Ender Turing Mofuta oa Software ea Tlhahlobo ea Setsi sa Mangolo-tsoetsa ka Lidiia Suslova

Moralo oa Ender Turing Mofuta oa Software ea Tlhahlobo ea Setsi sa Mangolo-tsoetsa ka Lidiia Suslova ke mohlodi oa khau e hlomphehang haholo ya 2023 Moputso oa Boronse oa A' Design Award Mokhahleong oa Tlhodisano oa Moralo wa Sebopeho, Tshebedisano le Boiphihlelo ba Mosebedisi.

BoronseSebopeho· 2023

Ender Turing Mofuta oa Software ea Tlhahlobo ea Setsi sa Mangolo-tsoetsa

ka Lidiia Suslova

Moreki: Ender Turing

Mohlodi oa Moputso oa Boronse oa A' Design Award Mokhahleong oa Sebopeho

22,531

Mohlodi oa Khau ea Netefaditsweng

Netefatsa Boemo ba Khau bakeng sa #149673

Etela Sebaka Sheba ho A' Design Award Tokollo ya Boraditaba Baeche ya Moputso

Mebala

Moralo

Mabapi le Ender Turing Mofuta oa Software ea Tlhahlobo ea Setsi sa Mangolo-tsoetsa

Ender Turing ke sethala sa software se thehiloeng lefaufaung bakeng sa taolo ea boleng ba setsi sa mangolo-tsoetsa. Boto ea taolo ea nakong ea sebele e lumella babeledi le batsamaisi ho beha leihlo tshebetso ka lintlafatso tsa nako ea sebele. Litekanyo tse bontšoang li kenyelletsa palo ea mangolo-tsoetsa, nako e tloaelehileng ea ho sebetsana le mongolo-tsoetso, tharollo ea mongolo-tsoetso oa pele, litekanyo tsa khotsofalo ea bareki le ho fumaneha ha baemeli. Tlhahlobo ea pele e itlhophelang ea mangolo-tsoetsa le lipuisano tsa marang-rang e khutsufatsa lipainelo tsa boleng, 'me lifiliti tse nolofalitsoeng li lumella ho khethoa ha mohlala o monyenyane ho fapana le puisano ka 'ngoe. Likarete tsa lintlha li ka thehoa le ho fetoloa ntle le tšehetso ea boenjiniere. Sebopeho sa sethala se fumaneha ka lipuo tse fetang tse leshome.

Kamohelo

Mabapi le Moputso oa Boronse oa A' Design Award

Ender Turing Mofuta oa Software ea Tlhahlobo ea Setsi sa Mangolo-tsoetsa ke mohapi oa Moputso oa Boronse oa A' Design Award, kananelo e hlomphehang haholo e fuoang meralo e ikhethang e tsejoang ka boqapi, bohlale ba ho qapa le bohlale. Bahapi ba Boronse ke lintho tse nang le bokhoni ka ho felletseng le boqapi bo hlollang tse tiisang boiphihlelo bo bongata, mohopolo le bokhoni ba ho iphumana ba baqapi ba tsona. Ender Turing Mofuta oa Software ea Tlhahlobo ea Setsi sa Mangolo-tsoetsa e lebelletsoe ho ba ntho e hlomphuoang haholo, e ratoang le e ratehang, e nang le boprofeshenale bo bonahalang le mehopolo e mecha. Moralo ona o hapileng Moputso oa Boronse oa A' Design Award o kenyelletsa mekhoa e metle ka ho fetesisa bonong, mahlaleng, moralong le theknolojing mme o hlahella ka ho tsepamisa maikutlo lintlheng tse nyenyane. Ender Turing Mofuta oa Software ea Tlhahlobo ea Setsi sa Mangolo-tsoetsa e kutloisisa litlhoko hantle, e ama litakatso, e hapa maikutlo mme e tsosa takatso ea ho tseba.

Ka Mora Moralo

Sete ya Boraditaba

Litlhaku, ditho tsa sehlopha le lintlha tsa botekgeniki tse fanoeng ke moetsi wa moralo.

Boleng bo Ikhethang: A real-time dashboard allows supervisors and managers to monitor call center KPIs and agent progress with live updates. It displays metrics such as call volume, average handle time, first call resolution, customer satisfaction scores, and agent availability. This helps identify areas of improvement, detect issues, and optimize call center performance through proactive management and data-driven decision-making, leading to improved efficiency, customer satisfaction, and business outcomes.
Boholo le Phuthelo: The primary function of Ender Turing engine is to help users create and train AI models that can understand and generate human-like language. The platform provides a range of NLP capabilities, including text classification, sentiment analysis, entity recognition, language translation, and text summarization, among others. Users can build custom models using Ender Turing's drag-and-drop interface or integrate pre-trained models into their applications using the platform's API.
Lithegi: Artificial intelligence (AI), Natural language processing (NLP), Machine learning, Text classification, Sentiment analysis, Data processing, Text summarization API, Cloud-based platform, Language modeling, Text mining, Call center, Scoring
Theknoloji ya Tlhahiso: We used a data-driven design approach to gather feedback for our design, which involved observing and interviewing customers of the product. All of our decisions were based on identifying user pain points and areas of frustration. As a result, we developed a product solution that eliminates most pain points for call center supervisors, increases their productivity, reduces stress and frustration, and features an intuitive UX interface covered with more than 10 language options.
Phephetso ya Moralo: Experiments were done to test hypotheses and validate design decisions. We found that our users are non-technical and prefer a more prominent communication style. We avoid using complex logic constructions and heavy interface decisions. Our UX solutions are continuously simplified.
Dipatlisiso: As we maintain ongoing communication with our end users, including data center supervisors and stakeholders, we have collected data through a range of research methods, such as interviews, surveys, and observations.

This has allowed us to gain valuable insights into usesr needs, preferences, and behaviors, which will inform the design of effective solutions that meet their specific requirements.
Kgothaletso: Call centers handle up to 1000 calls per agent per day. Measuring and analyzing this data with metrics similar to those used by humans is crucial to improve client service and call center productivity. A productive call center saves businesses revenue and promotes polite and friendly communication with customers, enhancing a business's reputation, improving customer satisfaction, and increasing their overall quality of life.
Nako ya Projeke: The project started in June 2020 in Tallinn, Estonia. The Design for the Project started at January 2022 and still developing and enhancing. Product was nominated as winner at Latitude59 pitching competition in May 2022, Tallinn, Estonia and took 2nd place at Infoshare Startup Contest where been selected among 500 startups from all over the world in October 2022, Gdansk, Poland.
Tsamaiso ya Mosebetsi: Our solution provides a 20-fold increase in speed for scoring calls and chats. This is made possible by automated AI pre-scoring and call center quality assurance. Using flexible filters, you can randomly select only a small number of calls and chats for quality monitoring, rather than monitoring all of them.

Moreover, our system allows for flexible setup of scorecards with no limitations. This means that you will no longer need engineers or Excel sheets to modify or create new scorecards.
Diphatente: Scientific publications:

1. Transferability Evaluation of Speech Emotion Recognition Between Different Languages
January 2022
DOI:10.1007/978-3-031-04812-8_35
In book: Advances in Computer Science for Engineering and Education (pp.413-426)

Ievgen Iosifov
Olena Iosifova
Oleh Romanovskyi
Vladimir Sokolov

2. Automated Pipeline for Training Dataset Creation from Unlabeled Audios for Automatic Speech Recognition

July 2021
DOI:10.1007/978-3-030-80472-5_3
In book: Advances in Computer Science for Engineering and Education IV (pp.25-36)

Oleh Romanovskyi
Ievgen Iosifov
Olena Iosifova
Vladimir Sokolov

3. Analysis of Automatic Speech Recognition Methods

January 2021
Conference: Cybersecurity Providing in Information and Telecommunication SystemsAt: Kyiv, Ukraine

Olena Iosifova
Ievgen Iosifov
Vladimir Sokolov
Oleh Romanovskyi
Igor Sukaylo

4. Sentence Segmentation from Unformatted Text using Language Modeling and Sequence Labeling Approaches

October 2020
DOI:10.1109/PICST51311.2020.9468084
Conference: 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T)

Ievgen Iosifov
Olena Iosifova
Vladimir Sokolov

5. Methods and components of natural language processing

August 2020 Adaptive automatic control systems 1(36):93-113
DOI:10.20535/1560-8956.36.2020.209780

Olena Iosifova
Ievgen Iosifov
Oleksandr Rolik

6. Techniques Comparison for Natural Language Processing

June 2020
DOI:10.5281/zenodo.3895814
Conference: 2nd International Workshop on Modern Machine Learning Technologies and Data ScienceAt: Lviv-Shatsk, Ukraine

Olena Iosifova
Ievgen Iosifov
Oleksandr Rolik
Vladimir Sokolov