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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Chukka, Santhaiah Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fault finding and crack detecting method, the manual method is involved which is very slow and consist of many errors. By considering all these challenges and issues in track detection method, we have implemented an advanced IoT based railway crack and fault-finding system. It uses advanced sensors, microcontroller, GPS and GSM module which can detect and report faults of the railway track. The system uses real time mechanism. The system is implemented and tested on a prototype railway track to detect crack or any fault. The internet of things technology added modern and accurate fault-finding feature in the system. The IoT plays an important role in implementing advance technologies, as it uses sensors and advance microcontroller. The accuracy rate of fault and crack detection is around 97%, which is for 2 mm crack. Along with this GPS module accurately track fault location. GSM module successfully send the alert message quickly after detecting the fault in the track. Hence the response time is very less and accurate. This system is implemented in order to ensure safety for the railway track and prevent the accidents. Hence the system is useful for improving overall transport safety. © 2024 IEEE.

关键词： Microcontrollers

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Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation

Granularity is crucial when applying differential privacy to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Vu, Doan Nam Long Igamberdiev, Timour Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

ISBN: (纸本)9798891761681

Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice of granularity at which DP is applied is often neglected. For example, neural machine translation (NMT) typically operates on the sentence-level granularity. From the perspective of DP, this setup assumes that each sentence belongs to a single person and any two sentences in the training dataset are independent. This assumption is however violated in many real-world NMT datasets, e.g., those including dialogues. For proper application of DP we thus must shift from sentences to entire documents. In this paper, we investigate NMT at both the sentence and document levels, analyzing the privacy/utility trade-off for both scenarios, and evaluating the risks of not using the appropriate privacy granularity in terms of leaking personally identifiable information (PII). Our findings indicate that the document-level NMT system is more resistant to membership inference attacks, emphasizing the significance of using the appropriate granularity when working with DP. © 2024 Association for Computational Linguistics.

关键词： Differential privacy

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Exploring Artificial Neural Network Models for c-VEP Decoding in a Brain-Artificial Intelligence Interface

Exploring Artificial Neural Network Models for c-VEP Decodin...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Miao, Zhengqing Meunier, Anja Žák, Michal Robert Grosse-Wentrup, Moritz Justus Liebig University Giessen Department of Psychology Giessen Germany University of Vienna Doctoral School Computer Science Faculty of Computer Science Vienna Austria University of Vienna Research Group Neuroinformatics Faculty of Computer Science Vienna Austria University of Vienna Vienna Cognitive Science Hub Vienna Austria University of Vienna Research Network Data Science Vienna Austria

ISBN: (纸本)9798350386226

The Conversational Brain-Artificial Intelligence Interface (BAI) is a novel brain-computer interface (BCI) that uses artificial intelligence (AI) to help individuals with severe language impairments communicate. It translates users' broad intentions into coherent, context-specific responses through an advanced AI conversational agent. A critical aspect of intention translation in BAI is the decoding of code-modulated visual evoked potentials (c-VEP) signals. This study evaluates five different artificial neural network (ANN) architectures for decoding c-VEP-based EEG signals in the BAI system, highlighting the efficacy of lightweight, shallow ANN models and pre-training strategies using data from other participants to enhance classification performance. These results provide valuable insights for the application of ANN models in decoding c-VEP-based EEG signals and may benefit other c-VEP-based BCI systems. © 2024 IEEE.

关键词： Chatbots

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimize...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Aly, Salah A. Bakhiet, Ali Balat, Mazen Badya University Faculty of Computing & Data Science Giza Egypt Fayoum University Faculty of Science Computer Science Dept. Fayoum Egypt Computer Science Dept. October 6th City Giza Egypt E-Japanese University of Science & Tech. Cs & It Dept. Alexandria Egypt

ISBN: (纸本)9798350367560

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology. © 2024 IEEE.

关键词： Lung cancer

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Hybrid Genetic Algorithm and Ant Colony Algorithm for Solving Travelling Salesman Problem 9

Hybrid Genetic Algorithm and Ant Colony Algorithm for Solvin...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Thongpiem, Jirawat Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (纸本)9798350352214

The traveling salesman problem (TSP) is a combinatorial optimization problem and a NP-complete problem. It is a well-known problem for comparison of algorithm performance. Many researchers try to solve the TSP by the meta-heuristic algorithms. Ant Colony Optimization (ACO) algorithm is a popular method to solve the TSP. To enhance performance of ACO for solving TSP, the recently proposed improving the ACO algorithm by the results from searching for 2-OPT algorithms that are applied with pheromones of ants. Moreover, when the population of ACO occurs trapping in local optimum, the pheromone of ants is re-initialized to solve trapping in local optimum problem. This algorithm is called Modified Ant Colony Optimization with updating Pheromone by Leader and Re-initialization Pheromone (MACO-LR). However, performance searching of MACO-LR can be improved. In this research, we propose to solve TSP using Hybrid Genetic Algorithm and Ant Colony Algorithm (HGAACO) to optimize the total travelled distance. The proposed technique is tested on twenty-three maps from the Traveling Salesman Problem Library (TSPLIB) and it is composed with MACO-LR. The results from the experiment show the proposed technique can enhance performance searching of MACO-LR. © 2024 IEEE.

关键词： Ant colony optimization

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Griffin: Fast Transactional database Index with Hash and B+-Tree 20

Griffin: Fast Transactional Database Index with Hash and B+-...

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20th IEEE International Conference on e-science, e-science 2024

作者： Nakazono, Sho Bessho, Yutaro Kawashima, Hideyuki Nakamori, Tatsuhiro NTT Computer and Data Science Laboratories Tokyo Japan Keio University Faculty of Environment and Information Studies Kanagawa Japan

ISBN: (纸本)9798350365610

Index access is one of the dominant performance factors in transactional database systems. Many systems use a B+tree or one of its variants to handle point and range operations. This access pattern has room for performance improvement. Firstly, point operations can potentially be processed in O(1) with a hash table. Secondly, to ensure serializability of transactions, range operations incur overhead from phantom avoidance techniques that involve additional processing or synchronization, such as an extra traversal of the B+-tree. To address these issues, we propose a hybrid index architecture, Griffin. For point operations, Griffin has a hash table that provides access paths in O(1) time, along with a B+-tree. For phantom avoidance, Griffin employs a precision locking method, which does not involve additional traversal of the B+-tree. Despite its hybrid architecture, Griffin transparently provides linearizable operations and an interface of a single database index. We built a Griffin index combining a hash table and BwTree. Compared to a baseline index that is composed of a BwTree only, it achieves up to 3.1x higher throughput in a point operation dominant workload, and up to 5.4x higher throughput in a range operation dominant workload. © 2024 IEEE.

关键词： database systems

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Utilization of Machine Learning in Breast Cancer Detection 25

Utilization of Machine Learning in Breast Cancer Detection

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Momani, Mutaz Yousef, Nidal Aljawazneh, Qotadeh Issa, A.T. Abuowaida, Suhaila Alazaidah, Raed Khouj, Mohammed Talat Batyha, Radwan Elsoud, Esraa Abu Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al-Albayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Zarqa University Department of Data Science and Ai Faculty of Information Technology Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Breast cancer is the most prevalent cancer among women and can be deadly, necessitating early detection to enhance patient outcomes and treatment effectiveness. Recently, Machine Learning (ML) techniques have shown potential in medical diagnostics, especially in breast cancer screening. ML algorithms examine extensive medical databases, including patient records, to identify subtle patterns and anomalies indicative of cancer. These algorithms excel at recognizing intricate patterns that may be missed by human analysis. Thereby, increasing the precision of breast cancer diagnoses. By evaluating and analyzing medical data, ML algorithms can detect minute anomalies, facilitating early identification of potential cancer cases. A significant advantage of ML-based breast cancer detection is its capacity for continuous learning and adaptation. In this study, we utilized 34 classifiers and five feature selection methods on three datasets. The first dataset comprises 286 instances with 10 integer-type features. The second dataset includes 699 instances with 10 integer-type features, and the third dataset consists of 286 instances with 13 integer-type features. For dataset 1, Filtered Classifier and J-48 yielded the best results, while the Bayesnet classifier performed best on dataset 2. In dataset 3, SimpleLogistic and LMT achieved the best outcomes. Regarding feature selection, the study showed that the GainRatioAttributeEval, InfoGainAttributeEval, and CorrelationAttributeEval techniques are the best among the feature selection methods tested in the study. © 2024 IEEE.

关键词： Lung cancer

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Multi-Task Learning Frameworks to Classify Food and Estimate Weight From a Single Image 20

Multi-Task Learning Frameworks to Classify Food and Estimate...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Siwathammarat, Pakin Jesadaporn, Panas Chawachat, Jakarin Chiang Mai University Data Science Research Center Faculty of Science Thailand Chiang Mai University Faculty of Medicine Department of Internal Medicine Thailand Faculty of Science Department of Computer Science Chiang Mai Thailand

ISBN: (纸本)9798350300505

Usually, elderly patients in hospitals suffer from malnutrition because they are unable to consume food as prescribed by doctors or nutritionists. Analyzing food intake is labor-intensive and time-consuming. Therefore, machine learning is used to analyze the food intake. Major food analysis tasks include food classification and food weight estimation. The basic machine learning approach to this problem is to combine a food classification model with a food weight estimate model sequentially. When we deployed it, we found that a large amount of memory and models were required. One solution is to use multi-task learning. In this study, we proposed multi-task learning frameworks that could recognize food and predict weight based on a single image. The performance of our frameworks was compared to the baseline models, which only utilized either regression or classification. Although baseline accuracy is higher, our framework has MAPE values that are lower than the baseline. To improve the performance, we explored different approaches for weighting loss, including manual weighting and auto weighting, using uncertainty and auxiliary tasks. From the experiment, our results showed that our multi-task learning framework that adjusted the weight of loss using auxiliary tasks outperformed the baseline models in terms of MAPE and Accuracy. Moreover, we demonstrate our framework when scaling up the backbone from ResNet50 to ResNet101 and ResNet152. © 2023 IEEE.

关键词： Image classification

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation 29

The Evolution of Zero Trust Architecture (ZTA) from Concept ...

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29th International Conference on Information Technology, IT 2025

作者： Nasiruzzaman, Md Ali, Maaruf Salam, Iftekhar Miraz, Mahdi H. Xiamen University Malaysia School of Computing and Data Science Malaysia Universiteti Metropolitan Tirana Faculty of Computer Science and IT Albania The Doctoral College United Kingdom Xiamen University Malaysia Mahdi H. Miraz Is with the School of Computing and Data Science Malaysia Wrexham University School of Computing Faculty of Arts Science and Technology United Kingdom University of South Wales Faculty of Computing Engineering and Science United Kingdom

ISBN: (纸本)9798331517649

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA's benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats. © 2025 IEEE.

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