Citations Report - Journal of Information Technology and Software

Journal of Information Technology & Software Engineering : Citations & Metrics Report

Articles published in Journal of Information Technology & Software Engineering have been cited by esteemed scholars and scientists all around the world. Journal of Information Technology & Software Engineering has got h-index 8, which means every article in Journal of Information Technology & Software Engineering has got 8 average citations.

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

	2024	2023	2022	2021	2020	2019	2018	2017	2016	2015	2014	2013	2012	2011
Total published articles	60	56	32	58	13	13	26	24	29	26	14	26	23	8
Research, Review articles and Editorials	50	5	6	31	7	6	22	22	25	25	11	25	22	8
Research communications, Review communications, Editorial communications, Case reports and Commentary	0	51	26	27	6	7	4	2	4	1	3	1	1	0
Conference proceedings	0	0	9	0	0	0	0	0	58	62	0	0	0	0
Citations received as per Google Scholar, other indexing platforms and portals	114	184	58	47	50	34	21	89	70	51	38	18	4	0

Journal total citations count	1526
Journal impact factor	1.16
Journal 5 years impact factor	1.47
Journal cite score	1.29
Journal h-index	8
Journal h-index since 2019	8

Important citations (347)

Catania lj, nicolitz e. artificial intelligence and its applications in vision and eye care. adv in ophthalmol optom.2018;3(1):21-38. View at Publisher View at Google Scholar
ErtaÅŸ g. neural network based classification of melanocytic lesions in dermoscopy: role of input vector encoding. electrica. 2018;18(2):242-8. View at Publisher View at Google Scholar
Hegde rm, kenchannavar hh. a survey on predicting resident intentions using contextual modalities in smart home. int j adv pervasive and ubiquitous comp.2019;11(4):44-59. View at Publisher View at Google Scholar
Al-akam r, paulus d. comparison of different machine learning models on feature extraction for human activity recognition from rgb-depth datasets.int conf mach vision.2019;11041:1104128. View at Publisher View at Google Scholar
Akila k, chitrakala s. highly refined human action recognition model to handle intraclass variability & interclass similarity. multimedia tools and applications.2019;1-9. View at Publisher View at Google Scholar
Nourani h, shihab e, sarbishei o. the impact of data reduction on wearable-based human activity recognition.ieee int conf pervasive comput commun workshops.2019;89-94. View at Publisher View at Google Scholar
Badawi aa, al-kabbany a, shaban h. multimodal human activity recognition from wearable inertial sensors using machine learning.ieee-embs conf biomed eng sci.2018;402-407. View at Publisher View at Google Scholar
Loreti d, chesani f, mello p, roffia l, antoniazzi f, cinotti ts, paolini g, masotti d, costanzo a. complex reactive event processing for assisted living: the habitat project case study. expert systems with applications.2019;126:200-17. View at Publisher View at Google Scholar
Manzi a, moschetti a, limosani r, fiorini l, cavallo f. enhancing activity recognition of self-localized robot through depth camera and wearable sensors. ieee sen j.2018;18(22):9324-31. View at Publisher View at Google Scholar
Al-akam r, al-darraji s, paulus d. human action recognition from rgbd videos based on retina model and local binary pattern features. View at Publisher View at Google Scholar
Anzer a, tabaza ha, ali j. predicting academic performance of students in uae using data mining techniques. int conf adv comp commun eng(icacce).2018;179-183. View at Publisher View at Google Scholar
Tanuar e, heryadi y, abbas bs, gaol fl. using machine learning techniques to earlier predict student's performance. indonesian association for pattern recognition int conf(inapr).2018;85-89. View at Publisher View at Google Scholar
Mahboob k, ali sa, khan du, ali f. a comparative study of engineering students pedagogical progress. int j adv comp sci app.2018;9(6):323-31. View at Publisher View at Google Scholar
Francis bk, babu ss. predicting academic performance of students using a hybrid data mining approach. j medi sys.2019;43(6):162. View at Publisher View at Google Scholar
Cyril a, mulangi rh, george v. data processing methodology for big data in public transportation. int j pure appl math.2018;120(6):649-60. View at Publisher View at Google Scholar
Nasr m. self-protected mobile agent paradigm for ddos threats using block chain technology.int j adv network appl.2019;10(6):4070-5. View at Publisher View at Google Scholar
Rusli r, hashim h, yunus mm, zakaria ny, norman h. leveraging the use of social media in teaching writing: a qualitative investigation among esl pre-service teachers. int j interac mobile tech(ijim).2019;13(11):85-94. View at Publisher View at Google Scholar
Habibi a, mukminin a, pratama r, harja h. predicting factors affecting intention to use web 2.0 in learning: evidence from science education. j baltic sci edu.2019;18(4):595-606. View at Publisher View at Google Scholar
Hassan h, halim na, ahmad r. the reasons narcissists portrayed themselves on facebook. inproceeding: 2nd int conf soc sci, human tech(icsht 2018).2018;9. View at Publisher View at Google Scholar
Singh k, kalyan p. attitude of undergraduate students towards use of social networking sites in relation to emotional maturity. indian j public health res develop.20191;10(6). View at Publisher View at Google Scholar
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Journal of Information Technology & Software EngineeringOpen Access

Journal of Information Technology & Software Engineering : Citations & Metrics Report

Important citations (347)

Catania lj, nicolitz e. artificial intelligence and its applications in vision and eye care. adv in ophthalmol optom.2018;3(1):21-38.

ErtaÅŸ g. neural network based classification of melanocytic lesions in dermoscopy: role of input vector encoding. electrica. 2018;18(2):242-8.

Hegde rm, kenchannavar hh. a survey on predicting resident intentions using contextual modalities in smart home. int j adv pervasive and ubiquitous comp.2019;11(4):44-59.

Al-akam r, paulus d. comparison of different machine learning models on feature extraction for human activity recognition from rgb-depth datasets.int conf mach vision.2019;11041:1104128.

Akila k, chitrakala s. highly refined human action recognition model to handle intraclass variability & interclass similarity. multimedia tools and applications.2019;1-9.

Nourani h, shihab e, sarbishei o. the impact of data reduction on wearable-based human activity recognition.ieee int conf pervasive comput commun workshops.2019;89-94.

Badawi aa, al-kabbany a, shaban h. multimodal human activity recognition from wearable inertial sensors using machine learning.ieee-embs conf biomed eng sci.2018;402-407.

Loreti d, chesani f, mello p, roffia l, antoniazzi f, cinotti ts, paolini g, masotti d, costanzo a. complex reactive event processing for assisted living: the habitat project case study. expert systems with applications.2019;126:200-17.

Manzi a, moschetti a, limosani r, fiorini l, cavallo f. enhancing activity recognition of self-localized robot through depth camera and wearable sensors. ieee sen j.2018;18(22):9324-31.

Al-akam r, al-darraji s, paulus d. human action recognition from rgbd videos based on retina model and local binary pattern features.

Anzer a, tabaza ha, ali j. predicting academic performance of students in uae using data mining techniques. int conf adv comp commun eng(icacce).2018;179-183.

Tanuar e, heryadi y, abbas bs, gaol fl. using machine learning techniques to earlier predict student's performance. indonesian association for pattern recognition int conf(inapr).2018;85-89.

Mahboob k, ali sa, khan du, ali f. a comparative study of engineering students pedagogical progress. int j adv comp sci app.2018;9(6):323-31.

Francis bk, babu ss. predicting academic performance of students using a hybrid data mining approach. j medi sys.2019;43(6):162.

Cyril a, mulangi rh, george v. data processing methodology for big data in public transportation. int j pure appl math.2018;120(6):649-60.

Nasr m. self-protected mobile agent paradigm for ddos threats using block chain technology.int j adv network appl.2019;10(6):4070-5.

Rusli r, hashim h, yunus mm, zakaria ny, norman h. leveraging the use of social media in teaching writing: a qualitative investigation among esl pre-service teachers. int j interac mobile tech(ijim).2019;13(11):85-94.

Habibi a, mukminin a, pratama r, harja h. predicting factors affecting intention to use web 2.0 in learning: evidence from science education. j baltic sci edu.2019;18(4):595-606.

Hassan h, halim na, ahmad r. the reasons narcissists portrayed themselves on facebook. inproceeding: 2nd int conf soc sci, human tech(icsht 2018).2018;9.

Singh k, kalyan p. attitude of undergraduate students towards use of social networking sites in relation to emotional maturity. indian j public health res develop.20191;10(6).

Journal of Information Technology & Software Engineering
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