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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Hariharan, Hannah Natasha Kirupananda, Abarnah Informatics Institute of Technology School of Computer Science and Engineering Colombo Sri Lanka

ISBN: (纸本)9781728185835

Sri Lanka (SL) is a nation fighting with corruption and fraud, suffering from no viable solution to eliminate land fraud. It has been a longstanding barrier for land utilization, especially after the 30-year post-war situation. There have been continuous delays and access difficulties for lengthy searches regarding the land folios from the land registry. There are acres of lands that are unutilized and under-utilized, which could otherwise generate capital income for the country. The increasing land fraud has impacted not only the country's revenue but also the lives of victims who have been unable to prove the ownership of their lands which in return, has become a serious global concern. Although the United Nations (UN) has funded in projects across multiple countries, among which Sri Lanka was also one of them, according to the reports submitted to the UN, the performance has been *** Chain of Ownership is a solution to reduce land forgery prevailing within the country by presenting a transparent land ownership portal;taking the citizen's privacy in concern and designing a solution for the outstanding problem. The proposed solution will ensure seamless and convenient access to land information to expedite and efficiently help proceed with land transfers and registrations involving the necessary stakeholders during the process. Further, all documents are guaranteed to be maintained as softcopies regardless of being obsolete and will secure against each land parcel, as a historical record. The solution also has the potential to elevate Sri Lanka's position in the 'Doing Business' indicator measured by the UN, upon improving access and transparency over the land registration information and the trust of the general public through this portal. © 2021 IEEE.

关键词： Blockchain

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity

A Review on Leveraging Language Agnostic Automated Text Data...

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Advanced Research in Computing (ICARC), International Conference on

作者： Sarith Wijesundera Suresh Michael Peiris Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages.

关键词： Sensitivity Explainable AI Reviews Semantics Transfer learning Data augmentation Search problems Natural language processing Multilingual Optimization

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Highly-Compact Wideband High-Gain Four-Element MIMO Antenna for 5G New Radio IoT

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IEEE Internet of Things Journal 2025年第12期12卷 22350-22365页

作者： Khan, Shahid Khan, Owais Shah, Syed Ahson Ali Nasir, Jamal Malik, Bilal Tariq Khan, Salahuddin Koziel, Slawomir Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad-Campus Islamabad Pakistan Gwangju Institute of Science and Technology Graduate School of Energy Convergence Gwangju Korea Republic of King Saud University College of Engineering P.O. Box 800 Riyadh11421 Saudi Arabia Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Reykjavik University Engineering Optimization and Modeling Center Reykjavik101 Iceland

Wideband millimeter-wave (mm-wave) coverage is essential for the high-speed, low-latency communication required in next-generation 5G New Radio (NR) Internet of Things (IoT) systems. This study develops a T-shaped four-element, highly compact wideband multiple-input multiple-output (MIMO) antenna covering the mm-wave n260 (37.40 GHz) and n259 (42.43.5 GHz) bands for 5G NR IoT applications. The antenna is designed on a 0.76 mm-thick Rogers RO4350B substrate with overall dimensions of 28 28 mm2 (3.3 × 3.3 λ2). For design simplicity, a T-shaped patch backed by a full-ground plane is devised to serve as the radiator of a single-element antenna with the dimensions of (Ls × Ws) mm2, optimized through a systematic three-step design process for improved performance. Moreover, the design has evolved into a 4× 4 orthogonal MIMO configuration, achieving improved gain, polarization diversity, and higher data rates, with each element exhibiting wideband characteristics across 11.5 GHz (36.5 to 48 GHz) with high gains of 9.8 dBi and 6.6 dBi at n260 and n259 5G mm-wave bands, respectively. Additionally, arc-shaped complementary split-ring resonators (CSRR) are integrated into the ground plane to significantly enhance the gain while effectively reducing mutual coupling, remarkably achieving a maximum gain of 9.8 dBi. Furthermore, the MIMO antenna exhibits an envelope correlation coefficient of less than 0.10 between any two MIMO elements encountering the required condition of © 2014 IEEE.

关键词： Microwave antennas

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Arthropodia-V1: Biodiversity Dataset for Semantic Segmentation, Quality Estimation, and Observation Extraction

Arthropodia-V1: Biodiversity Dataset for Semantic Segmentati...

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Advanced Research in Computing (ICARC), International Conference on

作者： Mohamed Ayoob Tomas Maul Tissa Chandesa School of Computing Informatics Institute of Technology Colombo Sri Lanka School of Computer Science The University of Nottingham Malaysia Semenyih Malaysia

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

This paper introduces a novel biodiversity image dataset named Arthropodia, which focuses on three crucial tasks: image quality estimation, semantic segmentation, and observation extraction (image similarity). Arthropodia is collected from tropical Malaysian forests and encompasses a diverse range of arthropod species captured under various lighting and environmental conditions. We manually annotate the dataset for each task, making it suitable for benchmarking and developing AI models applicable to biodiversity and environmental monitoring. To demonstrate its utility, we present baseline results using state-of-the-art deep learning models for each task. Our findings indicate that Arthropodia serves as a valuable resource for advancing research in the field of biodiversity conservation and ecological studies through automated image processing and analysis. The open-access availability of Arthropodia fosters further contributions from the research community, paving the way for the development of robust and accurate AI solutions for biodiversity monitoring and preservation.

关键词： Image quality Semantic segmentation Biological system modeling Estimation Lighting Optical imaging Biodiversity Environmental monitoring Environmental management Artificial intelligence

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Ultrasound Image Analysis in Breast Cancer: A Comparative Study of Decision Trees and Random Forests 16

Ultrasound Image Analysis in Breast Cancer: A Comparative St...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sivagami, S. Sathya, A. Afsin, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Bio Informatics Tamil Nadu Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for early detection and accurate diagnosis. Ultrasound imaging serves as a non-invasive diagnostic tool for identifying breast abnormalities, yet the complexity of these images presents significant interpretation challenges. This study explores the application of Machine Learning (ML) models, specifically Decision Tree (DT) and Random Forest (RF) classifiers, for the classification of breast cancer ultrasound images into benign, malignant, and normal categories. Data augmentation techniques are employed to address class imbalance and enhance model generalization using the Breast Cancer Ultrasonic Image dataset. Among preprocessing steps, pixel values were normalized to provide a consistent input for the models. RF model performed better than DT with an accuracy of 88.75% against an accuracy of 86.25% and those masks for ultrasound images contribute a lot to the overall classification accuracy. In summary, the results of our study can provide support for grand challenges in health and emphasize implications such as the potentiality of ML models to advance breast cancer diagnosis and it implies a necessity for an even further expansion that will allow complete patient outcomes optimization. © 2024 IEEE.

关键词： Radio frequency Accuracy Ultrasonic imaging Training data Predictive models Breast cancer Data models Decision trees Random forests Tumors

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Accidental Face Recognition and Detection Using Machine Learning 1

Accidental Face Recognition and Detection Using Machine Lear...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sharma, Ashish Agrawal, Piyansh Krishan Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications UP Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms. © 2023 IEEE.

关键词： Face recognition

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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Emotion Recognition using Deep Learning Techniques and Word Embeddings 6

Emotion Recognition using Deep Learning Techniques and Word ...

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6th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2021

作者： Kardakis, Spyridon Perikos, Isidoros Grivokostopoulou, Foteini Paraskevas, Michael Computer Technology Institute and Press 'Diophantus' and Computer Engineering University of Patras Informatics Department Patras Greece

ISBN: (纸本)9781728176819

Emotion recognition is a topic of vital importance in the world of Big Data. The accurate classification, analysis and interpretation of emotional content is highly desired in a wide spectrum of applications. In this work we examine the performance of Deep Learning models for an emotion recognition task. These models can also be applied to any classification task as well as text-related tasks in general. We examine the performance of deep learning methods in textual emotion recognition under different feature selection parameters such as one-hot-enconding, custom word2vec and more. An experimental study was designed and various deep learning methods were evaluated in publicly available datasets. The results are quite interesting and indicate the advantages of each method and each examined training approach. © 2021 IEEE.

关键词： Emotion Recognition

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Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

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computer Systems Science & Engineering 2022年第7期42卷 15-31页

作者： Monday Eze Charles Okunbor Solomon Esomu Nneka Richard-Nnabu Kayode Oladapo Oghenetega Avwokuruaye Abisola Olayiwola Akpovi Ominike Godwin Odulaja Oluwatobi Akinmerese Department of Computer Science Babcock UniversityOgunNigeria Department of Computer Science Admiralty UniversityDeltaNigeria Department of Computer Science National Open UniversityNigeria Department of Computer Science and Informatics Alex-Ekwueme Federal UniversityEbonyiNigeria Department of Cyber Security Admiralty UniversityDeltaNigeria Department of Computer Science Caleb UniversityImotaLagosNigeria Department of Computer Science and Information Technology Petroleum Training InstituteDeltaNigeria Department of Computer Science Tai Solarin University of EducationOgunNigeria

Knowledge propagation is a necessity,both in academics and in the *** focus of this work is on how to achieve rapid knowledge propaga-tion using collaborative study *** practice of knowledge sharing in study groupsﬁnds relevance in conferences,workshops,and class ***-nately,there appears to be only few researches on empirical best practices and techniques on study groups formation,especially for achieving rapid knowledge *** work bridges this gap by presenting a workﬂow driven compu-tational algorithm for autonomous and unbiased formation of study *** system workﬂow consists of a chronology of stages,each made of distinct *** of the most important steps,subsumed within the algorithmic stage,are the algorithms that resolve the decisional problem of number of study groups to be formed,as well as the most effective permutation of the study group participants to form collaborative *** work contributes a number of new algorithmic concepts,such as autonomous and unbiased matching,exhaustive multiplication technique,twisted round-robin transversal,equilibrium summation,among *** concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’performances in an examination,rather than through any external *** part of practical demon-stration of this work,study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty(40)participants,and partially for study groups of 50,60 and 80 *** quantitative proof of this work was done through the technique called equilibrium summation,as well as the calculation of inter-study group Pearson Correlation Coefﬁcients,which resulted in values higher than 0.9 in all *** life experimentation was carried out while teaching Object-Oriented Programming to forty(40)under-graduates between February and May *** result showed

关键词： Knowledge propagation collaborative study group unbiased matching twisted round robin exhaustive multiplication

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Comparing Ensemble Learning Techniques on Data Transmission Reduction for IoT Systems 1st

Comparing Ensemble Learning Techniques on Data Transmission...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Salah, Ahmad Bekhit, Mahmoud Alkalbani, Asma M. Mohamed, Mohamed A. Lestari, Nur I. Fathalla, Ahmed Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig Sharkia Egypt Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Ad Dhahirah516 Oman Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Kent Institute Australia SydneyNSW Australia 2P Perfect Presentation Riyadh Riyadh Saudi Arabia Computer Science Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031337420

The Internet of Things (IoT) systems include a massive number of connected devices. The communication between these devices requires a huge amount of communication. Thus, it is very crucial for an IoT system to reduce communication volumes by minimizing the amount of data transmission. There are several approaches to achieving the goal of data transmission reduction, such as data compression and dual prediction. Dual prediction schemes received more attention in comparison to data compression. The mainstream techniques for proposing dual prediction schemes in the literature can be classified into two main groups, namely, filter-based and deep learning-based methods. The filter-based methods, such as the 1-D Kalman filter, are lightweight in terms of running time and the model’s memory requirements. On the other hand, deep learning-based methods require more memory space and training time, but deep learning models are more accurate as predictive models in comparison to filter-based methods in dual prediction schemes. There are very limited efforts to utilize machine learning methods in dual prediction schemes as a compromise between the aforementioned mainstream techniques. In this work, we extended one of these limited efforts, which utilizes boosting ensemble learning as a machine learning predictive method in a proposed dual prediction scheme. The current work proposes exploring the performance gap between the three main approaches to ensemble learning, namely, boosting, stacking, and bagging. The three proposed ensemble learning models are evaluated on a real dataset and compared against state-of-the-art methods. The obtained results show that among the ensemble learning models, boosting and bagging models are better than the stacking model, but the three proposed ensemble learning models outperformed the state-of-the-art methods of comparison. For instance, the average numbers of mispredictions for all of the experiments were 580, 551, and 1,106 for boosting, ba

关键词： Forecasting

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