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  Automated Diagnosis and Classification of Cervical Cancer from Pap-Smear Images

.  Self driving cars Vs Pap-smear Image Analysis, Malaria Diagnosis  Opportunities in Africa to fill gaps in: Datasets AI Research in Africa 5  However, Reliable Datasets are typically smaller in Africa  But are more accessible. (...)  Limited APIs  A lot of open dataset NOT from Africa’s population  http://machineintelligenceafrica.org/resources/machine-intelligence/data-sets/ http://machineintelligenceafrica.org/resources/machine-intelligence/data-sets/ The Challenge AI Research in Africa 10  For Disease Diagnosis  Lack of disease specific datasets for AI research  Malaria, Cancer, etc.  Specific body part images  X-rays, CT scans.  Natural Language processing  Limited datasets for African Languages  Open Access Registries  A lot of feature selection required  Prediction  Limited datasets and History  Limited Expertise in Biomedical Data science Wayforward AI Research in Africa 11  Need to skill Africans with emerging AI techniques  Not Only, Academia  BUT also private sector (computing firms)  Health professionals  What AI can do for them..  Free Easily Accessible Online Courses  In Addition to  Coursera  Data Science Africa  More competitions  Kaggle  Africa specific health challenges Wayforward AI Research in Africa 12  A need for independent auditing of machine learning models  A number of free open Apps especially for Diagnosis  Empower Africans to build AI models for the existing challenges  A model built with right data, right algorithms may fail to work in a different setting.  General education curriculum need to prioritise the cultivation of AI skills to students  Currently, AI common to University (Masters, PhDs)  Setting up of Centres of Excellence in Machine Learning  Innovation Hubs with infrastructure to support AI  Gov. to support Open Data initiatives  AI labs in Universities Wayforward AI Research in Africa 13  Strengthen patternships and collaborations  African and other international academic institutions  Data sharing for training AI models  Expertise  Academic institutions and the private sector  Finally More AI for Health Networking Events  Identify challenges  Meet people working on similar projects  Thanks to ITU and WHO Thank you for Your Time 14 wwasswa@must.ac.ug mailto:wwasswa@must.ac.ug

Score: 1168543.4 - https://www.itu.int/en/ITU-T/W...illiam_Wasswa_Presentation.pdf

  Page 109 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine le

 Page 109 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks           Basic HTML Version Table of Contents View Full Version Page 109 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks P. 109 ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4 The μ in the upper and lower limits of the target Compared with the adjacency table, using an range is the average value of bandwidth utilization adjacency matrix to store the connection of all links in the network topology. relationships of nodes can improve query efficiency. In the calculation formula, is the flow value of the network element nodes in the link except the link head and tail nodes, and is the maximum value of the value of the other nodes in the link except the link head and tail nodes. 2.2 Architecture design Through team analysis, Qian Deng found ITU's Fig. 5 – Node structure machine learning framework in the future network (mainly containing three components, ML sandbox 2.4 Modeling system, ML pipeline subsystem and management Regarding the Topology Restoration Model (TRM) subsystem), and believed that the ML pipeline and Traffic Forecast Model (TFM), Zhouwei Gang subsystem met the needs of this competition. believes that the essence of topology restoration is to organize and form a new data set according to the The ML pipeline subsystem consists of 7 parts, but the data has been provided for this competition, and specified conditions from the original data set. the optimization results are given in the form of a Therefore, search algorithms can be used for table and do not need to be directly connected to the processing. (...) Data integrity: Zezhong Feng uses pandas to check the integrity of key fields (traffic, latitude, longitude, connection relationship, etc.) and fill in missing data to ensure normal operation of subsequent predictions and optimizations.

Score: 1168275.2 - https://www.itu.int/en/publica.../files/basic-html/page109.html

  Programme

Programme Committed to connecting the world Search for: ITU General Secretariat Radiocommunication Standardization About ITU-T Events All Groups Standards Resources BSG Study Groups Regional Presence Join ITU-T Development ITU Telecom Members' Zone Join ITU Programme Rollup Image You are here ITU > Home > ITU-T > Workshops and Seminars > ITU Workshop on Artificial Intelligence, Machine Learning and Security > Programme Share Page Content 20 ​​​​​​​​​ ​​​​​​​​​​​​ITU Workshop on Artificial Intelligence, Machine Learning and Security Geneva, Switzerland, 21 January 2019 Contact: tsbevents@itu.int Monday, 21 January 2019 08:30 - 09:30 Registration 09:15 - 10:00 Opening Remarks Reinhard Scholl ,  Deputy- Director, TSB, ITU [  Biography ] Heung Youl Youm , ITU-T Study Group 17 Chairman [  Biography I  Opening Remarks  ] Keynote presentation :  Overview of cybersecurity and AI , Andrew Gardner, Founder and  Leader, Center for Advanced Machine Learning (CAML), Symantec, USA [ Biography I Presentation ] 10:00 - 11:40 Session 1: Using AI and ML technologies for security – part 1 This session will further focus on identifying how AI and ML technologies can be leveraged to improve the cyber defence capabilities, including various use cases.   Session Chair: Zhaoji Lin , ZTE, China [ Biography ​ ]       ​Application of AI on APT defense , Tian Tian , APT Project Manager, ZTE, China [ Biography ​ I Abstract ​ I Presentation ​ ] Toward the automation of cybersecurity operations using machine learning techniques,   Takeshi Takahashi, Research Manager , NICT, Japan [ Biography I   Abstract I Presentation ] Customer privacy enhancement by using machine learning technology, ​ Joong-Gunn Park, ​Head of core network R&D, SK Telecom, Korea (Remote) [ Biography I Abstract ​ ] Cyber threats for telecommunications network and how does AI change that,  Mikko Karikytö, Head of  Network Security, Ericsson [ Biography  I Abstract ] CyberCop: AI is the cyber warrior, Neil Sahota, Master Inventor and World Wide Business Development Leader​, IBM [ Biography I Abstract I  Presentation ​ ]​ 11:40 - 12:00 Coffee Break 12:00 - 13:00 Session 2: Security threats and privacy risks of AI and ML applications  This session will aim to identify security threats and privacy risks of AI and ML applications, and discuss how such risks can be mitigated .   Session Chair: Arnaud Taddei, Director, Standards and Architectures, Digital Service Providers ,  Symantec, USA [ Biography ] Challenges for transparent and trustworthy machine learning, Vanessa Bracamonte, KDDI Researcher, Japan (Remote) [  Biography I Presentation ] The impact of AI on life cycles processes , Antonio Kung, CEO, Trialog, France [ Biography ​ I Presentation ] Online fraud detection with AI, Y anhui Wang, Manager, Detection of Fraud Department, 360 Technology, China ​[ Biography I Presentation ​ ]                         13:00 - 14:00 Lunch Break 14:00 - 15:40 Session 3: Using AI and ML technologies for security – part 2 This session will further focus on identifying how AI and ML technologies can be leveraged to improve the cyber defence capabilities, including various use cases.

Score: 1163605 - https://www.itu.int/en/ITU-T/W.../20190121/Pages/programme.aspx

  The Government of Rwanda and the UN Refugee Agency launch the issuance of Machine Readable Refugee T

The new refugee travel documents are available to eligible refugees hosted in Rwanda. The Machine Readable Refugee Travel Document includes personal data and photo of the refugee and a machine readable zone to conform to ICAO standards. Ines, Burundian refugees, shows her new issued Machine Readable Convention Travel Document. ©UNHCR/Eugene Sibomana UNHCR Representative to Rwanda, Mr. Ahmed BABA FALL gives a Machine Readable Convention Travel Document to Ines, one of the ten first refugees who applied for the travel document.

Score: 1163222.6 - https://www.unhcr.org/rw/13674...-refugee-travel-documents.html

  Microsoft Word - SEA-01

For products to be tested, examined and inspected or exhibited, formalities pertaining to exit from the areas shall be executed in accordance with Customs regulations on control of temporarily imported goods. Article 24 Where machines, equipment, modeling tools, articles for office use, etc. which are used in the areas need to be transported to outside areas for maintenance, testing or examination, enterprises within the areas or the administrative bodies shall fill in the Contact Sheet for Examination, Inspection and Maintenance of the Goods Transported from within the Export Processing Area to an Outside Area, submit applications to the competent Customs offices, and may transport those machines, equipment, modeling tools, articles for office use, etc. to outside areas for maintenance, testing or examination and inspection only upon approval, registration and inspection by the competent Customs offices. Where an enterprise within the area transports modeling tools to outside areas for maintenance, testing or examination and inspection, the sample products manufactured by these modeling tools shall be retained for the examination by Customs of the modeling tools transported back into the areas. Machines, equipment, modeling tools, articles for office use, etc. transported to outside areas for maintenance, testing or examination and inspection shall not be used for the processing, production and use in outside areas. Article 25 Machines, equipment, modeling tools, articles for office use, etc. transported to outside areas for maintenance, testing or examination and inspection shall be transported back into the processing areas within two months from the date on which they are transported out of the areas.

Score: 1158489.5 - https://www.wto.org/english/th...c_e/chn_e/WTACCCHN46_LEG_1.pdf

  WEIGHING AND PREPACK

This type of weighing machine helps us to give quick service and better information to our customers. (...) Usually that position can be adjusted with screws that - 1 9 - raise or lower one side of the scale. On some machines you may find a built-in water level to make adjustment easier. (...) a After the bags are closed. b When the bags have been placed on the shelf. c Before the bags have been filled. Why is there a water level on some weighing machines?

Score: 1151531.1 - https://www.ilo.org/wcmsp5/gro...tionalmaterial/wcms_628579.pdf

  1 I. INITIATIVES AU

SITE WEB : Fondation Jeunesse Numérique : https://fjn.ci ❖ ORANGE ACADEMY Orange Academy offre des formations en : - Réalités augmentées, - Intelligence artificielle, - Machine learning, - Internet des objets (IOT). SITE WEB: https://digitalacademy.orange.ci/ II. (...) SITE WEB: https://www.femmes-tic.org/ ❖ RESEAU INTERNATIONAL FEMMES EXPERTES DU NUMERIQUE Le RIFEN est une organisation non gouvernementale à vocation internationale, qui œuvre pour la promotion des femmes et jeunes filles professionnelles et universitaires, dans le domaine du numérique et autres domaines connexes tels que Internet, secteur Postal et Protection des données à caractère personnelles. (...) Le RIFEN contribue aussi à répondre aux défis liés à l’inclusion de plus de femmes et de jeunes filles dans les activités des organisations nationales, régionales, continentales et internationales.

Score: 1149759.4 - https://www.itu.int/generation...ES-JEUNES-ET-DES-FEMMES_vf.pdf

  Technical Barriers t

Kitchen equipment 97.040 8452 Sewing machines, other than book-sewing machines of heading 84.40; furniture, bases and covers specially designed for sewing machines; sewing machine needles. (...) Kitchen equipment 97.040 8472 Other office machines (for example, hectograph or stencil duplicating machines, addressing machines, automatic banknote dispensers, coin-sorting machines, coin-counting or wrapping machines, pencil-sharpening machines, perforating or stapling machines). (...) Laundry appliances 97.060 8418 Refrigerators, freezers and other refrigerating or freezing equipment, electric or other; heat pumps other than air conditioning machines of heading 84.15. Laundry appliances 97.060 8422 Dish washing machines; machinery for cleaning or drying bottles or other containers; machinery for filling, closing, sealing or labelling bottles, cans, boxes, bags or other containers; machinery for capsuling bottles, jars, tubes and similar containers; other packing or wrapping machinery (including heat-shrink wrapping machinery); machinery for aerating beverages.

Score: 1147678.1 - https://www.wto.org/english/re...cations_e/readme_tbt_stc_e.doc

  Introduction

All Internet messaging systems (Email) utilize DNS to find the host machines which service a particular recipients mail. (...) Any machine may in fact, host many domains; domains may also map to multiple machines for rendundancy. Once the host machine is found, a number of standard information records can be retrieved that are associated with the domain In this case, there is a record which indicates the address of a potentially different machine which handles email duties (e.g.

Score: 1146052.5 - https://www.itu.int/wftp3/av-a...993-1996/9609_Eib/AVC-1021.doc

  Migration : De retour en Guinée, Aminata reprend enfin une vie normale | UNICEF

Mais au fur et à mesure que j’avance avec l’appui de ma maitresse je commence à prendre le goût à manier la machine à coudre et à réaliser des tenues. Enfin de compte je crois que je vais jumeler les études à la couture, cela me servira de tremplin d’ici au métier d’avocate », dit -elle confiante. L’UNICEF vient de doter l’atelier d’Aminata de machines à coudre pour renforcer les capacités d’apprentissage des apprenantes et aider Aminata à financer ses études. (...) Aboubacar Sidiki DIALLO Remise par l'UNICEF des machines à coudre à l'atelier où Aminata vient apprendre la couture après l'école Thèmes connexes Réunification et réintégration Émancipation des filles Protection de l’enfant Migration Guinée Pour en savoir davantage sur l'UNICEF Communiqué de presse 23 mai 2022 L’UNICEF et la Fédération Guinéenne de Football (FEGUIFOOT), main dans la main pour assurer la promotion des droits de l’enfant en République de Guinée Consulter la page Article Les filles leaders se dressent contre le mariage d’enfant Les jeunes filles leaders de Ouré-kaba veulent mettre fin au mariage d’enfant dans la commune rurale à travers des actions concrètes Lire l’article Communiqué de presse 07 avril 2022 L’Union européenne soutient les Ministères en charge de la protection de l’enfant à travers l’UNICEF Consulter la page Article Le plan Triennal du Secrétariat Général des Affaires est dis Le SGAR a desormais son plan triennal 2022-2024 Lire l’article Footer Acceuil Ce que nous faisons À propos des enfants de la Guinée Nos histoires sur les enfants de la Guinée À propos de nous L'UNICEF en Guinée Notre Ambassadeur National Contactez-nous Devenez donateur Social Footer Secondary Contactez-nous Informations légales Footer tertiary Signaler une fraude, un acte répréhensible, un méfait

Score: 1143643.2 - https://www.unicef.org/guinea/...-reprend-enfin-une-vie-normale