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A Review of Algorithms's Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms's Complexities on Different Valued So...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Majeed, Asad Iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan Ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 IEEE.

关键词： Computational complexity

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Course Recommendation System for Company Job Placement Using Collaborative Filtering and Hybrid Model 13

Course Recommendation System for Company Job Placement Using...

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13th International Conference on Data Science, Technology and Applications, DATA 2024

作者： Park, Jaeheon Lee, Suan Lee, Woncheol Kim, Jinho Kangwon Joint Program of Software Convergence Course Kangwon National University Chuncheon Korea Republic of School of Computer Science Semyung University Jecheon Korea Republic of SeedsSoft Co. Ltd. Chuncheon Korea Republic of Dept. of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9789897587078

This study introduces a novel recommendation system aimed at enhancing university career counseling by adapting it to more accurately align with students' interests and career trajectories. Recognizing the challenges students face in selecting courses that complement their career goals, our research explores the efficacy of employing both collaborative filtering and a hybrid model approach in the development of this system. Uniquely, this system utilizes a company-course recommendation method, diverging from the traditional student-course paradigm, to generalize company-course relationships, thereby enhancing the system's recommendation precision. Through meticulous feature engineering, we improved the performance of the NeuMF model. Our experiments demonstrate that the proposed method outperforms other models by 10% to 79% based on the mAP metric, suggesting that the proposed model can effectively recommend courses for employment. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Collaborative learning

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Performance Evaluation of Quantum-Resistant TLS for Consumer IoT Devices

Performance Evaluation of Quantum-Resistant TLS for Consumer...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Jessica Bozhko Yacoub Hanna Ricardo Harrilal-Parchment Samet Tonyali Kemal Akkaya Dept. of Computer Science Auburn University Auburn USA Dept. of Electrical and Computer Engineering Florida International University Miami USA Dept. of Software Engineering Gumushane University Gumushane Turkey

Post-quantum (PQ) cryptographic algorithms are currently being developed to be able to resist attacks by quantum computers. The practical use of these algorithms for securing networks will depend on their computational and communication efficiency. In particular, this is critical for the security of wireless communications within the context of consumer IoT devices that may have limited computational power and depend on a constrained wireless bandwidth. To this end, there is a need to evaluate the performance of widely used application layer security standards such as transport layer security (TLS) to understand the use of the existing PQ algorithms that are being evaluated by NIST as a replacement to the current cryptographic algorithms. This paper focuses on two widely used IoT standards Bluetooth Low Energy (BLE) and WiFi to find out the optimal performing PQ algorithm for their security when used in end-to-end connections over the Internet. By implementing the capability for IP over BLE and all options of TLS connection establishment, we developed a client-server IoT testbed to measure the efficiency of PQ key encapsulation mechanisms (KEMs) and PQ digital signature algorithms. The test results showed that Kyber512 is the ideal KEM while Falcon-512 and Dilithium2 are the best signatures for BLE and WiFi devices. Based on this outcome, we developed a mechanism for IoT devices with multiple communication interfaces, that dynamically chooses a PQ KEM algorithm based on the MAC layer protocol being used at the time.

关键词： Wireless communication Performance evaluation Encapsulation Heuristic algorithms Resists Internet of Things Cryptography

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Towards the Diagnosis of Heart Disease Using an Ensemble Learning Approach

Towards the Diagnosis of Heart Disease Using an Ensemble Lea...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Zheng Zhang Liangliang Xu Yida Bao Sanjeev Baskiyar Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics Auburn University Auburn AL United States

Heart disease is one of the leading causes of death worldwide. Understanding the presence of heart disease is crucial for timely intervention and effective management. However, it is still a challenging task to accurately diagnose and treat heart disease. In this paper, we propose an ensemble machine learning model that combines the predictive power of three state-of-the-art machine learning algorithms: BERT (Bidirectional Encoder Representations from Transformers), FT-transformer, and XG-Boost. Through extensive training and evaluation, we assess the performance of our model using established metrics such as accuracy, precision, recall, F1-score, and receiver operating char-acteristic (ROC) curve analysis. The results obtained demonstrate the effectiveness and validity of our machine learning system in predicting heart disease accurately. This paper presents a promising approach that can assist healthcare professionals in making informed decisions and improving patient outcomes, enhances our understanding of heart disease patterns, and contributes to the development of effective diagnostic and treatment approaches.

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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Cocopal - A Deep Learning Based Intelligent System to Certify and Standardize the Quality of Coconut Based Products 4

Cocopal - A Deep Learning Based Intelligent System to Certif...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Gunawardana, K.H.R. Hemachandra, M.G.S.P. Hettiarachchi, N.M. Deshan, M.P.N. Ganegoda, Devanshi Weerasinghe, Lokesha Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The procedure of certifying and standardizing the quality of the coconut-based products is done manually in Sri Lanka at precent. It is a time consuming and labor- intensive task and is conducted by experts. In most cases, the quality is decided solely by visual inspections by buyers and suppliers, with no scientific basis. The paper reports the capacity of bringing modern technology solutions such as Artificial Intelligence (AI), Machine Learning (ML), Image Processing (IP), and decentralized storage to aid in the certification and standardization of the quality of raw *** showed that the accuracy of the proposed system is in the 86% to 90% range and showed that this technique could beimproved and used as an alternative to manual techniques. © 2022 IEEE.

关键词： Deep learning

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Multi-Label Classification of CS Papers Using Natural Language Processing Models

Multi-Label Classification of CS Papers Using Natural Langua...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Zheng Zhang Amit Das Mostafa Rahgouy Yida Bao Sanjeev Baskiyar Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics Auburn University Auburn AL United States

computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task.

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Woosung Kang Jinkyu Lee Youngmoon Lee Sangeun Oh Kilho Lee Hoon Sung Chwa Dept. of Electrical Engineering and Computer Science DGIST Republic of Korea Dept. of Computer Science and Engineering Sungkyunkwan University (SKKU) Republic of Korea Dept. of Robotics Hanyang University Republic of Korea Department of Software & Computer Engineering Ajou University Republic of Korea School of AI Convergence Soongsil University Republic of Korea

ISBN: (数字)9798350358414

ISBN: (纸本)9798350358421

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity.

关键词： Scheduling algorithms Memory management Graphics processing units Artificial neural networks Machine learning Drives Real-time systems

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Train a Robot to Climb Staircase Using Vision-Base System 10

Train a Robot to Climb Staircase Using Vision-Base System

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10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Jayawardana, J.T.H. Dilshan, H.V.V. Wijethilaka, R.G.K.H Balasooriya, T.D. Rajapaksha, U.U.S. Harshanath, S.M.B. Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

Currently, robots are used for different types of work, such as the manufacturing industry, healthcare, and the hotel industry. According to the current epidemic situation, the usage of robots was increased because of the need to reduce human interaction. As a result, they have to walk around the workplace, because of that, they may have to climb staircases. The world has many types of robots. Here the selected robot is a humanoid. This proposal is concerned with how to detect the staircase, count steps, get dimensions of it, and move the robot on it by keeping body balance. First, want to know what the objects are, then walk. The identified images from the image sensors will get as input. The technology stack that is used for image analysis is a method related to computer vision in deep learning. Other than that, while climbing the stairs robot needs to identify whether the staircase is over or not. Here we introduce a new concept: get the number of steps required to climb before climbing the stairs. It is related to how humans identify things by seeing and making decisions. The need to take the dimensions of the stairs is that when considering the stairs, they have their height, width, and range. Therefore, it is imperative to calculate the dimensions of each staircase separately. Creating a balance system similar to human balance is a great advantage in robotics. To implement such a system, there is an upright pose controller to allow the robot to walk stably by preventing tilting of the robot during walking on uneven floor. In general, for us to do that we need to calculate the global inclination of the floor is a key factor. It can be measured with a 2-axis accelerator meter, and it is installed in the inertial sensor. © 2022 IEEE.

关键词： Stairs

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Integrating Computational Thinking Into the Curricula to Bridge the Skill Gap in engineering Education

Integrating Computational Thinking Into the Curricula to Bri...

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Frontiers in Education (FIE) Conference

作者： Nasrin Dehbozorgi Maysam Nezafati Mehdi Roopaei Dept. of Software Engineering Kennesaw State University Marietta GA USA Dept. of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA Dept. of Electrical and Computer Engineering) University of Wisconsin-Platteville Platteville WI USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This work-in-progress research-to-practice paper presents an intervention on integrating computational thinking modules into a software engineering course. The national consensus on the significance of computational thinking has prompted the expansion of related educational initiatives over the past decade. Since the definition of computational thinking by Wing in 2006, this concept has gained significant attention within the educational community. Particularly this surge of interest has led to extensive research into its conceptual foundations and subsequent integration into educational curricula since 2013. National initiatives have since emerged to incorporate computational thinking into the educational system. Furthermore, as artificial intelligence and computing systems become increasingly integrated into daily life, there is a growing demand from industries for a workforce and graduates adept.at critical thinking and problem-solving. Aligned with this national movement, our study presents a two-year institutional initiative, aimed at integrating computational thinking into the software engineering program. The software engineering discipline extensively involves design thinking and problem-solving skills. However, we noticed that these higher-level skills are not imparted early in the program to teach students this method of thinking and approaching problems. To bridge this skill gap, we developed a set of computational thinking modules and integrated them into a gateway course in the software engineering program. Over two years, we implemented this intervention in an introductory-level course and evaluated its impact on students' computational thinking skills by analyzing their responses to a standard Computational Thinking Assessment survey. The results showed significant improvement in most components. These early findings underscore the effectiveness of integrating these computational thinking modules into the gateway courses, regardless of the specific co

关键词： Bridges Surveys Industries Logic gates Problem-solving Surges engineering education Artificial intelligence Standards software engineering

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