5th International Conference on Block chain and Internet of Things (BIoT 2024)

May 18 ~ 19, 2024, Zurich, Switzerland

Accepted Papers


Practical Terminology: General Foundations, Purposes and Practitioners’ Tools

Samuel Benga and Julien-Hervé Mbappé, LingaTech Consulting, Douala, Cameroon

ABSTRACT

For several decades now, terminology work has no longer been carried out on paper with pencils, as it certainly was when the thesis by Eugen Wüster (1898-1977), founder of a terminology theory and figurehead of the Vienna Circle, was published (Roche, Terminologie et Ontologie, 2005). Indeed, since the 1990s and the proliferation of “digital humanities” (Abiteboul & Hachez-Leroy, 2015), an avalanche of precision tools has become available to terminology practitioners more than ever, whose aim is to make knowledge representation products that are accessible to human users as well as to “artificial agents” (Carsenty, p. 193). It is against this backdrop that, in this article, we set out not only to revisit the major principles underpinning this practice, but also to present some of the essential tools in its panoply, all of which contribute to a final product in line with terminological principles and methods.

KEYWORDS

Terminology work, Ontology, Concept system, Semantic product, Knowledge representation.


Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (Anns-ac)

Yousef M. AlMutiri, Mohammed S. AlRashidi, AbdulRahman M. AlQahtani, Turki S. Alqahtani, and A.M. Sadek, General Administration of Laboratory, Saudi Standards Metrology, and Quality Organization (SASO), Riyadh, Kingdom of Saudi Arabia

ABSTRACT

In the present study, the artificial neural network (ANN) modeling technique was used to simulate the air conditioner (AC) performance test under various conditions. A backpropagation ANN models with multiple hidden layers were trained using 22 input variables and three targets. More than 800 test reports produced by the National Energy Efficiency Laboratory in KSA were used to train the ANN models. The input processing functions, neuron sizes, starting values of the weights and biases, layer transfer functions, training functions, and performance evaluation functions were discussed. The uncertainty components associated with the experimental measurements and leakage in learning in the ANN model were evaluated. The minimum replicate number of runs with resetting the weights and biases was estimated. The model was used to study the effect of airflow on the performance of the AC and identify the conditions leading to high AC efficiency.

KEYWORDS

Artificial Neural Network; Electrical Efficiency, Air Conditioner Test.


Predicting and Mitigating Delays in Cross-dock Operations: a Data-driven Approach

Amna Altaf1, Adnen El Amraoui1, Francois Delmotte1, Christophe Lecoutre2, 1Univ. Artois, UR 3926 Laboratoire de G´enie Informatique etd’Automatique de l’Artois (LGI2A), B´ethune, France, 2CRIL-CNRS, UMR 8188, University of Artois, Lens, France

ABSTRACT

This paper presents a predictive model designed to anticipate and mitigate late truck arrivals in cross-docking stations. In the absence of empirical data, a hypothetical dataset was generated. Employ- ing sophisticated machine learning techniques, our model takes into account historical arrival patterns, traffic conditions, and unforeseen delays to forecast the likelihood of late truck arrivals. The methodology, encompassing data sources and machine learning algorithms, is delineated. Three experimental configura- tions were meticulously tested and analyzed, utilizing the Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The outcomes contribute to the enhancement of preventive maintenance in cross-dock facilities, providing valuable insights for optimizing operations amid dynamic logistical challenges.


Arduino Devices as a Platform to Perform Machine Learning Algorithms: a Brief Review and Experimentation

Juan José Flores Sedano, Hugo Estrada-Esquivel, Alicia Martínez Rebollar, 1TecNM/Centro Nacional de Investigación y Desarrollo Tecnológico CENIDET, Cuernavaca, Morelos, México. C.P. 62470

ABSTRACT

At present, the Internet of Things and Artificial Intelligence are among the most relevant transformative technologies for making a smart world a reality. In this context, this paper explores the transformative synergy between the Internet of Things (IoT) and Artificial Intelligence (AI) by integrating AI algorithms into Arduino devices. The literature review has demonstrated a current need of the optimization in implementing AI algorithms on Arduino platforms. Through a empirical literature review and practical experimentation, this paper provides a comprehensive analysis of several Arduino boards, including the Portenta H7 Lite, Arduino Uno, Wemos D1 ESP8266, and Arduino Nano 33 BLE, comparing their performance for AI projects. The selection of an IoT board is emphasized based on project-specific needs and budget considerations. The research presented in this paper reveals the impact of combining IoT, AI, and Arduino on reshaping interactions with the connected world, paving the way for intelligent systems enabled for decision-making and to execute complex tasks.

KEYWORDS

Artificial Intelligence, Arduino platform, IoT.


Blockchain Technology Management in Food Traceability and Safety

Huang Jie,Fang Fang and Yee Choy Leong, School of Business and Economics, Universiti Putra Malaysia,43400 Serdang,Selangor Darul Ehsan,MALAYSIA

ABSTRACT

The role of food traceability is crucial in food safety management and quality assurance systems. The ability to effectively trace food products has increasingly influenced the purchasing decisions of Chinese consumers. While traditional food traceability systems have provided viable solutions for monitoring and tracing the quality of the food supply chain, most of these solutions rely on centralised servers, and suppliers may tamper with and hide information in the pursuit of profit. This also makes consumers suspicious of the food traceability information they receive and reduces the level of trust between participants in all parts of the supply chain. The most important features of the emerging blockchain technology are decentralisation and tamper-proof, which increases the possibility of secure and transparent food supply chain traceability management. However, the existing literature has not fully explored the development and management of food traceability systems using blockchain technology. Therefore, the main objective of this study is to design and develop a blockchain food traceability system through the study of blockchain technology for managing food traceability. This system will inject transparency and decentralisation in the supply chain to ensure food safety. The study also provides evidence for researchers and practitioners to apply blockchain for effective food traceability and has a positive effect on improving food sustainability.

KEYWORDS

Blockchain; food supply chain traceability system; food traceability management.


Data Trustworthiness: Quality Scoring

Faouzi Boufarès1, Aicha Ben Salem1, 2, and Adam Boufarès3, 1Northern Paris Computer Science Lab, LIPN, France LaMSN, Sorbonne Paris Nord University, France, 2Laboratory RIADI-La Manouba,Tunisia, 3Transactis Company, France

ABSTRACT

Nowadays, data is very important in organizations and companies. The data quality has a strong influence on decisions and the consequences can be very significant. It is very important to have an idea of how much trust you should place in the data before any process starts. For this reason, it is essential to carry out a checklist of data validity before using them and initiate, as much as possible, a process of data correction and enrichment. In this paper we present a method to visualize errors in a data source. A score is assigned to each error. The database checklist covers existing and missing values homogenizations, functional dependencies, duplicate and similar rows. Several measures are performed to assist the correction of anomalies.

KEYWORDS

Data Quality, Data trustworthiness, Data Anomalies, Constraints, Management Rules Scoring Data, Data Cleaning, Machine Learning, Datasets, Structured or not Structured Data, CSV or JSON files.


Enhancing Deepfake Detection Through Ensemble Learning and Transfer Learning with Facial Features

Nadeem Qazi and Iftikhar Ahmed, University of East London, Tietoevry Finland

ABSTRACT

Deepfake technology, facilitated by deep learning algorithms, has emerged as a significant concern due to its potential to deceive humans with fabricated content indistinguishable from reality. The proliferation of deepfake videos presents a formidable challenge, propagating misinformation across various sectors such as social media, politics, and healthcare. Detecting and mitigating these threats is imperative for fortifying defenses and safeguarding information integrity. This paper tackles the complexities associated with deepfake detection, emphasizing the necessity for innovative approaches given the constraints of available data and the evolving nature of forgery techniques. Our proposed solution focuses on leveraging facial features and transfer learning to discern fake videos from genuine ones, aiming to identify subtle manipulations in visual content. We systematically break down videos into frames, employ the Haar cascade algorithm for facial recognition, and utilize transfer learning to extract discriminative features. We evaluate multiple pre-trained models, including VGG16, ConvNeXtTiny, EfficientNetB0, EfficientNetB7, DenseNet201, ResNet152V2, Xception, NASNetMobile, and MobileNetV2, for feature extraction. Subsequently, we feed these features into a Deep Artificial Neural Network (DANN) for deepfake detection and employ ensemble learning to combine the strengths of the best-performing models for enhanced accuracy. We found that the ensemble model comprising ConvNextTiny, EfficientNetB0, and EfficientNetB7 showed enhanced accuracy in detecting deep fakes compared to alternative models achieving up to 98% accuracy through ensemble learning.

KEYWORDS

Deepfake, video classification, Transfer learning, EfficentNet, DenseNet, Ensemble learning, Haar Cascade .