{"id":569,"date":"2022-07-02T08:52:06","date_gmt":"2022-07-02T08:52:06","guid":{"rendered":"https:\/\/deeplearningindaba.com\/2022\/?page_id=569"},"modified":"2022-09-02T19:27:57","modified_gmt":"2022-09-02T19:27:57","slug":"posters","status":"publish","type":"page","link":"https:\/\/deeplearningindaba.com\/2022\/indaba\/posters\/","title":{"rendered":"Posters and Demos"},"content":{"rendered":"

This is the current list of posters and demos that will be presented on the Africa Research day. This page will be updated regularly.<\/p>\n

Poster Presentations<\/h3>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Poster #<\/strong><\/td>\nPresenter(s)<\/strong><\/td>\nPoster Title<\/strong><\/td>\nLink to Poster<\/strong><\/td>\n<\/tr>\n
 <\/td>\n <\/td>\nPOSTER SESSION 1<\/strong><\/span><\/td>\n <\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n <\/td>\n <\/td>\n<\/tr>\n
1<\/td>\nAbderrahman Aouadi<\/td>\nDetection and characterization of breast tumors from medical images by machine learning<\/td>\n <\/td>\n<\/tr>\n
2<\/td>\nAbdulganiyu Jimoh<\/td>\nApplication of Artificial Intelligence (AI) and Collective Intelligence (CI) for Diagnosis of Breast Cancer<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Abdulganiyu_Jimoh_6876_poster.pdf<\/a><\/td>\n<\/tr>\n
3<\/td>\nAbdulhameed Dere<\/td>\nMachine Learning in Diagnosis of Mental Illness: A study of University of Ilorin Medical Students<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Abdulhameed _Dere_5438_poster.pdf<\/a><\/td>\n<\/tr>\n
4<\/td>\nAbrar Elidrisi<\/td>\nRemote sensing-based crop classification for sustainable agriculture in Sudan<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Abrar_Elidrisi_4475_poster.pdf<\/a><\/td>\n<\/tr>\n
5<\/td>\nAdeyinka Abiodun<\/td>\nDevelopment of a Modified Likelihood Ratio Model for Multi-Modal Biometric Identification in Forensic Science<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Adeyinka_Abiodun_5217_poster.pdf<\/a><\/td>\n<\/tr>\n
6<\/td>\nAhmed Alhassan<\/td>\nAutonomous Cellular Network Optimization<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ahmed_Alhassan_1926_poster.pdf<\/a><\/td>\n<\/tr>\n
7<\/td>\nAJAGBE, Sunday Adeola<\/td>\nDevelopment of Multilingual Corpora in  Medical Domain Using Neural Machine Translation<\/td>\nhttps:\/\/drive.google.com\/open?id=1S-LoZ_IUm1SLI2046zFiTNLmyj_pcwfs<\/a><\/td>\n<\/tr>\n
8<\/td>\nAjala, Marvellous O<\/td>\nApplication of Computer Vision for the Microscopical classification of starches for pharmaceutical formulations<\/td>\nhttps:\/\/drive.google.com\/open?id=1soT-URSBK61g3O3xnyde_6JR78CU7-B9<\/a><\/td>\n<\/tr>\n
9<\/td>\nAletta S E Nortje<\/td>\nVisually prompted keyword localisation<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Aletta S E_Nortje_1388_poster.pdf<\/a><\/td>\n<\/tr>\n
10<\/td>\nAli Hussein<\/td>\nNAS-zero: Neural Architecture Search from scratch<\/td>\n <\/td>\n<\/tr>\n
11<\/td>\nAMEL LAIDI<\/td>\nA GAN Solution for Data imbalance in Atherosclerosis Screening<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/AMEL_LAIDI_7874_poster.pdf<\/a><\/td>\n<\/tr>\n
12<\/td>\nAmin KHOUANI<\/td>\nA NEW METHOD BASED ON A DEEP LEARNING MODEL FOR AUTOMATIC RECOGNITION OF CELLS IN CYTOLOGICAL IMAGES<\/td>\nhttps:\/\/drive.google.com\/open?id=11Qpe0lVC2Shixjv3nOXDtQrCy0lLHzvQ<\/a><\/td>\n<\/tr>\n
13<\/td>\nAmmar Khairi<\/td>\nRemote sensing-based crop classification for sustainable agriculture in Sudan<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ammar_Khairi_2093_poster.pdf<\/a><\/td>\n<\/tr>\n
14<\/td>\nAnne Osio<\/td>\nDetection of degraded Acacia using Deep Learning on UAV  Imageries.<\/td>\n <\/td>\n<\/tr>\n
15<\/td>\nAriane HOUETOHOSSOU<\/td>\nDeep Learning methods for biotic and abiotic stresses detection in fruits and vegetables: state of the art and perspectives<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ariane_HOUETOHOSSOU_5900_poster.pdf<\/a><\/td>\n<\/tr>\n
16<\/td>\nArnol Fokam<\/td>\nEffects of Annotations\u2019 Density on Named Entity Recognition Models\u2019 Performance in the Context of African Languages<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Arnol _Fokam_8192_poster.pdf<\/a><\/td>\n<\/tr>\n
17<\/td>\nAshraf Elneima<\/td>\nAdversarial Text-to-Speech for low-resource languages<\/td>\nhttps:\/\/drive.google.com\/file\/d\/1jYF_YYmUxP0mE1jnQnvgQKoVut07-IYl\/view?usp=sharing<\/a><\/td>\n<\/tr>\n
18<\/td>\nAsmelash Hadgu<\/td>\nLesan \u2013 Machine Translation for Low Resource Languages<\/td>\n <\/td>\n<\/tr>\n
19<\/td>\nassala benmalek<\/td>\nFire detection system with cameras and drones<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/assala_benmalek_6554_poster.pdf<\/a><\/td>\n<\/tr>\n
20<\/td>\nBelayneh Endalamaw<\/td>\nDiabets Disease Prediction Model Deployment onHeroku-based Cloud Computing Platforms using Homogeneous Ensemble Machine Learning Algorithms<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Belayneh_Endalamaw_6961_poster.pdf<\/a><\/td>\n<\/tr>\n
21<\/td>\nBELONA MARY SONNA MOMO<\/td>\nExplainable AI with propositional Logic<\/td>\n <\/td>\n<\/tr>\n
22<\/td>\nBerber Kramer<\/td>\nDevelopment Engineering (DevEng): Publish applications of deep learning for global development<\/td>\nhttps:\/\/www.dropbox.com\/s\/u9nr5ghqnhrlcm0\/Poster%20DevEng%20Deep%20Learning%20Indaba.pdf?dl=0<\/a><\/td>\n<\/tr>\n
23<\/td>\nBlessing Sibanda<\/td>\nVegetable plant disease classification using mobile convolutional neural network on offline smartphones<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Blessing_Sibanda_4013_poster.pdf<\/a><\/td>\n<\/tr>\n
24<\/td>\nBolaji Akorede<\/td>\nAge related changes in prostaglandins e2, nitric oxide, vascular endothelial growth factor in the healing of acetic acids induced ulcer<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Bolaji _Akorede _7068_poster.pdf<\/a><\/td>\n<\/tr>\n
25<\/td>\nBoluwaji Akinnuwesi<\/td>\nRe-Engineering Financial Inclusiveness in the Kingdom of Eswatini using Machine Learning Engineering (MLe) Algorithms<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Boluwaji_Akinnuwesi_5813_poster.pdf<\/a><\/td>\n<\/tr>\n
26<\/td>\nBunmi Akinremi<\/td>\nDetection and Analysis of Plastic Waste: A Tiling Approach<\/td>\nhttps:\/\/drive.google.com\/open?id=18FsjBpDUhoscw8T5oRCNCRia6-T4LNVa<\/a><\/td>\n<\/tr>\n
27<\/td>\nMbangula Lameck Amugongo<\/td>\nTowards trustworthy AI-based algorithms in healthcare: A case of medical images.<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Mbangula Lameck _Amugongo_8134_poster.pdf<\/a><\/td>\n<\/tr>\n
28<\/td>\nSebastien Diarra<\/td>\nGrassroots African ML : Tools and Methods for Enabling
the Participation of Bambaraphone Communities<\/td>\n		https:\/\/drive.google.com\/open?id=1MqzgrAZkny3On97zl4RaLZugcA1kFNGG<\/a><\/td>\n<\/tr>\n
29<\/td>\nVolviane Saphir MFOGO<\/td>\nCO-ATTENTION MECHANISM WITH MULTI-MODAL FACTORIZED BILINEAR POOLING FOR MEDICAL IMAGE QUESTION ANSWERING<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Volviane Saphir_MFOGO_3816_poster.pdf<\/a><\/td>\n<\/tr>\n
30<\/td>\nCaleb Robinson<\/td>\nGeospatial Learning at Microsoft AI for Good<\/td>\n <\/td>\n<\/tr>\n
31<\/td>\nCelia Cintas<\/td>\nPattern Detection in the Activation Space for Identifying Out of Distribution Samples<\/td>\n <\/td>\n<\/tr>\n
32<\/td>\nChristopher Fourie<\/td>\nImpact of Noise on Learned Value Functions at Depth in CoAgent Networks for Neural Network Credit Assignment<\/td>\n <\/td>\n<\/tr>\n
33<\/td>\nClaire Babirye<\/td>\nInferring Crop Pests and Diseases from Imagery Soil Data and Soil Properties<\/td>\nhttps:\/\/drive.google.com\/open?id=1_18velWgxpKo1TOc3QG982LI4cQZLQIj<\/a><\/td>\n<\/tr>\n
34<\/td>\nClaude Formanek<\/td>\nOff-The-Grid Multi-Agent Reinforcement Learning<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Claude_Formanek_8151_poster.pdf<\/a><\/td>\n<\/tr>\n
35<\/td>\nDaniel Abidemi Ajisafe<\/td>\nMirror-BPSA: Learning Human Body Pose, Shape and Appearance from Mirror Images<\/td>\nhttps:\/\/drive.google.com\/open?id=1EyY0vwuzB_PyDdIlMLPeERsacHgXegNp<\/a><\/td>\n<\/tr>\n
36<\/td>\nDaniel Whitenack<\/td>\nPhone-ing it in: Towards Flexible, Multi-Modal Language Model Training using Phonetic Representations of Data<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Daniel_Whitenack_4977_poster.pdf<\/a><\/td>\n<\/tr>\n
37<\/td>\nDaphne Machangara<\/td>\nPredictions and Application of Queuing Analysis at Regional Hospital Limbe (Cameroon)<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Daphne_Machangara_77_poster.pdf<\/a><\/td>\n<\/tr>\n
38<\/td>\nDuaa Alshareif<\/td>\nControlling texture and shape bias in deep learning classifiers<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Duaa_Alshareif_1709_poster.pdf<\/a><\/td>\n<\/tr>\n
39<\/td>\nDylan Geldenhuys<\/td>\nA Deep Learning Approach to Predict Blood Pressure from PPG Signals<\/td>\n <\/td>\n<\/tr>\n
40<\/td>\nEbenezer Olubayode<\/td>\nDevelopment of AI-Powered Digital Therapeutics For Personalized and Fun-Filled Exercise-Based Therapies<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ebenezer_Olubayode_5316_poster.pdf<\/a><\/td>\n<\/tr>\n
41<\/td>\nELIZABETH BENSON<\/td>\nA Deep learning Model for Retinopathy of Prematurity Stage III Disease Diagnosis<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/ELIZABETH_BENSON_1280_poster.pdf<\/a><\/td>\n<\/tr>\n
42<\/td>\nElsie Kaaya<\/td>\nDevelopent of an medical expert system for quality antenatal care in Tanzania<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Elsie_Kaaya_6183_poster.pdf<\/a><\/td>\n<\/tr>\n
43<\/td>\nEmily Muller<\/td>\nMapping city-wide perceptions of neighbourhood quality using street view images: a methodological toolkit.<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Emily _Muller_8199_poster.pdf<\/a><\/td>\n<\/tr>\n
44<\/td>\nEniola Olaleye<\/td>\nA Recommendation System to Enhance Midwives\u2019 Capacities in Low-Income Countries<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/eniola_olaleye_5120_poster.pdf<\/a><\/td>\n<\/tr>\n
45<\/td>\nEspoir Murhabazi<\/td>\nExploring Open Domain Question Answering in French<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Espoir_Murhabazi_274_poster.pdf<\/a><\/td>\n<\/tr>\n
46<\/td>\nEsther Oduntan<\/td>\nText Representation Enhancement using  multi-modal attention mechanism for Text Visual Question Answering<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Esther_Oduntan_6378_poster.pdf<\/a><\/td>\n<\/tr>\n
47<\/td>\nKeneilwe Mokoka<\/td>\nA comparison of the efficiency of MRI and Mammography in breast cancer detection, using Convolutional Neural Networks<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Keneilwe_Mokoka_6922_poster.pdf<\/a><\/td>\n<\/tr>\n
48<\/td>\nFadel THIOR<\/td>\nComputer Vision for Tumor Segmentation<\/td>\n <\/td>\n<\/tr>\n
49<\/td>\nFadoua Ouamani<\/td>\nA Tunisian-Dialect deep language model based psychologist chatbot to assess and manage the mental health of Tunisian people during and after COVID-19 pandemic.<\/td>\nhttps:\/\/drive.google.com\/open?id=1-o5Rr0gen32Cm5s8_IK4HQq1h9G-Xo-3<\/a><\/td>\n<\/tr>\n
50<\/td>\nFehmi Najar<\/td>\nPhysical informed neural network for structural health monitoring<\/td>\n <\/td>\n<\/tr>\n
51<\/td>\nFetenech Meskele<\/td>\nPrediction of Right Seeds Sowing Session Using Machine Learning Approach (Wolaita Zone) Ethiopia<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Fetenech_Meskele_2203_poster.pdf<\/a><\/td>\n<\/tr>\n
52<\/td>\nFolorunso Mubarak Temidayo<\/td>\nCLASSIFICATION MODEL FOR COVID-19 AND PULMONARY (TB) FROM X-RAY IMAGES USING HOG-PCA-LEARNING ALGORITHMS<\/td>\nhttps:\/\/drive.google.com\/open?id=1L3ceNtqrw0rsBkjnAUbu3aPU_jNex_SP<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
54<\/td>\nFoutse YUEHGOH<\/td>\nA Technology Intelligence Recommendation System based on Multiplex Networks<\/td>\nhttps:\/\/drive.google.com\/open?id=1-ncb_wVUUE6nqER_4qduuuuCs0DAz8LX<\/a><\/td>\n<\/tr>\n
55<\/td>\nFrancis TOSSOU<\/td>\nSpatio-temporal distribution of population in Lome<\/td>\n <\/td>\n<\/tr>\n
56<\/td>\nFred Sangol Uche<\/td>\nAI-Based Biomedical Image Analysis System for Disease Diagnosis<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Fred Sangol_Uche_4884_poster.pdf<\/a><\/td>\n<\/tr>\n
57<\/td>\nFrederick Apina<\/td>\nDeep Learning Towards Efficiency Malaria Dataset  Creation<\/td>\n <\/td>\n<\/tr>\n
58<\/td>\nGenet Shanko Dekebo<\/td>\nDeveloping an offline and online multilingual mobile app to improve public awareness  about COVID-19 to limit its Calamity Impact<\/td>\n <\/td>\n<\/tr>\n
59<\/td>\nghofrane Abidi<\/td>\nNLU agent for the insurance industry<\/td>\n <\/td>\n<\/tr>\n
60<\/td>\nGina Moape<\/td>\nSetswana Word Sense Disambiguation in Machine Translation for Implementation of Pepper Humanoid Instructor Robot<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Gina_Moape_5150_poster.pdf<\/a><\/td>\n<\/tr>\n
61<\/td>\nGizeaddis Simegn<\/td>\nClinical Decision support system for diagnosis of Heart Diseases<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Gizeaddis_Simegn_4949_poster.pdf<\/a><\/td>\n<\/tr>\n
62<\/td>\nGloria Namanya<\/td>\nGeospatial Data Analysis and Visualization for Crop Disease Surveillance, Sub-Suharan Africa.<\/td>\nhttps:\/\/drive.google.com\/open?id=1lcX6l0dTANTX2GzoBnU7j0PQoOsjGl8x<\/a><\/td>\n<\/tr>\n
63<\/td>\nGrace Omojola<\/td>\nChemotronix: IOT device enabled with AI for carbon reduction<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Grace_Omojola_6650_poster.pdf<\/a><\/td>\n<\/tr>\n
64<\/td>\nHalleluyah Aworinde<\/td>\nACHIEVING SDG 16: DEVELOPING MACHINE LEARNING-BASED STACKELBERG SECURITY GAME MODEL TO CURB INSURGENCY IN NIGERIA<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Halleluyah_Aworinde_5468_poster.pdf<\/a><\/td>\n<\/tr>\n
65<\/td>\nHeinrich van Deventer<\/td>\nATLAS: Efficient Learning Without Catastrophic Forgetting<\/td>\nhttps:\/\/drive.google.com\/open?id=1cEpJVD9gWfxIO_l7mkbzuXAcsIZQdYKv<\/a><\/td>\n<\/tr>\n
66<\/td>\nHenock Makumbu Mboko<\/td>\n“Application of Ensemble methods for solving imbalanced data problems”<\/td>\nhttps:\/\/drive.google.com\/open?id=1Nd4faBrN04YYFphovQipIDXAuMTBFIdE<\/a><\/td>\n<\/tr>\n
67<\/td>\nHewitt Tusiime<\/td>\nAutomatic Depression Detection<\/td>\nhttps:\/\/drive.google.com\/open?id=1OZDN8buBLinlcaT3CSJ9ocvw5wPCl2Nx<\/a><\/td>\n<\/tr>\n
68<\/td>\nHope Mbelwa<\/td>\nImage-Based Poultry Disease Detection Using Deep Convolutional Neural Network<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Hope_Mbelwa_959_poster.pdf<\/a><\/td>\n<\/tr>\n
69<\/td>\nHoussem Ben Khalfallah<\/td>\nToward a Clinical Decision Support System for Patients with Sepsis using Machine Learning and Data Mining<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Houssem_Ben Khalfallah_8091_poster.pdf<\/a><\/td>\n<\/tr>\n
70<\/td>\nHumphrey Owuor<\/td>\nIntelligent Network Slices for Digital Health Applications.<\/td>\n <\/td>\n<\/tr>\n
71<\/td>\nHyam Ali<\/td>\nComputational Diagnostic Method of the Organisms Causing Mycetoma Based on Histology<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Hyam _Ali_5228_poster.pdf<\/a><\/td>\n<\/tr>\n
72<\/td>\nIan Omung’a<\/td>\nAugmentation Is All You Need: A Baseline Benchmark for Whole Mammogram Classification Models<\/td>\nhttps:\/\/drive.google.com\/open?id=18nyenKKcX6JiS6gphE289ypuTNpTT-8m<\/a><\/td>\n<\/tr>\n
73<\/td>\nIchrak Hamdi<\/td>\nI can-cer vive<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ichrak_Hamdi_6044_poster.pdf<\/a><\/td>\n<\/tr>\n
74<\/td>\nIhssene Brahimi<\/td>\nFire detection system with cameras and drones<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/ihssene_brahimi_6560_poster.pdf<\/a><\/td>\n<\/tr>\n
75<\/td>\nInes Haouala<\/td>\nComputer Vision Enabled Industrial Robot Manipulator for Sorting Operation<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ines_Haouala_4598_poster.pdf<\/a><\/td>\n<\/tr>\n
76<\/td>\nJacobie Mouton<\/td>\nSIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Jacobie_Mouton_6064_poster.pdf<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
78<\/td>\nJacobus Martin<\/td>\nSumoGym: a Framework for Performing Traffic Control using Reinforcement Learning<\/td>\nhttps:\/\/drive.google.com\/open?id=1Kg3-9mtzUtL-OYdRMy4B3qk5lZ4svwOw<\/a><\/td>\n<\/tr>\n
79<\/td>\nJames Allingham<\/td>\nSparse MoEs meet Efficient Ensembles<\/td>\n <\/td>\n<\/tr>\n
80<\/td>\nJan Buys<\/td>\nSubword Segmental Language Modelling for Nguni Languages<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Jan_Buys_6099_poster.pdf<\/a><\/td>\n<\/tr>\n
81<\/td>\nJavier Antoran<\/td>\nImproving the efficiency of X-Ray image reconstruction with Bayesian Deep Learning<\/td>\nhttps:\/\/drive.google.com\/open?id=15QOrD5m8Stve17IomEhuTP8e-xls5bII<\/a><\/td>\n<\/tr>\n
82<\/td>\nJEAN AMUKWATSE<\/td>\nUtilization of machine learning in screening for multi drug resistance in TB cases<\/td>\n <\/td>\n<\/tr>\n
83<\/td>\nJeremiah Fadugba<\/td>\nDeep Multiple Instance Learning for referable or non referable diabetic retinopathy from Fundus images.<\/td>\n <\/td>\n<\/tr>\n
 <\/td>\n <\/td>\n <\/td>\n <\/td>\n<\/tr>\n
 <\/td>\n <\/td>\nPOSTER SESSION 2<\/strong><\/span><\/td>\n <\/td>\n<\/tr>\n
177<\/td>\nAla’a El-Nabawy<\/td>\nA Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ala’a_El-Nabawy_5365_poster.pdf<\/a><\/td>\n<\/tr>\n
84<\/td>\nJohan van den Burg<\/td>\nPredicting maize crop yields using multiple linear regression and backward elimination<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Johan_ van den Burg_8136_poster.pdf<\/a><\/td>\n<\/tr>\n
85<\/td>\nJonathan Mukiibi<\/td>\nThe Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Jonathan_Mukiibi_5390_poster.pdf<\/a><\/td>\n<\/tr>\n
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87<\/td>\nKaleab Tessera<\/td>\nLearning Dynamic Networks<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Kaleab_Tessera_1024_poster.pdf<\/a><\/td>\n<\/tr>\n
88<\/td>\nKathleen Siminyu<\/td>\nMore Data versus Less Repetition; which matters more for Kiswahili Automatic Speech Recognition?<\/td>\n <\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
90<\/td>\nKennedy Wangari<\/td>\nTowards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting<\/td>\n <\/td>\n<\/tr>\n
91<\/td>\nKevin Eloff<\/td>\nTowards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Kevin_Eloff_1288_poster.pdf<\/a><\/td>\n<\/tr>\n
92<\/td>\nKhadija Iddrisu<\/td>\nDeep Learning Architectures For Brain Vessel Segmentation<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Khadija _Iddrisu _4778_poster.pdf<\/a><\/td>\n<\/tr>\n
93<\/td>\nKHALID ELMADANI<\/td>\nSudaBERT: A Pre-trained Encoder Representation For Sudanese Arabic Dialect<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/KHALID_ELMADANI_1005_poster.pdf<\/a><\/td>\n<\/tr>\n
94<\/td>\nKhutso FENYANE<\/td>\nEfficient Electricity Consumption Tracking based on Machine Learning<\/td>\n <\/td>\n<\/tr>\n
95<\/td>\nKondwani Magamba<\/td>\nCrop management using predictive analytics and leaf venation networks <\/td>\nhttps:\/\/drive.google.com\/open?id=13RQM5huCoxK-5sbgaLxjk5FGLW-QPc2u<\/a><\/td>\n<\/tr>\n
96<\/td>\nKristina Georgieva<\/td>\nData science in early phase startups<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Kristina_Georgieva_4490_poster.pdf<\/a><\/td>\n<\/tr>\n
97<\/td>\nKrupa Suchak<\/td>\nPredicting data gravity growth and transitions in Europe, Middle East, and Africa (EMEA) Region<\/td>\n <\/td>\n<\/tr>\n
98<\/td>\nLillian Wanzare<\/td>\nKencorpus: Kenyan Languages Corpus for Natural Language Processing and Machine Learning<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Lillian _Wanzare_8541_poster.pdf<\/a><\/td>\n<\/tr>\n
99<\/td>\nLinda Marrakchi<\/td>\nBrain Tumor Survival Prediction Project: Challenges in a Tunisian context<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Linda_Marrakchi_5362_poster.pdf<\/a><\/td>\n<\/tr>\n
100<\/td>\nLoyani Loyani<\/td>\nA Deep Learning Approach for Quantifying Tuta Absoluta \u2019s damage on Tomato Plants<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Loyani_Loyani_967_poster.pdf<\/a><\/td>\n<\/tr>\n
101<\/td>\nLu\u00eds Pina<\/td>\nA computer vision non-intrusive mechanism to collect images and give real-time insights about animals behaviour on coral reefs in Mozambican waters<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Lu\u00eds_Pina_4004_poster.pdf<\/a><\/td>\n<\/tr>\n
102<\/td>\nLungisani Ndlovu<\/td>\nInvestigating Social Media Misinformation for the 2021 South African Municipal Elections<\/td>\n <\/td>\n<\/tr>\n
103<\/td>\nMahmoud Ghorbrl<\/td>\nDeep Visual Feature Learning for Person Re-identification<\/td>\n <\/td>\n<\/tr>\n
104<\/td>\nMai Gamal<\/td>\nPre-trained Deep Convolutional Neural Network for Modeling Natural Visual Stimulus Encoding in the Early Visual System<\/td>\nhttps:\/\/drive.google.com\/open?id=1mykVX9BKhSlYkJ1jo1K-tUZ-Y_Eg6XsI<\/a><\/td>\n<\/tr>\n
105<\/td>\nMarwa BEN AMMAR<\/td>\nSystem Aided Diagnosis of Breast cancer<\/td>\nhttps:\/\/drive.google.com\/open?id=1o5-UzpWE6EeotAXgtSXKh_PlghPQCi7i<\/a><\/td>\n<\/tr>\n
106<\/td>\nMarwa Dhiaf<\/td>\nNamed Entity Recognition for Handwritten documents  images<\/td>\nhttps:\/\/drive.google.com\/open?id=1jNN_MBgxmvtSc1KuLaxfzm89FEqHYY_8<\/a><\/td>\n<\/tr>\n
107<\/td>\nMary Salami<\/td>\nAFRIFASHION1600: A Contemporary African Fashion Dataset for Computer  Vision <\/td>\nhttps:\/\/drive.google.com\/open?id=12N6aDkgsroYA9IxhOtktR1YGn1lCD6JD<\/a><\/td>\n<\/tr>\n
108<\/td>\nMatimba Shingange<\/td>\nXitsonga <>English translation using NMT with Back translation<\/td>\n <\/td>\n<\/tr>\n
109<\/td>\nMatthew Morris<\/td>\nUniversally Expressive Communication in Multi-Agent Reinforcement Learning<\/td>\nhttps:\/\/drive.google.com\/open?id=1UY2amjoV41x5mJ0W-mgyftqaTyZz5_Ih<\/a><\/td>\n<\/tr>\n
110<\/td>\nMbithe Nzomo<\/td>\nAn Agent-Based Framework for Precision Health<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Mbithe_Nzomo_135_poster.pdf<\/a><\/td>\n<\/tr>\n
111<\/td>\nMelsew Belachew<\/td>\nIntegration of Data Mining with Knowledge Based System  for Diagnosis and Treatment of peppercorn Crop<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Melsew_Belachew_4554_poster.pdf<\/a><\/td>\n<\/tr>\n
112<\/td>\nMike Mwanga<\/td>\nEnhanced deep convolutional neural network for SARS-CoV-2 classification<\/td>\n <\/td>\n<\/tr>\n
113<\/td>\nMoayad Elamin<\/td>\nCreating Spoken Dialog Systems in Ultra-Low Resourced Settings<\/td>\n <\/td>\n<\/tr>\n
114<\/td>\nMohammed Almakki<\/td>\nAutophase V2: Towards Function Level Phase Ordering Optimization<\/td>\nhttps:\/\/drive.google.com\/open?id=1uK_4salQju04KVDklRQkpQ-oXerRbfk0<\/a><\/td>\n<\/tr>\n
115<\/td>\nMona Mayouf<\/td>\nA review on cutting edge neural networks advancements<\/td>\n <\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
117<\/td>\nMuhammed Saeed<\/td>\nOvercoming Orthographic Variations of Related Languages with Noisy Augmentation in Naija Pidgin<\/td>\n <\/td>\n<\/tr>\n
118<\/td>\nMulualem Bitew<\/td>\nAccessibility of AI Technologies for Persons with Disabilities and the Legal Requirements: Assessing the Situation In Ethiopia<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Mulualem_Bitew_6964_poster.pdf<\/a><\/td>\n<\/tr>\n
119<\/td>\nNabil BADRI<\/td>\nCombining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection<\/td>\nhttps:\/\/drive.google.com\/open?id=12MrCuUgjgbmQJpFdA4zd2RGMJrZXJ9e-<\/a><\/td>\n<\/tr>\n
120<\/td>\nNasirudeen Raheem<\/td>\nReducing the risk of Gender-based Violence with Interpretable Machine Learning<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Nasirudeen_Raheem_1055_poster.pdf<\/a><\/td>\n<\/tr>\n
121<\/td>\nNesrine Ben Yahia<\/td>\nHealthcare analytics for predicting influenza epidemic outbreaks: Experiences and Lessons Learned<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Nesrine_ Ben Yahia  _8209_poster.pdf<\/a><\/td>\n<\/tr>\n
122<\/td>\nNicolas Lopez Carranza<\/td>\nDeepChain : A platform for Protein Design<\/td>\nhttps:\/\/drive.google.com\/open?id=1L31y-l_cNFyAKloZUBK-TJVkWBUZmMOC<\/a><\/td>\n<\/tr>\n
123<\/td>\nNomsa Thabethe<\/td>\nMACHINE LEARNING APPROACH FOR ATMOSPHERIC MODELING, HEALTH IMPACTS AND LEGAL ANALYSIS<\/td>\nhttps:\/\/drive.google.com\/open?id=1z6celihTYEnAdilD6zqYttoUwxJ1yoOW<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
125<\/td>\nOfentse Phuti<\/td>\nIdentification of Plasmodium species in malaria diagnosis through
image classification<\/td>\n		https:\/\/drive.google.com\/open?id=13ijThzX2vzwXrhfMJkFAqGR024YQhNd7<\/a><\/td>\n<\/tr>\n
126<\/td>\nOkegbemi Lawrence<\/td>\nAn Emergency Influence based Twitterbot using ULMfit<\/td>\nhttps:\/\/drive.google.com\/open?id=1UszeSLnagyVoUijuND0KhEyslpgxJ-60<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
128<\/td>\nOlutola Agbelusi<\/td>\nDevelopment of predictive model for the survival of HIV\/AIDs pediatric patients  using data mining techniques.<\/td>\n <\/td>\n<\/tr>\n
129<\/td>\nOluwabukola Adegboro<\/td>\nIncremental Learning Based Anomaly Detection For Computed Tomography (CT)<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Oluwabukola_Adegboro_6604_poster.pdf<\/a><\/td>\n<\/tr>\n
130<\/td>\nOtmane Amel<\/td>\nMultimodal learning for customs fraud detection and action recognition.<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Otmane_Amel_448_poster.pdf<\/a><\/td>\n<\/tr>\n
131<\/td>\nOyindamola Olatunji<\/td>\nA Machine Learning Approach to Predict Autism Spectrum Disorder using fMRI data<\/td>\n <\/td>\n<\/tr>\n
132<\/td>\nPaul Adedeji<\/td>\nMeteorological-based oil temperature prediction in wind turbine gearbox using deep learning<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Paul_Adedeji_5828_poster.pdf<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
134<\/td>\nPelonomi Moiloa<\/td>\nAnalysis of the Restitution Discourse Online<\/td>\n <\/td>\n<\/tr>\n
135<\/td>\nProscovia Nakiranda<\/td>\nBuilding Identification In Satellite Imagery using Deep learning<\/td>\n <\/td>\n<\/tr>\n
136<\/td>\nRachel Catzel<\/td>\nInvestigation of brain ageing in HIV-positive individuals using a neural network<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Rachel_Catzel_6077_poster.pdf<\/a><\/td>\n<\/tr>\n
137<\/td>\nRaesetje Sefala<\/td>\nConstructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Raesetje_Sefala_787_poster.pdf<\/a><\/td>\n<\/tr>\n
138<\/td>\nRamadimetse Sydil Kupa<\/td>\nDistilling Radio Sources From Noise: A Computer Vision Approach<\/td>\nhttps:\/\/drive.google.com\/open?id=1SFQAhbv1ODBY0HPxAx62EeYBYpzjOF_v<\/a><\/td>\n<\/tr>\n
139<\/td>\nRami Ahmed<\/td>\nModel-Based Actor-Critic<\/td>\n <\/td>\n<\/tr>\n
140<\/td>\nRuan van der Merwe<\/td>\nManifold Characteristics That Predict Downstream Task Performance<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Ruan_van der Merwe_4656_poster.pdf<\/a><\/td>\n<\/tr>\n
141<\/td>\nSakinat Folorunso<\/td>\nORIN: The Nigerian music benchmark dataset for Music Information Retrieval task<\/td>\nhttps:\/\/drive.google.com\/open?id=1X8HcA6lHb2mIKMQX_QPEpa3l4Ak-M-Qr<\/a><\/td>\n<\/tr>\n
142<\/td>\nSegun Adebayo<\/td>\nTiny-ML Possibilities for the implementation of Keyemaba: Pest Detection and Prevention using Unmanned Aerial Vehicle on Farmland.<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Segun_Adebayo_4020_poster.pdf<\/a><\/td>\n<\/tr>\n
143<\/td>\nSeleme Shoky Maakgetlwa<\/td>\nMulti-objective Loan Portfolio Optimization in Peer-to-Peer Lending Markets using Machine-Learning Techniques<\/td>\n <\/td>\n<\/tr>\n
144<\/td>\nShamsuddeen Muhammad<\/td>\nAFRISENTI-SEMEVAL SHARED TASK 12: SENTIMENT ANALYSIS FOR 15 LOW-RESOURCE AFRICAN LANGUAGES USING TWITTER DATASET<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/shamsuddeen_muhammad_4792_poster.pdf<\/a><\/td>\n<\/tr>\n
145<\/td>\nSHESTER LANDRY MSOUOBU GUEUWOU<\/td>\nGhanaian Sign Language Translation<\/td>\n <\/td>\n<\/tr>\n
146<\/td>\nSiobhan Hall<\/td>\nA Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Siobhan _Hall_28_poster.pdf<\/a><\/td>\n<\/tr>\n
147<\/td>\nSisipho Hamlomo<\/td>\nLow-rank Approximation and Gaussian Noise Estimation. An Analysis with Surgical Imaging<\/td>\nhttps:\/\/drive.google.com\/open?id=159mCj4LrO3dPpSiTP9zBLKtLAh0hXeto<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
149<\/td>\nSteven Kolawole<\/td>\nTowards an Inclusive Society: Sign-to-Speech Model for Sign Language Understanding<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Steven_Kolawole_4633_poster.pdf<\/a><\/td>\n<\/tr>\n
150<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
151<\/td>\nSunday Ajagbe<\/td>\nDevelopment of multimedia corpus in medical domain using neural machine translation<\/td>\nhttps:\/\/drive.google.com\/open?id=1HC4hNUbMZBaMlm6vELm49LkiZSprKGan<\/a><\/td>\n<\/tr>\n
152<\/td>\nTamlin Love<\/td>\nHarnessing the wisdom of an unreliable crowd for autonomous decision making<\/td>\nhttps:\/\/drive.google.com\/open?id=1JGqNiihZMhtr5YhJbA-W7M3d9CdKIJ1d<\/a><\/td>\n<\/tr>\n
153<\/td>\nTayeb Benzenati<\/td>\nDeep Learning for Satellite Image Fusion<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Tayeb_Benzenati_1612_poster.pdf<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
155<\/td>\nThapelo Andrew Sindane<\/td>\nCross-Lingual Embedding Methods and Applications: A Systematic Review for Low-Resourced Scenarios<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Thapelo Andrew_Sindane_4865_poster.pdf<\/a><\/td>\n<\/tr>\n
156<\/td>\nToluwani Adegoke<\/td>\nApplication of Pattern Recognition to Early Detection of symptoms of sickle cell crisis<\/td>\n <\/td>\n<\/tr>\n
157<\/td>\nUmar Adam Ibrahim<\/td>\nSpeech Recognition for Hausa language<\/td>\n <\/td>\n<\/tr>\n
158<\/td>\nWafaa Mohammed<\/td>\nVisual grounding of Interlingual Word embeddings<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Wafaa_Mohammed_4576_poster.pdf<\/a><\/td>\n<\/tr>\n
159<\/td>\nWathela Alhassan<\/td>\nDetection of Einstein Telescope gravitational wave signals from binary black holes using CNNs<\/td>\nhttps:\/\/drive.google.com\/open?id=13vZ0wx_Bx7SVWogYrYv_1ny7LBv2ymV5<\/a><\/td>\n<\/tr>\n
160<\/td>\nWilhelmina Nekoto<\/td>\nWON (Writing Our Narratives) – My African Dream<\/td>\n <\/td>\n<\/tr>\n
161<\/td>\nYannick Serge Obam Akou<\/td>\nPLANT DISEASE CLASSIFICATION USING DEEP LEARNING MODELS<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Yannick Serge_Obam Akou_554_poster.pdf<\/a><\/td>\n<\/tr>\n
162<\/td>\nYara Armel D\u00e9sir\u00e9<\/td>\nHorus Eye<\/td>\nhttps:\/\/drive.google.com\/open?id=1NBd2UXwTJSWKuyvHJJO7OM0FordxNb9F<\/a><\/td>\n<\/tr>\n
163<\/td>\nYaroub ELLOUMI<\/td>\nEnd-to-End Mobile System for Diabetic Retinopathy Screening Based on Lightweight Deep Neural Network<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Yaroub_ELLOUMI_6850_poster.pdf<\/a><\/td>\n<\/tr>\n
164<\/td>\nYassir Osman<\/td>\nControlling texture and shape bias in deep learning classifiers<\/td>\n <\/td>\n<\/tr>\n
165<\/td>\nYusuf Brima<\/td>\nSupervised Contrastive Deep Learning for Individual Recognition<\/td>\n <\/td>\n<\/tr>\n
166<\/td>\nYvan Tamdjo Biakeu<\/td>\nBuilding a zero-shot translation model for some African languages: Afrikaans, Swahili, Malagasy, Hausa, Xhosa and Zulu.<\/td>\nhttps:\/\/drive.google.com\/open?id=1M87cfdhMEswwcErL6TeUCF4ONb1Fy4iV<\/a><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
168<\/td>\nZakia Salod<\/td>\nCreating and comparing machine learning-based reverse vaccinology models for predicting viral protective antigens<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/Zakia_Salod_1885_poster.pdf<\/a><\/td>\n<\/tr>\n
169<\/td>\nZephania Reuben<\/td>\nDiabetic Retinopathy Diagnosis<\/td>\n <\/td>\n<\/tr>\n
170<\/td>\nSARA EL-ATEIF<\/td>\nSingle-modality and joint fusion deep learning for diabetic retinopathy diagnosis<\/td>\nhttps:\/\/drive.google.com\/open?id=1Jzq8fHFUEaU9ZYPFoOCotGNikHiz7-hT<\/a><\/td>\n<\/tr>\n
171<\/td>\nAhmed Rebai<\/td>\nUnsupervised diversification applied on the tunisian stock market before and during the covid-19 crisis<\/td>\nUnsupervised diversification applied on the tunisian stock market before and during the covid-19 crisis<\/a><\/td>\n<\/tr>\n
172<\/td>\nChihebeddine HAMMAMI<\/td>\nConnected Concession – Object detection applications for mine impact monitoring<\/td>\n<\/td>\n<\/tr>\n
173<\/td>\nTol\u00fal\u1ecdp\u1eb9\u0301 \u00d2g\u00fanr\u1eb9\u0300m\u00ed<\/td>\nAn analysis of fine-tuned representations for code-switched speech recognition of Yor\u00f9b\u00e1 and English<\/td>\nhttps:\/\/drive.google.com\/open?id=1VFIEb_dOTojnVfVFnI95KxY43W2Kegnf<\/a><\/td>\n<\/tr>\n
174<\/td>\nSaoussen Mathlouthi<\/td>\nEfficient learning approach for the resolution of syntactic reprise phenomena in Arabic: Case of the anaphora and the ellipse<\/td>\nhttps:\/\/drive.google.com\/open?id=1BRchG9igTxmOrSh98nhcd4ATxA3IYaHJ<\/a><\/td>\n<\/tr>\n
175<\/td>\nTejumande Afonja<\/td>\nSautiDB-Naija: A Nigerian L2 English Speech Corpus<\/td>\nhttps:\/\/drive.google.com\/file\/d\/1JjEtCXLkWkkYimbJEJgxv7hA8Fwptxsj\/view?usp=sharing<\/a><\/td>\n<\/tr>\n
176<\/td>\nAmal Nammouchi<\/td>\nIntegration of AI, IoT and Edge computing for Smart Microgrid Energy Management System<\/td>\nhttps:\/\/drive.google.com\/file\/d\/1-raPK1CgQ630jlIj-BFyHuqqpfgrWC-1\/view?usp=sharing<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<\/p>\n

Demos<\/h3>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Demo Title<\/th>\nPresenter<\/th>\n<\/tr>\n<\/thead>\n
Deep Visual Feature Learning for Person Re-identification<\/td>\nMahmoud Ghorbrl<\/td>\n<\/tr>\n
Deep learning-based approach to predict request for helps in emergency
situations<\/td>\n		Sarra CHAIIR<\/td>\n<\/tr>\n
AfriDial: A NLP based API for African Dialect Sentiment Analysis<\/td>\nWael Ouarda<\/td>\n<\/tr>\n
Possibilistic Rank-Level Fusion Method for Person Re-Identification<\/td>\nilef ben slima<\/td>\n<\/tr>\n
Satellite Image Fusion<\/td>\nTayeb Benzenati<\/td>\n<\/tr>\n
An expectation maximization algorithm for the hidden Markov model with multiparameter Student-t observations<\/td>\nMahdi LOUATI<\/td>\n<\/tr>\n
A Tunisian-Dialect deep language model based psychologist-chatbot to
assess and manage the mental health of Tunisian people during and after
COVID-19 pandemic.<\/td>\n		Fadoua Ouamani<\/td>\n<\/tr>\n
Named Entity Extraction from document images<\/td>\nMarwa DHIAF<\/td>\n<\/tr>\n
Healthcare analytics for predicting influenza epidemic outbreaks:
Experiences and Lessons Learned<\/td>\n		Nesrine Ben Yahia<\/td>\n<\/tr>\n
Using voice assistant to improve patient\u2019s engagement in online
therapy platforms<\/td>\n		Mariem Jelassi<\/td>\n<\/tr>\n
Possibilistic modeling of small sample size<\/td>\nSonda Ammar<\/td>\n<\/tr>\n
Toward a Clinical Decision Support System for Patients with Sepsis
using Machine Learning and Data Mining<\/td>\n		Houssem Ben Khalfallah<\/td>\n<\/tr>\n
A hybrid method based on Quantum-enhanced RNN And Data
Integration for the prediction of COVID-19 outbreak<\/td>\n		Ahmed Nasri<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n","protected":false},"excerpt":{"rendered":"

This is the current list of posters and demos that will be presented on the Africa Research day. This page will be updated regularly. Poster Presentations Poster # Presenter(s) Poster Title Link to Poster     POSTER SESSION 1           1 Abderrahman Aouadi Detection and characterization of breast tumors from medical […]<\/p>\n","protected":false},"author":2,"featured_media":570,"parent":6,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-569","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nPosters and Demos - Deep Learning Indaba 2022<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/34.24.164.239\/2022\/indaba\/posters\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Posters and Demos - Deep Learning Indaba 2022\" \/>\n<meta property=\"og:description\" content=\"This is the current list of posters and demos that will be presented on the Africa Research day. This page will be updated regularly. 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