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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Robust and Secure Watermarking of Medical Images Using Möbius Transforms 10

Robust and Secure Watermarking of Medical Images Using Möbi...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Al-Rammahi, Atheer Sajedi, Hedieh Department of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran14155-6455 Iran

ISBN: (纸本)9798350348873

Watermarking medical images is essential to ensure data integrity, authentication, and secure information transfer in healthcare systems. This paper explores the application of Möbius transforms, a non-linear transformation technique, for robust and secure watermarking of medical images. Our approach optimizes Möbius transform parameters through an evolutionary algorithm. Comprehensive experiments subject medical images to common attacks, evaluating the robustness of the Möbius transform-based watermarking (MTW) method using performance measures. The findings demonstrate significant resilience, maintaining image quality and watermark integrity. This paper enhances medical data security, contributing to secure medical imaging systems, advancing reliability, and confidentiality in healthcare practices. ©2024 IEEE.

关键词： Image watermarking

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Artificial Intelligence Modeling for Scour Depth Prediction Upstream of Bridge Piers

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SN computer science 2023年第6期4卷 862页

作者： Fujail, Abul Kashim Md. Das, Jarita Department of Computer Science MHCM Science College Assam Hailakandi India Department of Statistics MHCM Science College Assam Hailakandi India

The failure of river bridges due to scour depth is a significant issue worldwide. The traditional scour depth estimation equations available in the literature are derived from a particular dataset and suitable for a limited range of situations. There is no general formula available in the literature which is applicable to all circumstances. The prediction of scour depth around bridge piers is essential for ensuring the safety and stability of foundations. This paper demonstrates the applicability and efficiency of hybrid artificial intelligence (AI) models, namely adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm-based artificial neural network (GA-ANN) in the prediction of scour depth upstream of bridge piers. Affecting parameters such as pier length, pier width, flow velocity, flow depth, skew and median sediment size are taken into account to predict scour depth around piers at non-cohesive bed sediments. The hybrid AI model development considered 267 field data compiled from published literature. The results show that the hybrid AI techniques are effective in predicting scour depth around bridge piers with high accuracy and outperform other traditional models. The results of the study also indicate the encouraging performance of the GA-ANN model in accurate estimation of scour depth compared to regression-based formulae as well as ANFIS model. Thus, GA-ANN model can be used as an effective tool for the prediction of scour depth at bridge piers and designing safe bridge pier structure. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANFIS Artificial intelligence Bridge pier GA-ANN Scour depth

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City 13

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Muljana, Richie Deffansyah, Syauqi Akmal Andrew, Hari Gunawan, Alexander Agung Santoso Irwansyah, Edy Pramudya, Fabian Surya Ario, Muhamad Keenan Syahputra, Muhammad Edo Achmad, Said School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368086

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard's primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang's districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development. © 2024 IEEE.

关键词： Urban planning

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Standard Multi-Layer Perceptron on Positive - Unlabeled Glycosylation Site dataset 8

Standard Multi-Layer Perceptron on Positive - Unlabeled Glyc...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Mahesworo, Bharuno Cenggoro, Tjeng Wawan Lumbanraja, Favorisen Rosyking Pardamean, Bens Statistics Department School of Computer Science Bina Nusantara University Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia Computer Science Department School of Computer Science Bina Nusantara University Indonesia Computer Science Department School of Matematics and Natural Science University of Lampung Indonesia

The implementation of computational approaches for protein glycosylation site prediction is becoming popular since the experimental-validated glycosylation data became more abundant. Some of the data were found to be wrong after the experiment was again carried out with more sophisticated technology. To solve this issue, the latest state-of-the-art model trained the model based on a positive-unlabelled algorithm. The aim of this research is to explore the possibility of an approach applying a simple neural network algorithm and still achieve high classification performance. The model proposed in this research gave competitive results with fewer preprocessing steps. Increasing the accuracy of glycosylation site prediction can complement laboratory-based methods and is very useful for understanding the role of glycosylation. © 2023 The Authors. Published by Elsevier B.V.

关键词： Glycosylation PTM Site Prediction Standard Multi-Layer Perceptron

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Impact of mastery learning instructional scheme on post-basic school students’ engagement levels in mathematics in Osogbo, Nigeria

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Discover Education 2025年第1期4卷 1-18页

作者： Akinpelu, Gabriel Akinyemi Akinpelu, Samson Adebisi Folorunsho, Medinat Salman Ameen, Khadijat Saka Adebayo, Segun Rapheal Daramola, Comfort Yetunde Department of Science Education University of Ilorin Ilorin Nigeria School of Mathematics Statistics and Computer Science University of Kwazulu-Natal Durban South Africa Department of Mathematical Science Osun State University Osogbo Nigeria Department of Computer Science Federal University Oye-Ekiti Oye-Ekiti Nigeria

The extent of engagement between students and their teachers, peers, academic and extracurricular activities goes a long way in creating a sense of belonging for students and effectual improvement in their performance in the subject matter. This study investigated the effect of mastery learning instructional scheme (MLIS) on post-basic school students’ engagement levels in Mathematics in Osogbo, Nigeria. The purpose of this quasi-experimental study was to determine the effect of MLIS on student affective, cognitive and behavioural engagement in Mathematics based on students’ gender and score levels. Two co-educational government senior secondary schools were purposively selected as the study’s population, out of which a total of 191 Post-basic School II students from two intact classes were engaged as samples from the two schools. A 31-item Student Engagement Questionnaire (SEQ) was adapted as a research instrument. SEQ was subjected to a reliability test using Cronbach Alpha and the value of 0.73 was obtained. Descriptive statistics and ANCOVA were used to examine data gathered in accordance with the hypotheses. Outcomes of the study among others revealed significant improvement in students’ cognitive, behavioural and affective engagement for MLIS class (t ₍₁₈₉₎ = 2.68, p < 0.05). Gender had a statistically significant influence on affective (F _{(1, 94)} = 2.04, p < 0.05) and behavioural (F _{(1, 94)} = 0.77, p < 0.05) engagement. It was concluded that MLIS improved students, cognitive, affective and behavioural engagement in Mathematics. Thus, the study recommended that mathematics teachers should consider using MLIS in teaching Mathematics while taking into consideration students’ sitting arrangements with respect to their gender and score levels. © The Author(s) 2025.

关键词： Affective engagement Behavioural engagement Cognitive engagement Mastery learning instructional strategy Mathematics

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BMWP: the first Bengali math word problems dataset for operation prediction and solving

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Discover Artificial Intelligence 2025年第1期5卷 1-15页

作者： Mondal, Sanchita Khatua, Debnarayan Mandal, Sourav Prasad, Dilip K. Sekh, Arif Ahmed School of Medical Science and Technology IIT Kharagpur West Bengal Kharagpur721302 India Department of Mathematics and Statistics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur512213 India School of Computer Science and Engineering XIM University Odisha Puri752050 India Department of Computer Science Uit the Arctic University of Norway Tromsø9017 Norway

Solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark data Set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of 92±2%. The data set and the code will be available at https://***/SanchitaMondal/BMWP. © The Author(s) 2025.

关键词： Recurrent neural networks

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HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES 12

HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Zihan Lee, Jason D. Chen, Yuxin Du, Simon S. Department of Electrical and Computer Engineering Princeton University United States Department of Statistics and Data Science University of Pennsylvania United States Paul G. Allen School of Computer Science and Engineering University of Washington United States

A recent line of works showed regret bounds in reinforcement learning (RL) can be (nearly) independent of planning horizon, a.k.a. the horizon-free bounds. However, these regret bounds only apply to settings where a polynomial dependency on the size of transition model is allowed, such as tabular Markov Decision Process (MDP) and linear mixture MDP. We give the first horizon-free bound for the popular linear MDP setting where the size of the transition model can be exponentially large or even uncountable. In contrast to prior works which explicitly estimate the transition model and compute the inhomogeneous value functions at different time steps, we directly estimate the value functions and confidence sets. We obtain the horizon-free bound by: (1) maintaining multiple weighted least square estimators for the value functions;and (2) a structural lemma which shows the maximal total variation of the inhomogeneous value functions is bounded by a polynomial factor of the feature dimension. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Markov processes

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Model-based framework for exploiting sensors of IoT devices using a botnet: a case study with android

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Cyber-Physical Systems 2025年第1期11卷 1-46页

作者： Khaliq, Zubair Khan, Dawood Ashraf Baba, Asif Iqbal Ali, Shahbaz Farooq, Sheikh Umar Department of Computer Science University of Kashmir Jammu and Kashmir Srinagar India AI Thndr Dubai United Arab Emirates Department of Computer Science Tuskegee University TuskegeeAL United States Data Engineering hyke.ai Dubai United Arab Emirates

Botnets have become a severe security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic, and the ever-larger number of cybercriminals are incentivising the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices used by organisations to safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators that can be exploited to monitor or control the physical environment of the entities connected to them, thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. The framework uses an enhanced centralised architecture associated with a novel ‘Domain Fluxing Technique’. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation, consequently preserving privacy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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Palm Fruit Ripeness Detection and Counting Using YOLOv8 Algorithm in PTPN IV Medan North Sumatera Indonesia 6

Palm Fruit Ripeness Detection and Counting Using YOLOv8 Algo...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Hutapea, Rael Russel Purba, Samdo Jumar Tandion, William Ferdinandz Sihotang, Erna Fransisca Angela Irwansyah, Edy School of Computer Science Bina Nusantara University Jakarta Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Oil palm plantations have become a crucial sector in the Indonesian economy. This research investigates the effectiveness of YOLOv8 in identifying the ripeness of oil palm fruits. The goal is to create a system that uses the YOLOv8 Deep Learning algorithm to detect the ripeness level of oil palm fruits. An automatic detection system will simplify and accelerate the sorting or classifying of oil palm fruits. This research utilizes datasets sourced from the oil palm plantation repository of PTPN IV Medan, North Sumatera, Indonesia. The dataset collected for this research consists of 85 images with a size of 640x640. The dataset is loaded into Roboflow and undergoes pre-processing and augmentation. The total dataset obtained after augmentation is 185 images. After obtaining the API from Roboflow and integrating it into Google Colab for YOLOv8 training, testing was performed on the provided images. The results indicated that YOLOv8 achieved an accuracy of 0.991 (99.1%) in detecting ripe, unripe, and rotten oil palm fruit. The high value of accuracy leads to the potential to increase the efficiency of oil palm plantation management and become the basis for developing AI-based agricultural technology. With further updates, the YOLOv8 model could become a handy tool in oil palm plantation monitoring and management in the future. © 2024 IEEE.

关键词： Fruits

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