Dharmender Salian, Department of Information Technology, University of the Cumberlands, New York, USA
This study aims to review studies related to Artificial Intelligence (AI) maturity models (MM) in automotive manufacturing systematically. Digitalization is required for automotive small and medium enterprises (SMEs) if they want to remain competitive. In the USA, a large segment of workers is employed in SMEs. Due to scarce funds, SMEs had not been aggressive in digitalization but benefits like operational efficiency, quality improvement, cost reduction and innovative culture make it appealing and beneficial to customers. Digitalization of operations that employ Artificial Intelligence techniques is becoming more commonplace in the past few years. Using the lens of an AI maturity model, this paper reviews the state of AI applications in SMEs.
Industry 5.0, Maturity Model, AI maturity model, Maturity level & digitalization.
Aicha Aggoune, Department of Computer Science, LabSTIC, University of 8th May 1945, Guelma, Algeria
The NoSQL databases allow the storage and processing of huge data in a distributed environment, ensuring high availability, scalability, and fault tolerance. Unlike relational databases, where the data are presented by the relational model, the NoSQL databases provide four data models that are different from one another (key-value, Columnar, Document, and Graph). In this context, novel opportunities may arise when leveraging the selection of the NoSQL database, which may be more suitable than another for representing such data. This paper presents the MCMM algorithm for transforming the MongoDB database to the Cassandra database and vice versa. The proposed algorithm is based on the use of translation and inverse translation rules. Two examples are used to demonstrate how the MCMM algorithm works.
MongoDB, Cassandra, Translation rules, Inverse translation rules, Mapping between NoSQL.
Hiro Horie1, Sinan Chen1, Masahide Nakamura1,2, and Kiyoshi Yasuda3, 1Kobe University, 1-1 Rokkodai-cho, Nada, Kobe, 657-8501, Japan, 2RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan, 3Osaka Institute of Technology 5-16-1 Omiya, Asahi-ku, Osaka, 535–8585 Japan
Our research group is conducting research of a system to support the lives of the elderly at home. We developed “Rakuraku Video Service”(Rakuraku means easy.), a service that obtains information on the interests and preferences of elderly people and recommends YouTube videos based on this information. The purpose of this service is to help the elderly relieve stress and relax by watching videos. However, it has not been tested yet whether this service has such an effect on the elderly. We conduct an experiment to evaluate the service. In conducting the experiment, we will collaborate with a previous study, “PC-Mei” which aims to watch over the elderly and support their daily lives with a virtual agent. The experiment was conducted to obtain evaluations of watched videos and a questionnaire. From the results, it is clear that the service is useful in relieving stress among the elderly.
Elderly at home, watching videos, stress relief, individual adaptive type, spoken dialogue agent.
Aoi Nagatani1, Tasuku Watanabe1, Yuya Tarutani1, Yoshifumi Kamae1, Shun Sato1, Marin Shoda1, and Masahide Nakamura2, 1Graduate School of System Informatics, Kobe University Rokkodai-cho 1–1，Nada-ku，Kobe，Hyogo，657–8501 Japan, 2the Center of Mathematical and Data Science, Kobe Univ.
In recent years, Japan has been actively pursuing the realization of zero carbon cities. However, significant challenges persist, including a lack of effective methods for local governments to communicate zero carbon initiatives to their citizens. This has resulted in limited awareness among citizens about how to participate in zero carbon initiatives. To address these issues, the authors develop a gamified application aimed at promoting zero carbon activities in this research.Through a case study conducted in Sanda City, Hyogo Prefecture in Japan, the authors report the progress of its social implementation.
Zero carbon, zero carbon city, gamification, web application, local government.
Takuya Nakata1, S. Chen2, S. Saiki3, and 2M. Nakamura2, 1Graduate School of Engineering, Kobe University, Rokkodai-cho 1–1, Nada-ku, Kobe, Hyogo, 657–8501 Japan, 2Center of Mathematical and Data Sciences, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe, 657-8501, Japan, 3School of Data and Innovation, Kochi University of Technology, 185 Miyanokuchi, Tosayamada, Kami, Japan
Research concerning the personalization of services encompasses approaches such as machine learning and dialogue agents; however, the explainability of the recommendation process remains a challenge. Previous studies have proposed dialogue-based needs extraction systems utilizing the 6W1H need model, but extracting complex needs using simple natural language processing proved challenging. In this research, we embark on the development of an Application Programming Interface (API) that extracts user needs from natural language by leveraging the rapidly advancing Large Language Models (LLM), and on constructing a dialogue-based needs extraction system using this API. For evaluation, we conducted a verification on 100 needs with the aim of assessing the accuracy and comprehensiveness of the outputs from the needs extraction and restoration API. Through this study, it became feasible to extract needs with high accuracy and comprehensiveness from complex natural language using LLM.
Personalization, need, large language model, natural language processing, dialogue agent.
Fernando W.A.M.A.R, Jinadasa U.G.O.C Amarasinghe B.P.A, Mandalawatta M.T, Uthpala Samarakoon and Manori Gamage, Faculty of Computing, Sri Lanaka Institute of Information Technology, Malabe, Sri Lanaka
The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counsellor chat, the application offers a holistic approach to STD management.
STD, Machine Learning, NLP, Neural Network , Image Processing.
H.N Haputhantri, E.G.R.M Piyathissa, W.K.K.H Ariyarathna and G.R.P.S Wijewickrama, Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
The proposed online voting and ranking system for elections in Sri Lanka aims to increase efficiency, reduce time consumption and improve transparency. By leveraging the Internet and digital technology, the proposed system provides a convenient, secure and user-friendly platform for voters to cast their votes from anywhere. Strong authentication methods, such as facial recognition and fingerprint verification, ensure the validity of the voting process and reduce the risk of fraud and manipulation. Voters must verify their identity before voting, and all votes are encrypted to protect voter privacy. The system automatically tabulates and aggregates votes, reducing human error and speeding up the release of results. Real-time updates improve transparency and public confidence in the electoral system. A comprehensive evaluation was conducted to determine the systems effectiveness, including technical framework, security precautions, user approval, and legal considerations. Surveys and interviews were used to assess the usability, safety and fairness of the system. The integration of facial recognition and fingerprint authentication into the proposed online voting and grading system will further enhance its security and reliability. These technologies will help ensure the integrity of the election process and protect the privacy of voters.
Central Counting Station, Voter Registration, Voter Authentication, Data Visualization, Transparent Qualities.
Abderezak Touzene, Ahmed Al Farsi, Nasser Al Zeidi, Department of Computer Science, College of Science, Sultan Qaboos University, Oman
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic (Big Data) are challenging to store, deal with and analyse to detect normal or cyber-attack traffic. In this paper we developed an Intrusion Detection System (NMF-IDS) based on Non-Negative Matrix Factorization dimension reduction technique to handle the large traffic datasets and efficiently analyses them in order to detect with a good precision the normal and attack traffic. The experiments we conducted on the proposed IDS-NMF give better results than the traditional ML-based intrusion detection systems, we have got an excellent detection accuracy of 98%.
Intrusion Detection Systems, Machine Learning, Dimensionality Reduction, IoT traffic.
Andrew Chain1, Logan Coons2, Adam Ramos3, Kya Richardson4, Kamaluddeen Usman Danyaro5, Bimal Nepal6, Abdullahi Mujaheed Saleh7, Hashir Sohrab8, 1Department of Computer Science & Engineering, Texas A&M University, Texas, USA 2Department of Chemistry and Biochemistry University of Nevada, Las Vegas, USA, 3Department of Biochemistry & Biophysics, Texas A&M University, Texas, USA 4Department of Bioengineering and Biomedical Engineering, North Carolina A&T State University, North Carolina, USA, 5Department of Computer & Information Science, Universiti Teknologi PETRONAS, Malaysia, 6Department of Engineering Technology & Industrial Distribution, Texas A&M University, Texas, USA, 7Department of Computer & Information Science, Universiti Teknologi PETRONAS, Malaysia, 8Department of Industrial & Systems Engineering, Texas A&M University, Texas, USA
Brain cancer is on the rise globally, with a significant increase in adult brain tumor cases in the last two decades. Detecting and treating brain tumors is challenging due to delayed diagnosis, asymptomatic presentation, and size and shape variations. Gliomas, slow-growing brain tumors, are classified by grade and type. These classifications are useful in predicting the tumors growth rate and likelihood of recurrence. Brain tumors are categorized as benign or malignant. Medical image processing methods can be time-consuming, and accurate grading and typing guidance are scarce. Convolutional neural networks are a deep learning model that can automatically learn and extract notable features from MRI images and is our selected machine learning tool to accomplish accurate classification of brain tumors. It is important to recognize brain tumors early on, so that treatment can be given early in the progression of the disease.
Brain Tumor Classification, Convolutional Neural Network, MRI Images, High-grade Glioma, Image Processing.
Cristian Daniel Alecsa, Technical University of Cluj-Napoca, Cluj-Napoca, Romania, Romanian Institute of Science and Technology, Cluj-Napoca, Romania
In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM (kernel mean matching) method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The codes associated to our Python implementation can be found at https://github.com/CDAlecsa/Generalized-KMM.
Kernel mean matching, quadratic optimization, density ratio estimation, loss function.
Gurleen Kaur, Bakul Gupta, Ashima Anand, Thapar Institute of Engineering and Technology, India
The security of digital images is crucial since they often contain sensitive and confidential data. Unauthorized access to this data could result in severe penalties for the parties involved. Despite the availability of highly secure algorithms, security remains a significant concern due to the rapid emergence of new technologies that can breach it. Thus the proposed work implements a technique that makes the confidential data inaccessible to intruders. Hence fragile type of data hiding technique is used where even with the slightest tampering to the image by an attacker, the information i.e. watermark image is completely destroyed, hence preventing it from unauthorized access. Also, a hybrid transform including DTCWT and NSST is used to fuse two medical images to form a more sophisticated output image, which serves as the final watermark. Further, the zero watermarking model is implemented using the ResNet 50 DL model for more precise results and extraction of feature maps. Embedding the actual image in the carrier image could make the watermarking detectable especially when it is fragile, hence Zero Watermarking overcomes this also by virtual embedding. Moreover, the algorithm employs the avalanche effect of SHA512 for highly secure authentication, further strengthening the security of the system. Overall, the proposed method is an effective way to ensure the security of digital images with confidential data.
Zero watermarking, Image Fusion, RDWT, Encryption, Medical images, Deep Learning.
Josephine (Hsin) Liu1, 2, Phoebe (Yun) Liu1, 2, Joseph (Yu) Liu1, 2, Emily X. Ding1, Robert J. Hou1, 1Vineyards AI Lab,, Auckland, New Zealand, 2Rangitoto College, Auckland, New Zealand
This paper explores the applications of Artificial intelligence (AI) techniques for classifying Deoxyribonucleic Acid (DNA) sequences by us three high school students under teaching and supervision. It presents the processes we did in this research, including investigating DNA sequences, understanding AI models, coding implementation, experiments, and developing a demo for users. During the research, a couple of analogies were introduced to explain AI information and concepts understandably. They were displayed using interesting images to give the high school students a better understanding, and we have successfully achieved our goal of Auto Recognition of DNA Sequences. We first transformed the DNA sequences into human-like language. Then we employed Natural Language Processing (NLP) and Multi-layer perceptron (MLP) to complete sequence classification into 7 gene families from 3 organisms (humans, dogs, and chimpanzees). During this exciting research, we deeply understood the biological and mathematical knowledge we learned in class and adapted them to our research (e.g. DNA-related information for analysis of the experiments, sets, functions, vectors, and matrix, etc., to the classification model). Finally, we used Python and TensorFlow to implement it. The experiments have shown that our project succeeded in achieving high accuracy. In addition, we developed a demo for the user to access the classifier easily.
DNA Sequences, Auto Recognition, Natural Language Processing(NLP), Multi-layer Perceptron (MLP).
Büşra Öztürk1 and Yakup Genç2, 1Computer Engineering Department, Gebze Technical University, Kocaeli, Türkiye, 2Computer Engineering Department, Gebze Technical University, Kocaeli, Turkey
In todays technologies, artificial intelligence systems are frequently used. In these systems, the development phase is as important as the final product. Visualization provides convenience to the user when monitoring data and the model during data collection and development. Augmented reality interfaces offer an effective environment for the user. We often see augmented reality used in various applications alongside artificial intelligence. The use of these interfaces during the development of artificial intelligence systems will provide an immersive experience with a positive impact on the users perception. This study examines research conducted in two dimensions on deep learning models and explores what can be accomplished in three dimensions. By adding augmented reality interfaces as an additional dimension, a three-dimensional experience is provided to the user, and the results are observed. Visualization has been achieved through an application using Microsoft HoloLens 2, and it has been tested with users working with deep learning models to see what its contribution to the user will be in three dimensions.
Deep Learning Models, Visualization, Augmented Reality Interfaces.
Henok Ghebrechristos and Gita Alaghband, Department of Computer Engineering, University of Colorado-Denver, Denver, Colorado
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), it significantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100)and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.
Convolutional Neural Network, Adversary Attack, Deep Learning,Volumization, Adversary Défense, Curriculum Learning.
Rohit Khankhoje, Independent Researcher Avon, Indiana, US
As the pervasive presence of Smart AI permeates various facets of our technological landscape, the assurance of software quality for AI-driven systems emerges as an imperative of utmost importance. This scholarly paper delves into the intricate ecosystem surrounding Smart AI software, with the objective of unraveling the challenges and issues that are inherent in its quality assurance and articulating the urgent necessity for robust solutions. We undertake an exploration of the multidimensional aspects associated with the challenges in ensuring the quality of Smart AI software. These challenges span from the complexities entailed in handling biased training data to the ethical considerations surrounding the transparency of algorithms. The paper delves into the technical challenges, encompassing the intricacy of testing and the resilience of AI models, and further expands the discussion to encompass societal and ethical considerations, including concerns pertaining to privacy and the establishment of trust among users. Moreover, this paper underscores the compelling need for a comprehensive framework for quality assurance in Smart AI software, with a specific emphasis on its pivotal role in ensuring safety, reliability, and compliance with regulatory standards. The impact of quality assurance on user experience is examined, thereby highlighting the mutually beneficial relationship between the assurance of quality and user satisfaction. By exploring these challenges, issues, and the burgeoning need for effective solutions, this scholarly paper contributes to the ongoing discourse surrounding the responsible development and deployment of Smart AI software, thereby paving the way for advancements in the practices of quality assurance within this dynamic and ever-evolving technological landscape.
Artificial Intelligent, Software Testing, AI Software,Quality Assurance.
Agbesua Oluwatoyin1, Bello Samuel2, and Lawal Nurudeen3, 1,Department of Insurance with minor in Computer Science, The Polytechnic Ibadan, Nigeria, 2Department of Computer Science, Ladoke Akintola University of Technology, Nigeria, 3Department of Building Technology, Federal Polytechnic of Offa, Nigeria
This article addresses vital challenges in children with Autism Spectrum Disorder (ASD): recognizing emotions and comprehending repetitive behaviors. It employs Software Engineering to create an interactive web app for autism support using autocorrection-based learning. The software focuses on emotion-related queries and caters to ASD childrens preferences. It incorporates animated characters, aligning with research showing their appeal to ASD kids over adult interactions. Interactive elements, like sound effects, so as to create a user-friendly learning environment tailored specifically for children on the Autism Spectrum Disorder. A significant feature is autocorrection, providing constructive feedback through Human-Computer Interaction. Correct answers are highlighted in green, incorrect ones in red, and the correct ones blinked in green, reinforcing emotional understanding. Recognizing ASDs repetition tendency, the software reintroduces previously answered questions, aiding emotional comprehension in line with their learning patterns, as ASD children benefit from repeated information.
Autism Spectrum Disorder (ASD), Software Engineering, Assistive technology, Emotions, Human-Computer Interaction.
Ledesma. O, Sánchez. M.A, Lamo. P, Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de la Rioja (UNIR), 26004 Logroño, Spain
This paper analyzes the Directed Acyclic Graph (DAG)-based architecture as an alternative to Blockchain technology for critical Internet of Things (IoT) use cases. The speed of transactions and the scalability of Blockchain technology are limitations for critical IoT applications such as vaccine cold chain monitoring. A pilot project has been developed to analyze the speed of the DAG architecture. It simulates monitoring the vaccine cold chain, recording temperatures and alarms. Using the same architecture, two cases with different IoT connectivity technologies in the pilot project are defined: LoRaWAN and Sigfox. The results of these two cases show the comparison between both technologies that show that the DAG architecture can provide the necessary time delays for critical IoT use cases. The main limitation found after the execution of the two cases of the pilot project is related to the need for worldwide coverage of the communications technologies used. For this reason, the study of communications through IoT satellites with global coverage is proposed as future work.
Distributed Ledger Technology, Blockchain, Directed Acyclic Graph, Internet of Things, IOTA.
Tanuj Surve1, Amit Tyagi2, 1University of California, Berkeley, 2National Institute of Fashion Technology
Quadratic Voting has its advantages over other traditional voting systems as Quadratic Voting enables the minority group to show their intensity and preference for a cause. The paper aims to evaluate the efficacy of Quadratic Voting in decentralized governance. The paper uses a qualitative approach and secondary sources to collect the necessary information to serve the purpose of the research. The research highlighted the claimed benefits derived from Quadratic Voting which are not limited to the representation of minority groups, ensuring voter preferences and sincerity in stating preferences, aggregating information efficiently and maximizing social welfare function under assumptions. The paper also sheds light on the limitations and assumptions of Quadratic Voting. Finally, comparative analysis between Quadratic voting and other systems necessitates that the policymakers give their attention to assessing its feasibility and implementation.
Quadrating Voting, Decentralized Governance, Blockchain.
Surya Boppanaa, William Kane, and Long Ma, Department of Computer Science, Troy University, Troy, Alabama, USA
The Secured communication is the ideal approach to communicating with one another without leakage of data. Data encryption is a valuable method for protecting and securing data. This paper proposes novel reversible information, a concealing strategy for computerized pictures using a whole number-to-number wavelet change, and a companding procedure to install and recoup the mystery data and re-establish the image to its perfect state. This paper also presents the use of genetic operators in cryptography. As general information altering happens in the system, the messages should be ensured when transmitted through any system. This paper introduces another encryption method where the genetic operators cross-over and mutation are utilized to encrypt messages to give protection while the information is transmitted. The goal is to provide a better-secured environment by using encryption and a digital image watermarking method for data hiding in the image is paper gives complete guidelines for authors submitting papers for the AIRCC Journals.
Genetic Algorithm, Cryptography, Adaptive Thresholding, Companding Technique, Integer Wavelet Transform, Reversible Watermarking work Protocols.
Lu Yang. and Yan He,School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
The application of multi-sequence spreading technology in grant-free non-orthogonal multiple access can significantly improve resource utilization and have good adaptability to typical mMTC scenarios in 5G. However, in grant-free multiuser detection through multi-sequence spreading random access, the correlation between spreading sequences directly affects the performance of active user detection. Therefore, this paper proposes a grouping algorithm that can reduce the correlation between different spreading sequence groups. The algorithm performs a low correlation grouping design for the basic spreading sequence set pool based on Gaussian random matrix, further reducing the correlation. Then, compressed sensing technology is used to detect multiple users using low-correlation multi-sequence spreading grouping access. Finally, the algorithm was simulated in a GF-NOMA system based on multiple measurement vector compression sensing, and the results showed that the multiuser detection bit error rate (BER) and error detection probability (DER) of this algorithm were lower than those of other existing multi- sequence grouping methods.
Grant-Free Non-Orthogonal Multiple Access, Multi-Sequence Spreading Random Access, Multiuser Detection, Spreading Sequence Grouping.
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