Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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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 7, which means every article in Journal of Computer Engineering & Information Technology has got 7 average citations.

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

  2020 2019 2018 2017 2016

Year wise published articles

27 13 21 30 25

Year wise citations received

43 60 48 46 61
Journal total citations count 316
Journal impact factor 1.26
Journal 5 years impact factor 2.22
Journal cite score 2.16
Journal h-index 7
Journal h-index since 2016 6
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.� Radioengineering� 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., & Rodríguez, 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.
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.
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 (2014–2020). 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 MASTER’S 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., Mu’azu, 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.
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 Metodología para Síntesis Óptima de Mecanismos.
Unterkalmsteiner, M., Abrahamsson, P., Wang, X., Nguyen-Duc, A., Shah, S. Q., Bajwa, S. S., ... & Yague, A. (2016). Software startups–a 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 Project–An 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). Moth–flame 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).
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.

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Citations : 316

Journal of Computer Engineering & Information Technology received 316 citations as per google scholar report

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