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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu dan Teknologi Kelautan Tropis Jurnal Sains dan Inovasi Perikanan/Journal of Fishery and Innovation (JSIPi) JURNAL SPASIAL Jurnal Sains dan Teknologi Pangan Jurnal Manajemen Sumber Daya Perairan Jurnal Sosial Ekonomi Perikanan Aquasains : Jurnal Ilmu Perikanan dan Sumberdaya Perairan Jurnal Pengabdian Magister Pendidikan IPA Jurnal Gema Ngabdi Jurnal Abdi Insani INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Jurnal Bilqolam Pendidikan Islam Coastal and Marine Journal Jurnal Laut Pulau PekaBuana : Jurnal Ilmiah Teknologi dan Manajemen Perikanan Tangkap Bina Bahari : Jurnal Pengabdian Kepada Masyarakat
Ahmad Mustafa
Fakultas Perikanan Dan Ilmu Kelautan Universitas Haluoleo Jl. HEA Mokodompit Kampus Bumi Tridharma Anduonohu Kendari
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Transportation Other

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Journal : International Journal of Electrical and Computer Engineering

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Explaining transfer learning models for the detection of COVID-19 on X-ray lung images Odeh, Abd Al-Rahman; Mustafa, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4542-4550

Abstract

Amidst the coronavirus disease 2019 (COVID-19) pandemic, researchers are exploring innovative approaches to enhance diagnostic accuracy. One avenue is utilizing deep learning models to analyze lung X-ray images for COVID-19 diagnosis, complementing existing tests like reverse transcription polymerase chain reaction (RT-PCR). However, trusting these models, often viewed as black boxes, presents a challenge. To address this, six explainable artificial intelligence (XAI) techniques: local interpretable model agnostic explanations (LIME), Shapley additive explanations (SHAP), integrated gradients, smooth-grad, gradient-weighted class activation mapping (Grad-CAM), and Layer-CAM are applied to interpret four transfer learning models. These models: VGG16, ResNet50, InceptionV3, and DenseNet121 are analyzed to understand their workings and the rationale behind their predictions. Validating the results with medical experts poses difficulties due to time and resource constraints, alongside the scarcity of annotated X-ray datasets. To address this, a voting mechanism employing different XAI methods across various models is proposed. This approach highlights regions of lung infection, potentially reducing individual model biases stemming from their structures. If successful, this research could pave the way for an automated system for annotating infection regions, bolstering confidence in predictions and aiding in the development of more effective diagnostic tools for COVID-19.
A semantic similarity search engine for movies Mustafa, Ahmad; Mheidat, Hammam; Shatnawi, Adam
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7137-7144

Abstract

Semantic similarity has been gaining traction in the field of natural language processing. It is a measure of how similar two pieces of text are in terms of their meaning. It can be used to improve search engine results. We propose a deep learning-based approach to build a semantic similarity search engine for movies based on a movie summary. Filmmakers can gain insight into audience preferences and trends, allowing them to create more engaging and successful films. The dataset used in this study was gathered from internet movie database (IMDb), it includes movie summaries along with their corresponding name movies. The test dataset was generated using ChatGPT to be very close to human input. The universal sentence encoder (USE) model presented promising results in semantic similarity, the model results show that for the top 5 similar movies, the model returned 176 out of 300 movies (58.6%). For the top 10 similar movies, the model returned 211 out of 300 movies (70.3%). Additionally, for the top 15 similar movies, the model returned 229 out of 300 movies (76.3%). And, for the top 20 similar movies, the model returned 249 of 300 movies (83%). This method can be applied to movie recommendation systems or to organize films in a collection automatically.
Co-Authors . Bahtiar A.A. Ketut Agung Cahyawan W Abdul Razak, La Ode Abdullah Abdullah Agusliana M, Sri Eka Alwin, . Andi Irwan Nur, Andi Irwan Arami, Hasnia Arami, Hasnia Asnani Asnani Asriyana, . Bahtiar, . Balubi, Abdul Muis Baru Sadarun Budy Wiryawan Bujang, Azis Dedy Oetama Desriani . Dwi Budi Santoso Erni, Rosma Faisal Danu Tuheteru Fajriah Fajriah Halili Halili Hendri, Laode Hestiana, . Hidayat Hidayat Husna I Gusti Bagus Wiksuana Ibrahim, Mohammad Nuh Indah Fitria, Indah Indrayani INDRAYANI INDRAYANI Irsan, La Ode Muhammad Isamu, Kobajashi Togo Kaduk, Okta Santika Kamri, Syamsul Kasim, Ma'ruf La Anadi, La La Ode Wahidin La Sara M. Iqbal Mheidat, Hammam Muhammad Irsan, La Ode Muhammad Rivai Muhammad Trial Fiar Erawan Muis Muis, Muis Muslim Tadjuddah Muzuni, Muzuni Nanto, . Nasiru, Wa Ode Asrina Naslina Alimina Neni, Neni Nugroho, Tanto Nur, Astati Nurdiansyah, . Nurmila, Wa Ode Odeh, Abd Al-Rahman Ogbonna, David N. Pariakan, Arman Pratikino, A. Ginong Purnama, Muh. Fajar Ramadhan, Bertha Zulmiadi Ramlah Ramlah Rasyid, M. Abdul Raup, Syahril Abd. Saenuddin Safitri, Dita Sah, Firman Sarini Yusuf Sarnia Sarnia, Sarnia Shatnawi, Adam Siahaan, Abdurrahman Sidiq, Azwar Sudarno Sudarno Syarif, Syahdan Tadjuda, Muslim Tadjudah, Muslim Taher, Muh Ikhsan Umar Umar Untu, La Wardha Jalil Wellem H. Muskita Yanglera, Abdullah Yasidi, Farid