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Advanced Research in Computing (ICARC), International Conference on

作者： K. P. W. D. V. Kariyawasam P. N. Kahandagamage M. R. R. Fernando J. M. Fernandopulle Vishan Jayasinghearachchi Kapila Dissanayaka Dept. of Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka Dept. of Computer Science Sri Lanka Institute of Information Technology (SLIIT) Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments.

关键词： Scalability Plantations Systems architecture Crops Real-time systems Internet of Things Resource management Reliability Monitoring Stress

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Application of Machine Learning in Identification of Best Teaching Method for Children with Autism Spectrum Disorder

arXiv

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arXiv 2023年

作者： Zoana, Zarin Tassnim Shafeen, Mahmudul Wahed Akter, Nasrin Rahman, Tanvir Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of Computer and Information Sciences College of Engineering University of Delaware DE United States

A good teaching method is incomprehensible for an autistic child. The autism spectrum disorder is a very diverse phenomenon. It is said that no two autistic children are the same. So, something that works for one child may not be fit for another. The same case is true for their education. Different children need to be approached with different teaching methods. But it is quite hard to identify the appropriate teaching method. As the term itself explains, the autism spectrum disorder is like a spectrum. There are multiple factors to determine the type of autism of a child. A child might even be diagnosed with autism at the age of 9. Such a varied group of children of different ages, but specialized educational institutions still tend to them more or less the same way. This is where machine learning techniques can be applied to find a better way to identify a suitable teaching method for each of them. By analyzing their physical, verbal and behavioral performance, the proper teaching method can be suggested much more precisely compared to a diagnosis result. As a result, more children with autistic spectrum disorder can get better education that suits their needs the best. © 2023, CC BY.

关键词： Diseases

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A Secure Token-Based Approach for DHCP Client Authentication and Replay Attack Prevention

A Secure Token-Based Approach for DHCP Client Authentication...

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International Conference on computer and information Technology (ICCIT)

作者： Asaduzzaman Jony Muhammad Nazrul Islam Rahat Ahmed Talukder Dept. of Information and Communication Technology Bangladesh University of Professionals (BUP) Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Dynamic Host Configuration Protocol (DHCP) plays a crucial role for assigning IP address and other related parameters to clients. Due to lack of client authentication and anti reply mechanism the DHCP protocol is susceptible to DHCP starvation attack. Although most of the existing researches proposed digital certificate based DHCP client authentication that involves a trusted third party named certificate authority (CA), but securing the private key of certificate authority is very challenging and risky task. Again, traditional MAC binding technique has been used to authenticate DHCP clients which could be easily evaded by MAC spoofing attack. Moreover, SMS based one time password (OTP) is also suggested to authenticate DHCP clients which is susceptible to Man-In-the-Middle (MITM) attack as OTP is transmitted in plain text. Therefore, the objective of this research is to propose an effective DHCP framework to authenticate DHCP clients and prevent DHCP replay attack. To achieve this objective, at first, DHCP starvation and DHCP replay attack and their consequences are briefly demonstrated. Secondly, a python-based secured DHCP framework was proposed that authenticates DHCP clients and prevents DHCP replay attack by exchanging security tokens uniquely generated from clients Pre-Shared Key (PSK), MAC address, PIN and system time. Finally, the effectiveness and validation of the proposed DHCP framework is demonstrated using the Multivendor Network Emulation Software (EVE-NG).

关键词： Phase shift keying Protocols Prevention and mitigation Authentication Public key Production Passwords Software Pins Servers

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‘Do Not Disturb’: An Implication Design To Be Alerted But Less Stress

‘Do Not Disturb’: An Implication Design To Be Alerted But ...

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International Seminar on Application for Technology of information and Communication (ISemantic)

作者： Weerachaya Jarupreechachan Kotcharat Kitchat Thattapon Surasak Dept. of Computer Engineering Faculty of Engineering at Kamphaeng Saen Kasetsart University Nakhon Pathom Thailand Dept. of Computer Science and Information Engineering National Central University Taoyuan Taiwan Dept. of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

The pervasive utilization of smartphones, while augmenting personal and professional communications, also elicits mental stress and disrupts the work-life balance. Users necessitate staying connected to critical updates without undue disruption, necessitating an optimized, user-friendly smartphone interface. However, current "Do Not Disturb (DND)" features fall short of providing the desired customization options, thereby contributing to heightened anxiety and diminished productivity. Consequently, this study proposes a design modification to the DND mode by incorporating an "Exceptional Apps Notification", feature thereby aiding in alleviating stress and enhancing focus by permitting essential alerts from selected apps or contacts. In other words, the users only need to turn off notifications for specific applications, not every application. This approach addresses the problem of users fearing they will miss some important notifications. Therefore, the design process employed principles of selective attention and color theory, and user experience was assessed through questionnaires and psycho-physiological feedback. Early findings suggest the potential for stress reduction, albeit with opportunities for further optimization, such as including a memory feature for user preferences and allowing selective access for specific contacts.

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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Future Machine Learning and Data science (FMLDS), IEEE International Conference on

作者： Md. Habib Ehsanu Hoque Mrittika Mahbub Mohd Razali Bin Md Tomari Rezaul Bashar Md. Golam Rashed Dipankar Das Dept. of Information and Communication Engineering University of Rajshahi Bangladesh Dept. of Computer Science and Engineering Pundra University of Science and Technology Bangladesh Dept. of Electronic Engineering University Tun Hussein Onn Malaysia (UTHM) Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification.

关键词： Training Road accidents Virtual assistants Traffic control Streaming media Real-time systems Robustness Road safety Shearing Vehicles

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AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Severity Prediction, and Post Effects

AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Sev...

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International Conference on information Technology Research (ICITR)

作者： Nivasheni B Fernando V.G.S.O. Srihari M Sarah M.I.L. Vishan Jayasinghearachchi Samadhi Chathuranga Rathnayake Dept. of Computer Science Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

This paper aims to examine the applicability of machine learning models for aiding in the diagnosis and management of arboviral illnesses, especially Dengue. The study focuses on three key objectives: Diagnosis of Dengue, prediction of prognosis of severe disease, and the identification of the post dengue effects. Multiple strategies were used to address these challenges. They are feature engineering based multiclass classification of arboviral diseases, enhancement of standard conventional machine learning algorithms for binary classification of severe Dengue and finally MLSOL enhancing ensemble model for drawing future effects of Dengue infections. The comparison of traditional models included both Random Forest, XGBoost, and also ensemble method while applying the feature selection methods of SFA and RFE. This study presents the comparison of traditional individual models and optimal ensemble method for the multiclass classification in arboviral diseases. In the case of binomial classification, the best accuracy level was 0.93 achieved by the Random Forest with RFE feature selection compared to Logistic Regression as well as the Gradient Boosting. Furthermore, the study proposed an MLSOL augmented ensemble approach to handle label imbalance problem in the dataset which in turn substantially enhanced the prediction accuracy. This approach decreased Hamming Loss to 0. 12 and increased the F-measure to 0.82, this has the capability of handling the imbalanced dataset and improving the predictive accuracy and performance. This machine learning framework offers great potential for clinical use in such aspects as early intervention and in effects following Dengue fever. It is meant to help healthcare practitioners and policy makers on how to better prevent and combat Dengue in Sri Lanka.

关键词： Accuracy Machine learning algorithms Medical services Feature extraction Ensemble learning Object recognition Prognostics and health management Viruses (medical) Random forests Standards

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Mining Road Traffic Accident Data for Prediction of Accident Severity

Mining Road Traffic Accident Data for Prediction of Accident...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bahiru, Tadesse Kebede Manjula, V.S. Akele, Tadesse Birara Tesfaw, Engdaw Ayalew Belay, Tadesse Destaw Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information Technology Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Pg Dept of Computer Science Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information System Amhara Kombolcha Ethiopia Wachamo University School of Computing and Informatics College of Engineering and Technology Dept. of Information Technology SSNPR Hosaena Ethiopia

ISBN: (纸本)9781665474511

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities. © 2023 IEEE.

关键词： Support vector machines

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Multicarrier Waveforms for Moon-to-Earth RF Transmission

Multicarrier Waveforms for Moon-to-Earth RF Transmission

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IEEE Metrology for Aerospace

作者： Talha Faizur Rahman Vuk Marojevic Claudio Sacchi Dept. of Electrical and Computer Engineering Mississippi State University Mississippi State MS Dept. of Information Engineering and Computer Science University of Trento Trento Italy

More than 50 years after the last Apollo mission, the race to the Moon has started again. In the framework of new missions, it will be of paramount importance to establish a reliable communication link between the vehicles (or astronauts) present on the Moon's surface and an Earth station. Alternative choices consider radio-frequency (RF), optical, and hybrid optical-RF solutions. In this paper, we focus on the RF transmission case. Recent works evidenced how diversity is required to face multipath fading issues due to the reflection of electromagnetic rays on the Lunar surface. For this reason, suitable waveforms, which are robust against multi path distortion should be considered. In this work, we propose multicarrier Orthogonal Frequency Division Multiplexing (OFDM), Single-Carrier OFDM (SC-OFDM), and Constant-Envelope OFDM (CE-OFDM). The comparative results will be discussed in order to select the best solution to cope with the various tradeoffs between link performance, spectral efficiency, and computational complexity.

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SroX: Accelerating Hidden Weight Creation in Neural Networks via Spectral Relaxation and X-Iteration

SroX: Accelerating Hidden Weight Creation in Neural Networks...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Harshit Agarwal Parth Middha Ayush Thakur Sofia Singh Shipra Saraswat Neha Bhatia Dept. of Computer Science & Engineering Amity University Noida India Amity Institute of Information Technology Amity university Noida India Dept. of Information Technology Panipat Institute of Engineering & Technology Panipat India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper proposes SroX, a novel approach for accelerating Hidden Weight creation in neural networks, which is a critical component of neural network training that can significantly impact the overall efficiency of the training process. SroX leverages spectral relaxation and X-Iteration to reduce the computational time required for Hidden Weight creation, enabling faster training times without sacrificing accuracy. Our approach addresses the long-standing challenge of efficient Hidden Weight creation, which has been a major bottleneck in neural network training. We demonstrate the efficacy of SroX through extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and ImageNet, where SroX outperforms existing methods, including Random Projection and Gradient-Based Optimization, in terms of training time and accuracy. The proposed approach provides a promising solution for large-scale neural network applications, where efficient training is crucial, and has the potential to revolutionize the field of neural network research and applications, driving innovation and breakthroughs in areas such as computer vision, natural language processing, and beyond. By providing a faster and more efficient way to create Hidden Weights, SroX can enable researchers and practitioners to tackle complex problems with unprecedented speed and efficiency, leading to significant advancements in the field of artificial intelligence.

关键词： Training Technological innovation computer vision Accuracy Scalability Neural networks Benchmark testing Natural language processing Communications technology Optimization

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Design and Development of Lemmatizer for KannadaVachana Sahithya - A Hybrid Approach

Design and Development of Lemmatizer for KannadaVachana Sahi...

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Multimedia Processing, Communication & information Technology (MPCIT), International Conference on

作者： Lavanya C. B H.S. Nagendra Swamy Pavan Kumar M.P. Dept. of studies in Computer Science University of Mysore Mysore India Dept. of Information Science & Engg. J.N.N College of Engineering Shimoga India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

This paper presents a hybrid approach for the design and development of Lemmatizer for Kannada Vachana Sahithya. Kannada has a rich literary history and is officially recognized as one of the India's classical languages. The existing work on Kannada Morphological Analysis and Lemmatization is conducted on Kannada nouns and verbs from “Kannada Shabdha Kosha” and Kannada text from Government Documents, Circulars and news from All India Radio. In this paper, a hybrid lemmatizer for Kannada Vachana Sahithya is designed and evaluated on an annotated dataset of 5,735 words. In 5735 words of KVS, 80% (4588) are considered for training and 20% (1147) words are used as testing data. The experimental results with the hybrid lemmatizer demonstrate that the proposed model achieves approximately 90% accuracy across various types of KVS input text.

关键词： Training Accuracy Translation Government Named entity recognition Machine learning Machine translation History information technology Testing

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