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51st International Colloquium on Automata, Languages, and Programming, ICalP 2024

作者： Charalampopoulos, Panagiotis Gawrychowski, Pawel Ghazawi, Samah School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Institute of Computer Science University of Wroclaw Poland Department of Computer Science University of Haifa Israel Department of Software Engineering Braude College of Engineering Karmiel Israel

ISBN: (纸本)9783959773225

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form UU, a string of length n can contain as fragments. It turns out that this is always O(n), and the bound cannot be improved to sublinear in n [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms – it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an n × n 2D string is O(n2 log2 n) and that they can be computed in O(n2 log2 n) time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of n × n 2D strings with Ω(n2 log n) distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an n × n 2D string is O(n2 log n) and they can be computed in the worst-case optimal O(n2 log n) time. As expected, our solution heavily exploits the periodic structure implied by occurrences of quartics. However, the two-dimensional nature of the problem introduces some technical challenges. Somewhat surprisingly, we overcome the final challenge for the combinatorial bound using a result of Marcus and Tardos [JCTA 2004] for permuta

关键词： Optimization

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An Integrated Machine Learning Framework for Effective Classification of Water

An Integrated Machine Learning Framework for Effective Class...

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： aleem, Isha Ur Rehman, Attique Javaid, Sabeen ali, Tahir Muhammad University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9781665476010

Water is an essential need for all living things. The problem people are facing especially in urban areas is the diseases that have been caused by water which is mainly Dengue or malaria because there are many metals such as ammonium aluminum silver and other bacterial or viral things present in water. The first step toward a healthy lifestyle is to drink purified water. In this paper, the methodology that has been used here is to detect whether the water people are using for drinking purposes is safe enough to use or not in the data set and the specific methodology is binomial type as yes or no, 912 for positive states and 7084 negative states which means that 0.8864 for 0 and 0.114 for 1. The ratio for negative is far higher than the positive one. We tested the model in two ways first with simple feature extraction, smote Upsampling and with vote ensemble. In smote Upsampling accuracy with the random forest is 88.21 % (highest). The classification error of random forest is 11.7% and with the highest rate is 94.73% which is recall and specificity is 81.71% which is the lowest in the random forest whereas with vote ensemble the combinations of two algorithms have been used and the highest accuracy is from naive Bayes and KNN and the accuracy from them is 96.00% with the classification error of 6% only its precision rate is higher which is 98.89% and lowest specificity of 75.00%. © 2023 IEEE.

关键词： Machine learning

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An Intelligent Technique for the Effective Prediction of Monkeypox Outbreak 3

An Intelligent Technique for the Effective Prediction of Mon...

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Mir, Azka Rehman, Attique Ur Javaid, Sabeen ali, Tahir Muhammad University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350322125

Monkey pox is a viral disease that spreads from animals especially monkey to human beings. Monkey pox outbreak has been increasing at a concerning rate. The outbreak of monkey pox has infected several people around the world. The extent and intensity of the disease can be determined by the occurrence of the symptoms. The objective of this paper is to predict monkeypox virus so that outbreak can be administered before monkeypox looms as a viral health hazard. The monkeypox case has been classified as confirmed, discarded and suspected. This paper uses a supervised machine learning model to predict the status of monkey pox case. To diagnose monkeypox virus case, clinical parameters are required. The selected dataset contains the parameters of monkey pox virus from April 2022 onwards. It is necessary to predict the monkey pox outbreak before it effects more valuable lives. For the purpose of this paper, supervised machine learning techniques have been used to determine the performance of the dataset through experimental analysis. The experiment has been performed using various classifiers such as Decision tree, Naïve Bayes etc. to compare the accuracy rate. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 93.51% using K-NN classifier with k=5 neighbors. Rapid miner platform is used for the application of the machine learning tools and techniques for the purpose of this research. This paper highlights the effective machine learning steps for the development of highly accurate model using machine learning techniques on monkey pox outbreak dataset. © 2023 IEEE.

关键词： Viruses

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Towards Modeling Functional Requirements From Tacit Knowledge

Towards Modeling Functional Requirements From Tacit Knowledg...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Jafar, Rameen almufareh, Maram Fahaad Ashraf, Sundas Khan, Bushra Butt, Wasi Haider Humayun, Mamoona National University of Sciences and Technology Department of Computer and Software Engineering Pakistan College of Computer and Information Sciences Jouf University Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350327595

Requirements Elicitation is the process of identifying system needs by talking with stakeholders who have a direct or indirect effect on the requirements. Requirements may be derived from several sources and are one of the most important phases of requirement engineering. We need to perform different elicitation techniques to find out the user's needs. Pre-requirements tracing is the process of determining the origins of a specific demand. Typically, certain needs arise that have no apparent source, but stakeholders will testify to their importance. Such criteria, however, are most likely dependent on tacit or tacit-like information entrenched in the issue domain. Tacit knowledge is one of the most common challenges for requirement analysts. This study contributes to constructing a systematic literature review for exploring multiple ways to model functional requirements using tacit knowledge. We have identified and analyzed 10 studies. In these research papers, different authors have discussed different methods to gather functional requirements from tacit knowledge. © 2023 IEEE.

关键词： Elicitation Techniques Requirement engineering Tacit-knowledge

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Mobile-based Invasive Species Identifier for Biodiversity Conservation 17

Mobile-based Invasive Species Identifier for Biodiversity Co...

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17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Hussien, Ilham Regasa, Tsion Debele, Deborah El Moutaouakil, Amine United Arab Emirates University Department of Computer Science and Software Engineering College of Information Technology Al Ain United Arab Emirates United Arab Emirates University Department of Electrical and Communication Engineering College of Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350303568

alien species have the potential to significantly threaten and harm the living beings and the environment. The search for cheap and easy-to-deploy solutions has been among the priorities of environmentalists and researchers. In this work, we developed a mobile application that enables users to differentiate registered plants in the gene database in the UAE from alien species, and offers a handy method for alerting authorities through a series of easy steps. The mobile application will be useful for agencies or government personnel who oversee the conservation of the environment in order to keep track of local and alien species. The mobile application will be also useful for people to use it in their farms and gardens. © 2023 IEEE.

关键词： Biodiversity

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A Robust Development of Calorie Prediction Methodology based on Artificial Intelligence Assisted Machine Learning Model 4

A Robust Development of Calorie Prediction Methodology based...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Naga Venkata Ramakrishna, G. Sreelakshmi, S. Diwakar, Meenakshi Ramkumar, S. Vinithra Banu, T. Thilagam, T. University of Technology and Applied Sciences Al Musannah Faculty of Engineering Oman Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India S.A Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

Modern people's hectic schedules are a direct result of the ways they live and the responsibilities they have at work. On the other hand, maintaining a healthy lifestyle demands consistent physical exertion. Obesity is a result of people not paying attention to what they eat. As a result of modern living, obesity is on the rise. As a result, in order to maintain a healthy weight, people pick their food and exercise habits accordingly. People should know how many calories they consume and how many calories they burn. Calorie counts are easy to get online or on product labels, so it's not hard to keep track of one's calorie intake. There aren't many tools available to help you keep track of the calories you burn. To keep up with the hectic pace of modern life and the constant search for shortcuts, we built a system that uses machine learning to track post-workout calorie burn and development. By taking a few attributes as input, this system can approximate the calories burned, which will show daily growth and motivate people to exercise more. This paper presents an innovative ML model for accurate calorie prediction;it's called the Improved Learning Model for Calorie Prediction (ILMCP). To test how well it works, the authors cross-validate it with the traditional Extreme Gradient Boosting (XGBoost) algorithm. As a result, it is the quickest approach and may be utilized for object identification in real-time. Next, we'll apply the ILMCP algorithm to the picture for segmentation. Foreground extraction with little to no user input requires it. Following picture segmentation, we use the probing object's known volume to determine the food item's volume. Once the volume has been determined, the food item's mass may be determined using formulae. Then, the connection between weight and calories can be used to compute the food item's calories. © 2024 IEEE.

关键词： Image segmentation

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Segmentation of Retinal Blood Vessels in Fundus Images Using Fully Convolutional Networks

Segmentation of Retinal Blood Vessels in Fundus Images Using...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Vijayalakshmi, G. al-Farouni, Mohammed Sourav Sravani, Ch Satya Kumar Singh, Rajesh Saiharsha, R. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Aditya Engineering College Department of Computer Science & Engineering Surampalem India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

Segmentation is mandatory to analyze retinal blood vessel indication for diagnosis of various ophthalmic and systemic disorders. This paper takes a look at this problem, specifically, the application of Fully Convolutional Networks (FCNs) in this whole process, with a primary focus on the U-Net architecture. In this work, the authors conduct the experiments on three datasets, namely DRIVE, STARE and CHASE_DB1, and prepare the images for modeling through normalization, resizing, and data augmentation. Due to the binary structure of the task, the loss function of the proposed method is binary cross-entropy along with the Dice coefficient, and the model is trained with the Adam optimizer. The improvement scores that was obtained shows that the proposed model has a high accuracy, precision, recall and F1 score on all the datasets used and has proven to be better than other existing methods. The study establishes the usefulness of FCNs in segmenting the tree structure of the retinal blood vessel structures in uniterrupted manner, and presented promising improvement for early diagnosis and treatment in the clinical application. it emphasizes on the necessity of utilizing far improved deep learning architectures and more holistic training mechanisms in striving to create more accurate diagnosis support systems in medical image analysis. © 2024 IEEE.

关键词： Ophthalmology

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Self-Care Assessment for Daily Living Using Machine Learning Mechanism

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computers, Materials & Continua 2022年第7期72卷 1747-1764页

作者： Mouazma Batool Yazeed Yasin Ghadi Suliman A.alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer *** and optical sensors are widely used to assess the daily living activities in healthy people and people with certain *** conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity *** this paper,an RGB-D-based ADL recognition system has been ***,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a *** on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)***,random forest classifier has been used to classify activities into different *** n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.

关键词： Angular geometric features decision tree classifier human activity recognition probability based incremental learning ridge detection

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Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab ali Mustafa Qamar Rehan Ullah Khan Waleed albattah Khalil Khan Shabana Habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related *** surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this *** research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd *** theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the *** Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in *** crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major *** have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority *** algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

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A Multifaceted Approach for Identifying Propaganda on Social Networks 1

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Khanday, Akib Mohi Ud Din Baniya, Pashupati Bhushan, Bharat alazzawi, Esraa Mohammed Jamil, Akhtar Agrawal, Atul United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Department of Computer Science and Engineering IEC College of Engineering and Technology Greater Noida India

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Online Social media facilitate human interaction, information sharing, and opinion expression in digital era. Misinformation, disinformation and propaganda propagate quickly through social media platforms and impacts public opinion, political debate, and societal views. Understanding the mechanics of propaganda dissemination and its repercussions on society is critical for reducing its negative implications. Recognizing propaganda will allow individuals to safeguard their values and resist attempts to manipulate them. In this paper we propose a hybrid feature selection technique to classify the propaganda and non propaganda tweets. Data is prepared based on the annotation scheme and features are selected by fused various state of art feature selection techniques. Various machine and ensemble learning classifiers are trained and tested based on the features selected. The results showed that the there is an increase in the performance of all the classifiers trained on the proposed hybrid selection criteria. In future Deep learning techniques may be incorporated to improve the efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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