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Abed, Amira Hassan

Diabetes Disease Detection through Data Mining Techniques

Abstract Views :172 | PDF Views:0

Authors

Amira Hassan Abed ¹, Mona Nasr ²

Affiliations
1 Department of Information Systems center, Egyptian Organization for Standardization & Quality, EG
2 Department of Information Systems, Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 11, No 1 (2019), Pagination: 4142-4149

Abstract

Diabetes is a inveterate defect and disturbance resulted from metabolic conk out in carbohydrate metabolism thus it has occupied a globally serious health problem. In general, the detection of diabetes in early stages can greatly has significant impact on the diabetic patients treatment in which lead to drive out its relevant side effects. Machine learning is an emerging technology that providing high importance prognosis and a deeper understanding for different clustering of diseases such as diabetes. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence ‘BI’ which is a data-driven Decision Support System.

In this study, we proposed a high precision diagnostic analysis by using k-means clustering technique. In the first stage, noisy, uncertain and inconsistent data was detected and removed from data set through the preprocessing to prepare date to implement a clustering model. Then, we apply k-means technique on community health diabetes related indicators data set to cluster diabetic patients from healthy one with high accuracy and reliability results.

Keywords

Business Intelligence, Health Care, Data Mining, Data-Driven Decision Support System.

Full Text

References

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Ahmed, S., Seddawy, A. Nasr, A Proposed Framework for Detecting and Predicting Diseases through Business Intelligence Applications, International Journal of Advanced Networking and Applications (IJANA), Vol. 10, Issue 04, Jan - Feb 2019 issue, pp 3951-3957.

Soni, Y. et al (2011), Predictive Data Mining for Medical Diagnosis: An overview of heart disease Prediction, International Journal of Computer Application, pp.43-48.

Srinivas, R. (2010), Application of Data Mining techniques in healthcare & Prediction of heart attacks, International Journal on computer science and engineering, pp 250-255.

Sudhakar, K. et al (2014), Study of Heart Disease Prediction Using Data Mining, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 14, no. 3, pp.1157-1160.

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Abbas O., “Comparisons Between Data Clustering Algorithms,” The International Arab Journal of Information Technology, vol. 5, no. 3, pp. 320-325, 2008.

Khandegar Anjali. Khushbu Pawar diagnosis of diabetes mellitus using PCA, neural Network and cultural algorithm. International Journal of Digital Application Contemp Res 2017; vol.5, no.6, pp. 115-125.

Patil BM, Joshi RC, Durga Toshniwal. Hybrid prediction model for Type-2 diabetic patients. Expert Systems Applications 2010; Vol.37, no.8.pp:8102–8115.

Iyer A, Jeyalatha S, Sumbaly R. Diagnosis of diabetes using classification mining techniques. Int J Data Min Knowl Manag Process (IJDKP) 2015; Vol.5, no.1.

Gowda Karegowda Asha, Jayaram MA, Manjunath AS. Cascading K-means clustering and K-nearest neighbor classifier for categorization of diabetic patients. International Journal of Eng Adv Technology 2012; Vol.1, no.3. ISSN: 2249 – 8958.

Novakovic J, Rankov S. Classification performance using principal component analysis and different value of the ratio R. International Journal of Computer Communication Control 2011;Vol. VI, no.2, pp. :317–27. ISSN 1841-9836, E-ISSN 1841-9844.

Han J, Kamber M, Pei J. Data mining concepts and techniques. 3rd USA: Morgan Kaufmann Publishers; 2012.

The Future of Internet of Things for Anomalies Detection using Thermography

Abstract Views :143 | PDF Views:0

Authors

Amira Hassan Abed ¹, Mona Nasr , Walaa Saber ²

Affiliations
1 Egyptian Organization for Standardization &Quality, EG
2 Emirates College of Technology, Abu Dhabi, AE

Source

International Journal of Advanced Networking and Applications, Vol 11, No 3 (2019), Pagination: 4298-4304

Abstract

Abnormal temperature of human body is a natural extensive indicator of illness. Infrared thermography (IRT) is a fast, non-invasive, non-contact and passive substitution to ordinary medical thermometers for monitoring and observation human body temperature. Aside from, IRT is able to chart body surface heat remotely. Last five decades testified a stationary development in thermal imaging cameras utilization to obtain relations between the thermal physiology and surface temperature. IRT has effectively used in diagnosis and detection of breast cancer, diabetes neuropathy and peripheral vascular disorders. It has been employed to detect issues related to gynecology, dermatology, heart, neonatal physiology, and brain imaging. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field. The emergent technology known as the Internet of Things (IoT) has guided practitioners, physicians and researchers to design innovative solutions in different environments, particularly in medical and healthcare using smart sensors, computer networks and a remote server. This paper aims to propose IoT-enabled medical system enables diagnostics and detection for several medical anomalies remotely; in real-time and simultaneous depend on combination of IoT and Thermal Infrared imaging techniques. It will detect and diagnostics any abnormal and alert the user through IoT remotely and in real-time.

Keywords

Thermography, Anomaly Detection, Infrared Thermography, Imaging Techniques, Medical Systems.

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Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

Big Data with Column Oriented NOSQL Database to Overcome the Drawbacks of Relational Databases

Internet of Things (IoT) Technologies for Empowering E-Education in Digital campuses of Smart Cities

Abstract Views :207 | PDF Views:2

Authors

Amira Hassan Abed ¹

Affiliations
1 Dept. of Information Systems Center, Egyptian Organization for Standardization & Quality, EG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 2 (2021), Pagination: 4925-4930

Abstract

This article centers around the exploration related to the e-learning in the smart cities. The recent innovation, for example, Internet of Things (IoT) is quickly grown in the computerized life. Formation of the intelligent urban communities is developing with the idea of the IoT in the same time. E-residents as the fundamental component play an imperative part in building the keen urban communities. It is undeniable that another type of the resident in the smart cities can assume a fundamental part in case he/she gets satisfactory e-learning. In the computerized life, the IoT grounds in the intelligent urban communities are focused on the enhancement of the e-Leaning part by utilizing advanced communications and methods. Our work here centers around the requirement of embracing IoT techniques in campuses of intelligent cities , as well as supporting the theoretical analysis about the anticipated benefits of the smart learning and its application in the brilliant communities in a definite discussion.

Keywords

E-Leaning, E-education, Digital Campuses, Internet of Things (IoT), Smart Cities.

Full Text

References

Amira Hassan. Mona Nasr, & Walaa Saber “The Future of Internet of Things for Anomalies Detection using Thermography”, International Journal of Advanced Networking and Applications (IJANA), Volume 11 Issue 1, pp. Pages: 4142-4149 (2019).

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Marquez J., Villanueva J., Solarte Z. and Garcia A. 2021. “IoT in Education: Integration of Objects with Virtual Academic Communities,” In: New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 444. Springer, Cham, pp: 201-212.

Liu D., Huang R. and Wosinski M. 2018.“Smart Learning in Smart Cities,” Publisher Springer Singapore, ISBN 978- 981-10-4343-7, DOI:1 0.1007/978-981-10-4343-7,first Edition , 232 pages.

Bayani M. and Vílchez E. 2020. “Predictable Influence of IoT (Internet of Things) in the Higher Education”, International Journal of Information and Education Technology vol. 7, no. 12, pp: 914-920.

Bayani M., Segura A., Saenz J. and Mora B. 2019.“ Internet of Things Simulation Tools: Proposing Educational Components,” SIMUL, Greece, Athens, The 9TH International Conference on Advances in System Simulation, IARIA Conference, pp. 57-63.

del Blanco A., Serrano A., Freire M., and MartinezOrtiz I. “E-Learning Standards and Learning Analytics: can data Collection be Improved by Using Standard Data Models?”, IEEE Global Engineering Education Conference, Berlin, Germany, pp. 1255-1261, March 2019.

Caviglione L., Coccoli M., Grosso A. “A Framework for the Delivery of Contents in RFID-driven Smart Environments”, in Proc. of the IEEE International Conference on RFID-Technologies and Applications, Sitges, Spain, pp. 45-49, Sept. 2021.

Soliman M. and Elsaadany A. 2019. “Smart Immersive Education for Smart Cities with Support via Intelligent Pedagogical Agents,” 2019 IEEE 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 28 July, 10.1109/MIPRO.2019.7522247.

Dagger D., O’Connor A. and Lawless S. “Serviceoriented e-Learning Platforms: from Monolithic Systems to Flexible Services”, IEEE Internet Computing, Vol. 11, No. 3, pp. 28-35, 2007.

Amira H., Mona N., & Basant S. “The Principle Internet of Things (IoT) Security Techniques Framework Based on Seven Levels IoT’s Reference Model” Proceedings of Internet of Things—Applications and Future ITAF 2019. Springer publisher, Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 114).

Modeling Deep Neural Networks for Breast Cancer Thermography Classification: A Review Study

Abstract Views :216 | PDF Views:2

Authors

Amira Hassan Abed ¹, Essam M. Shaaban ²

Affiliations
1 Dept. of Information Systems Center, Egyptian Organization for Standardization & Quality, EG
2 Dept. of Information Systems Center, Faculty of Computers & Artificial Intelligence, BaniSwif University, EG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 2 (2021), Pagination: 4939-4946

Abstract

Building up a breast cancer screening platform is vital to encourage early "Breast cancer" detection and treatment. Proposing a screening system utilizing clinical imaging methodology that doesn't cause body tissue harm (non-obtrusive) and doesn't include actual touch is a major challenge. Thermography, a "non-intrusive" and "non-contact" malignancy screening strategy, can recognize tumors at the beginning phase significantly under determined conditions by noticing temperature circulation in the two bosoms. The thermograms can be deciphered utilizing Deep learning models, for example, "convolutional neural networks (CNN)". CNNs can naturally group bosom thermograms into classifications, for example, ordinary and up-normal. In this work, we intend to cover the most significant studies identified with the usage of deep neural networks for bosom thermogram classification. As we accept that, an overview of breast thermogram possibilities shows that the early manifestations of bosom malignant can be seen by recognizing the asymmetrical warm dispersions between the bosoms. The asymmetrical warm appropriation on bosom thermograms can be assessed utilizing a computeraided platform that depended on deep learning models.

Keywords

Breast Cancer, Convolutional Neural Networks (CNN), Thermography.

Full Text

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Deep Learning Techniques for Improving Breast Cancer Detection and Diagnosis

Abstract Views :143 | PDF Views:1

Authors

Amira Hassan Abed ¹

Affiliations
1 Department of Information Systems center Egyptian Organization for Standardization & Quality, EG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 6 (2022), Pagination: 5197-5214

Abstract

In this paper, we aim to introduce a survey on the applications of deep learning for breast cancer detection and diagnosis to provide an overview of the progress in this field. In the survey, we firstly provide an overview on deep learning and the popular architectures used for breast cancer detection and diagnosis. Especially we present four popular deep learning architectures, including convolutional neural networks, fully convolutional networks, auto encoders, and deep belief networks in the survey. Secondly, we provide a survey on the studies exploiting deep learning for breast cancer detection and diagnosis.

Keywords

Deep Learning (DL), Breast Cancer, Breast Cancer Detection.

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Business Intelligence (BI) Significant Role in Electronic Health Records - Cancer Surgeries Prediction: Case Study

Abstract Views :147 | PDF Views:1

Authors

Amira Hassan Abed ¹, Mona Nasr ²

Affiliations
1 Department of Information Systems center Egyptian Organization for Standardization & Quality, EG
2 Department of Information Systems, Faculty of Computers & Information Helwan University, EG

Source

International Journal of Advanced Networking and Applications, Vol 13, No 6 (2022), Pagination: 5220-5228

Abstract

Medical datasets reflect a great environment as they integrate analyses of structured and unstructured data that holds several benefits for medical sector. With a continues demand for implementing Electronic Health Records (EHRs), there is a relative requirement for utilizing data mining (DM) techniques to find out useful data, unknown patterns and inference rules from data stored in EHRs which help in a real-time decisions making process and prove-based practice for medical providers and experts. Business Intelligence (BI) is a technology able to process the huge data inside EHRs repository for enhancing the quality of medical delivery. DM is data processing techniques that considered a critical part of the BI platform. In this paper, we highlight significance of the BI integration with the EHRs to aid medical providers and professionals in real- time detection and prediction for several diseases. For more explanation, we apply BI technology with support of clustering technique as one of DM methods, for cancer surgeries prediction to prove the power of cooperating BI and EHRs in medical area.

Keywords

Business Intelligence (BI), Electronic Health Records, Data Mining, Cancer Surgeries Prediction.

Full Text

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