhancement. In 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 101-104). IEEE. |
Thakur, K., & Kumar, G. (2021). Nature Inspired Techniques and Applications in Intrusion Detection Systems: Recent Progress and Updated Perspective. Archives of Computational Methods in Engineering, 28(4), 2897-2919. |
Tessaro Lunardi, W. (2020). A Real-World Flexible Job Shop Scheduling Problem With Sequencing Flexibility: Mathematical Programming, Constraint Programming, and Metaheuristics (Doctoral dissertation, University of Luxembourg,? Luxembourg,?? Luxembourg). |
Cuevas, E., Diaz, P., & Camarena, O. (2021). Experimental Analysis Between Exploration and Exploitation. In Metaheuristic Computation: A Performance Perspective (pp. 249-269). Springer, Cham. |
Khanduja, N., & Bhushan, B. (2020). Recent Advances and Application of Metaheuristic Algorithms: A Survey. Metaheuristic and Evolutionary Computation: Algorithms and Applications, 916, 207. |
Kumar, A. (2021). A Survey on Metaheuristics-Based Task Scheduling. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) (pp. 859-870). Springer, Singapore. |
Ajala, E. O., Ehinmowo, A. B., Ajala, M. A., Ohiro, O. A., Aderibigbe, F. A., & Ajao, A. O. (2022). Optimisation of CaO-Al2O3-SiO2-CaSO4-based catalysts performance for methanolysis of waste lard for biodiesel production using response surface methodology and meta-heuristic algorithms. Fuel Processing Technology, 226, 107066. |
Fagan, F. (2014). A qualitative model of evolutionary algorithms (Doctoral dissertation, Stellenbosch: Stellenbosch University). |
Nand, R., Sharma, B. N., & Chaudhary, K. (2021). Stepping ahead firefly algorithm and hybridization with evolution strategy for global optimization problems. Applied Soft Computing, 107517. |
Morales Benhumea, M. Propuesta de MetodologÃa para SÃntesis Ãptima de Mecanismos. |
Unterkalmsteiner, M., Abrahamsson, P., Wang, X., Nguyen-Duc, A., Shah, S. Q., Bajwa, S. S., ... & Yague, A. (2016). Software startupsâa research agenda. e-Informatica Software Engineering Journal, 10(1), 89-123. |
Scheerer, A., Hildenbrand, T., & Kude, T. (2014, January). Coordination in large-scale agile software development: A multiteam systems perspective. In 2014 47th Hawaii international conference on system sciences (pp. 4780-4788). IEEE. |
Nguyen-Duc, A., Seppänen, P., & Abrahamsson, P. (2015, August). Hunter-gatherer cycle: a conceptual model of the evolution of software startups. In Proceedings of the 2015 International Conference on Software and System Process (pp. 199-203). |
Souza, R., Rocha, L., Silva, F., & Machado, I. (2019, September). Investigating agile practices in software startups. In Proceedings of the XXXIII Brazilian Symposium on Software Engineering (pp. 317-321). |
Rikkilä, J., Wang, X., & Abrahamsson, P. (2013, December). Agile ProjectâAn Oxymoron? Proposing an Unproject Leadership Model for Complex Space. In International Conference on Lean Enterprise Software and Systems (pp. 194-209). Springer, Berlin, Heidelberg. |
Joosten, A. (2018). Unorder and the applicablity of agile. University of Amsterdam Faculty of Science, Amsterdam. |
Arellano, D., Schaller, U. M., Rauh, R., Helzle, V., Spicker, M., & Deussen, O. (2015, August). On the Trail of Facial Processing in Autism Spectrum Disorders. In International Conference on Intelligent Virtual Agents (pp. 432-441). Springer, Cham. |
Christensen, H. L., Turner, R. E., Hill, S. I., & Godsill, S. J. (2013). Rebuilding the limit order book: sequential Bayesian inference on hidden states. Quantitative Finance, 13(11), 1779-1799. |
McGehee, C. C. (2013). Dynamics of an ocean energy harvester (Doctoral dissertation, Ph. D. thesis, Duke University). |
Swingler, A. J. (2017). An Econophysics Approach to Short Time-Scale Dynamics of the Equities Markets (Doctoral dissertation, Duke University). |
Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic algorithms: A comprehensive review. Computational intelligence for multimedia big data on the cloud with engineering applications, 185-231. |
Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., & Khasawneh, A. M. (2020). Mothâflame optimization algorithm: variants and applications. Neural Computing and Applications, 32(14), 9859-9884. |
George, J. T., & Elias, E. (2014). Reconfigurable channel filtering and digital down conversion in optimal CSD space for software defined radio. AEU-International Journal of Electronics and Communications, 68(4), 312-321. |
Bindiya, T. S., & Elias, E. (2015). Design of totally multiplier-less sharp transition width tree structured filter banks for non-uniform discrete multitone system. AEU-International Journal of Electronics and Communications, 69(3), 655-665. |
Bindiya, T. S., & Elias, E. (2016). Meta-heuristic evolutionary algorithms for the design of optimal multiplier-less recombination filter banks. Information Sciences, 339, 31-52. |
Kim, J. H., Lee, H. M., & Yoo, D. G. (2016). Investigating the convergence characteristics of harmony search. In Harmony Search Algorithm (pp. 3-10). Springer, Berlin, Heidelberg. |
Bertaska, I. R. (2016). Intelligent supervisory switching control of unmanned surface vehicles. Florida Atlantic University. |
Kim, J. H., Lee, H. M., Jung, D., & Sadollah, A. (2016). Performance measures of metaheuristic algorithms. In Harmony search algorithm (pp. 11-17). Springer, Berlin, Heidelberg. |
Lee, H. M., Jung, D., Sadollah, A., & Kim, J. H. (2020). Performance comparison of metaheuristic algorithms using a modified Gaussian fitness landscape generator. Soft Computing, 24(10), 7383-7393. |
Yoo, D. G., Kim, Y. H., Kim, Y. D., Cho, J., & Kim, J. H. (2016). Development of optimal pipe size design tool for irrigation systems and its application to Saemangeum reclamation area. Irrigation and Drainage, 65, 58-68. |
Bentlemsan, K., Bennouar, D., Tamzalit, D., & Hidouci, K. W. (2020). A hybrid re-composition based on components and web services. International Journal of Computers and Applications, 42(5), 449-462. |
BETKA, A. (2019). Estimation de mouvement par les techniques métaheuristiques (Doctoral dissertation, Université Mohamed Khider-Biskra). |
Hashemi, P., & Eghtedarpour, N. (2019). An Improved Harmony Search Algorithm to Solve Dynamic Economic Load Dispatch Problem in Presence of FACTS Devices. In Fundamental Research in Electrical Engineering (pp. 667-682). Springer, Singapore. |
Lee, H. M., Jung, D., Sadollah, A., & Kim, J. H. (2016). Test problem generation using a modified Gaussian fitness landscape generator. In The 12th international conference on hydroinformatics (HIC 2016). |