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A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology under the Fuzzy Set Theory Framework.

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Procedia Computer Science 2024年 246卷 1271-1280页

作者： Marcus Vinicius Leite Jair Minoro Abe Marcos Leandro Hoffmann Souza Graduate Program in Production Engineering-Paulista University-UNIP Rua Dr. Bacelar 1212-São Paulo-SP-04026002 Brazil Graduate Program in Applied Computing-Vale do Rio dos Sinos University-UNISINOS Av. Unisinos 950 - São Leopoldo-RS-93022-000- Brazil

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts’ final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system’s effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance.

关键词： Fuzzy Set Theory Fuzzy Logic Decision Making Commercial Opportunity Qualification BANT Methodology Sales Force Support Commercial Process Optimization

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Converging on mutual harmony of knowledge-based expert system and technology consultant 2

Converging on mutual harmony of knowledge-based expert syste...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Siek, M. Urian, T.W. Business Information Systems Program Information Systems Department Faculty of Computing Media Bina Nusantara University Jakarta11480 Indonesia

In the Society 5.0, the adoption of advanced technologies in many fields has large positive impacts on the way people think and behave as well as how the companies provide services for any other businesses. In adopting new technology, however, the businesses require significant efforts and deep insights on how to implement such technology that offers large benefits for their businesses. This attempt is typically in line with the one conducted by many consulting firms which provide solution services on how the new innovation and technologies can be implemented and aligned with business goals. With regards to that, this paper focuses on the implementation of a knowledge-based expert system as a technology consultant to recommend suitable technological solutions in supporting the existing teaching and learning activities at educational institutions. A number of tasks have been executed in the research in order to build a reliable expert system, such as properly using design knowledge ontology, formulating if-then rules and reasoning, expert system structure and user interface, and so forth. The resulting knowledge-based expert system demonstrates promising performance and permits to its integration with machine learning techniques for automated knowledge acquisition. This knowledge-based expert system can be a mutual supports in harmony to the human consultants on providing their technology consultancy services. © Published under licence by IOP Publishing Ltd.

关键词： Expert systems

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SHELLEY: Exploring Learning-Based Network Alignment on Biological Data

SHELLEY: Exploring Learning-Based Network Alignment on Biolo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： De Luca, Riccardo Petti, Manuela Guzzi, Pietro H. Tieri, Paolo Sapienza University of Rome Program in Data Science Rome Italy Sapienza University of Rome Diag Dept. Comp. Contr. Manag. Eng. Rome Italy Magna Graecia University Dept. Umg Catanzaro Italy Cnr National Research Council Iac Institute for Applied Computing Rome Italy

ISBN: (纸本)9798350386226

Global network alignment is the computational problem of determining the similarity between nodes of different networks to establish a one-to-one correspondence between them. It has important applications in the biological field, particularly for discovering similar roles between the elements of different systems or for transferring knowledge from a well-studied system to another. In this paper, we present SHELLEY, a tool that facilitates the development, testing, and combination of learning-based network alignment algorithms by providing a set of modules that allow for the recreation and combination of both representation learning methods (RLMs) and deep matching methods (DMMs). We then present a case study in which we apply this tool to a protein-protein interaction network (PPI), demonstrating how the representation phase of RLMs is crucial for model robustness against *** code of SHELLEY is available at: https://***/rickydeluca/shelley © 2024 IEEE.

关键词： Contrastive Learning

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Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language 1

Aspect Based Sentiment Analysis: Restaurant Online Review Pl...

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1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021

作者： Tedjojuwono, Samuel Mahatmaputra Neonardi, Clement Bina Nusantara University Business Information Systems Program Faculty of Computing and Media Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

The paper has designed a dynamic dashboard that will show a summarized information of restaurants in Indonesia on four distinct metrics which are Food, Service, Ambience and Covid Safety. Each metrics shown will have their own ratings which shows the detailed score for each aspect of the restaurant. The data inside the dashboard have been developed by using semi supervised learning of aspect-based sentiment analysis approach. The idea is to analyze past reviews/comments of each restaurant in the current restaurant's online review platform and extract the sentiment as well as the aspect of each of the reviews. The restaurant lists and the reviews have been collected through web scraping method on one of the most used online review platforms in Indonesia which is Tripadvisor. Scraped data has been cleaned through several process of data pre-processing by utilizing Sastrawi and NLTK library for Indonesian languages. The machine learning tools that will extract the aspect and sentiments in every of the reviews will be built by applying Monkeylearn machine learning platform through APIs. Cleaned datasets have been imported into the platform for data annotations of model training to identify the set of words belongs in each aspect categories as well as their sentiment values. Although after reaching the end of the analysis, this paper has concluded that accuracy of the analysis may not be ideal due to lack of negative sentiment dataset being gathered which affects the model during the training process. In conclusion, the feature has successfully been built and implemented as well as deployed into a web server which supported by Ngrok services however, there are still more room for improvement regarding the analysis of the model. © 2021 IEEE.

关键词： Sentiment analysis

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Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait 2

Multilabel Machine Learning System for Predicting Gender and...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Qomariyah, Nunung Nurul Nurul Fajar, Ahmad Yono, Vincent Kazakov, Dimitar Bina Nusantara University School of Computing and Creative Arts Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Information System Management Information Systems Management Department Jakarta11480 Indonesia University of York Computer Science Department YO10 5GH United Kingdom

ISBN: (纸本)9798350360523

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm. © 2024 IEEE.

关键词： Gait analysis

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An Analysis of Interventions with Students with Autistic Spectrum Disorder (ASD) Using Gamified Geometric Thinking

An Analysis of Interventions with Students with Autistic Spe...

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Frontiers in Education (FIE) Conference

作者： Lidiane Maciel Pereira Regina Barwaldt Vinícius Menezes de Oliveira Mateus Oliveira Jung Graduate Program in Science Education Federal University of Rio Grande Rio Grande Brazil Graduate Program in Computing Federal University of Rio Grande Rio Grande Brazil Graduate Program in Computional Modeling Federal University of Rio Grande Rio Grande Brazil

Studies have been indicating the use of digital resources for interventions with children diagnosed with Autism Spectrum Disorder (ASD), especially when it comes to supporting communication and social interaction. However, there is research that suggests the use of digital environments not only in these aspects but also to assist them in their academic knowledge, such as in their mathematical skills, specifically related to geometric thinking. From this context, Active Methodologies point to various ways of rethinking traditional teaching, one of them being gamification, which can be understood as the use of game elements in non-game situations. Gamification has shown significant benefits for this audience, proving to be an effective tool to aid in the development and learning of these individuals. Therefore, this article in its complete form presents an excerpt from a doctoral thesis that seeks to understand the geometric thinking of nine autistic participants through gamified activities in their real mode. The research is divided into two stages: the first stage involves a preliminary survey with five teachers through structured interviews, aiming to investigate the demand for gamified activities for students with ASD, as well as indicating possibilities for such activities. From the Discursive Textual Analysis (DTA) of these interviews, three categories emerged: opportunities, skills, and limitations. As for the second stage, conducted with nine participants, who are students with ASD, interventions were carried out with these activities in their real mode, which will be implemented digitally later. Through the researcher's observation analysis, based on recorded videos of the interventions, three subcategories related to the initial categories can be identified: potential interactions (opportunities); strategies for geometric thinking (skills); and gamification in the real mode (limitations). The first subcategory emerged due to the need to describe how students

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Business process mining from e-commerce event web logs: Conformance checking and bottleneck identification 2

Business process mining from e-commerce event web logs: Conf...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Siek, M. Mukti, R.M.G. Business Information Systems Program Information Systems Department Faculty of Computing and Media Bina Nusantara University Jakarta11480 Indonesia

A range of advanced methods have been formulated and utilized in the efforts of improving the business processes in many enterprises. One impacting enhancement technique is to employ process mining algorithms as modeling and analysis tools in order to provide the actual business performance by digging the event log data and finding the useful information. This paper focuses on the applications of process mining in e-commerce industry. Event log data with timestamps were retrieved and analyzed from the web databases of an e-commerce company and process mining algorithms, like inductive miner and fuzzy miner were executed for generating the actual e-commerce business processes automatically and checking their conformance with the standardized processes as well as to early detecting any bottlenecks and issues in the e-commerce processes. Several e-commerce process issues were considered, such as item procurement, product order and delivery item tracking. The process mining modeling and its statistical results indicate that process mining can provide an efficient and effective tool for modeling and analyzing the e-commerce business processes allowing for real-time process auditing and reengineering. © Published under licence by IOP Publishing Ltd.

关键词： Miners

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Developing An Automated Face Mask Detection Using Computer Vision and Artificial Intelligence 1

Developing An Automated Face Mask Detection Using Computer V...

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1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021

作者： Tedjojuwono, Samuel Mahatmaputra Sulaiman, Sheryl Livia Bina Nusantara University Faculty of Computing and Media Business Information Systems Program Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

As the number of people affected by COVID-19 keeps on rising. Importance of wearing masks and washing hands has been the most important protocol right now to prevent the spread of COVID-19. As the pandemic has been going on for almost a year now, people have already started to go around to public places whether it is to eat out, work, or grocery shopping. Many people, however, have not been wearing masks properly by only putting them below their nose or putting it down until their chin. Hence, in this project a mask detection system is made to detect people live time who are wearing or not wearing a mask and can generate a business intelligence report for the shop owner to be aware of the number of people not wearing a mask per day. This system can detect the percentage of the mask is worn properly or not. The more proper it is worn (full up to nose), the higher the percentage will be. This system is useful in a pandemic like this as it is hard to keep track of the number of people who are not wearing masks, especially in a big crowd or in a large space as one person not wearing a mask can greatly affect others. © 2021 IEEE.

关键词： Augmented reality

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An Enhanced TSP-Based Approach for Active Debris Removal Mission Planning 10th

An Enhanced TSP-Based Approach for Active Debris Removal Mis...

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10th Brazilian Conference on Intelligent Systems, BRACIS 2021

作者： Rodrigues Neto, João Batista de Oliveira Ramos, Gabriel Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos RS São Leopoldo Brazil

ISBN: (纸本)9783030917012

The extensive exploration of the Low Earth Orbit (LEO) has created a dangerous spacial environment, where space debris has threatened the feasibility of future operations. In this sense, Active Debris Removal (ADR) missions are required to clean up the space, deorbiting the debris with a spacecraft. ADR mission planning has been investigated in the literature by means of metaheuristic approaches, focused on maximizing the amount of removed debris given the constraints of the spacecraft. The state-of-the-art approach uses an inver-over and maximal open walk algorithms to solve this problem. However, that approach fails to deal with large instances and duration constraints. This work extends the state of the art, increasing its performance and modeling all the constraints. Experimental results evidence the improvements over the original approach, including the ability to run for scenarios with thousands of debris. © 2021, Springer Nature Switzerland AG.

关键词： Space debris

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GPU-Accelerated Vecchia Approximations of Gaussian Processes for Geospatial Data using Batched Matrix Computations

GPU-Accelerated Vecchia Approximations of Gaussian Processes...

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ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

作者： Qilong Pan Sameh Abdulah Marc G. Genton David E. Keyes Hatem Ltaief Ying Sun Division of Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Statistics Program Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Jeddah Saudi Arabia

Gaussian processes (GPs) are commonly used for geospatial analysis, but they suffer from high computational complexity when dealing with massive data. For instance, the log-likelihood function required in estimating the statistical model parameters for geospatial data is a computationally intensive procedure that involves computing the inverse of a covariance matrix with size $n$ × n, where $n$ represents the number of geographical locations in the simplest case. As a result, in the literature, studies have shifted towards approximation methods to handle larger values of $n$ effectively while maintaining high accuracy. These methods encompass a range of techniques, including low-rank and sparse approximations. Among these techniques, Vecchia approximation is one of the most promising methods to speed up evaluating the log-likelihood function. This study presents a parallel implementation of the Vecchia approximation technique, utilizing batched matrix computations on contemporary GPUs. The proposed implementation relies on batched linear algebra routines to efficiently execute individual conditional distributions in the Vecchia algorithm. We rely on the KBLAS linear algebra library to perform batched linear algebra operations, reducing the time to solution compared to the state-of-the-art parallel implementation of the likelihood estimation operation in the ExaGeoStat software by up to 700X, 833X, 1380X on 32GB GV100, 80GB A100, and 80GB H100 GPUs, respectively, with the largest matrix dimension that can fully fit into the GPU memory in the dense Maximum Likelihood Estimation (MLE) case. We also successfully manage larger problem sizes on a single NVIDIA GPU, accommodating up to 1 million locations with 80GB A100 and H100 GPUs while maintaining the necessary application accuracy. We further assess the accuracy performance of the implemented algorithm, identifying the optimal settings for the Vecchia approximation algorithm to preserve accuracy on two real geos

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