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An Efficient Natural Merge Sort Incorporating Array Decomposition

An Efficient Natural Merge Sort Incorporating Array Decompos...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Kanchon Gharami Kazi Mehrab Rashid Golam Rabbi Farhana Amin Swakkhar Shatabda S.M. Shovan Md. Al Mehedi Hasan Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of Computer Science & Engineering United International University Dhaka Bangladesh Dept. of Computer Science & Engineering Missouri University of Science & Technology Missouri United States

Modifying an algorithm that has been established over many years and making it even faster has always been a fascinating and challenging area in the field of algorithms, which motivated us to take the challenge of improving the performance of Knuth’s NaturalMergeSort by reducing the runtime with considering both ascending and descending runs. The way we optimize it is by taking advantage of both ascending and descending runs, i.e., increasing the potential of the decomposition method compared to the existing algorithm. The proposed algorithm was implemented in C++, and the experiment was conducted with some random and manually prepared datasets that resulted in improving the worst case of NaturalMergeSort by an exceedingly large margin of 97.5%, demonstrating the efficiency and flexibility of our algorithm. Even for the average case, our proposed algorithm beats Knuth’s NaturalMergeSort by a slight margin, and it also outperforms traditional merge sort with 17.5% improvements. The performance and efficiency of our algorithm have been recorded and presented in graphical form by comparing time and space complexity with other competitor sorting algorithms.

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Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

引用

Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Pavan Kalyan S Arun Kumar B R Satish Kumar T Prem Raj S Pratik B Patil Pothireddy Harshini Dept. Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments.

关键词： Adaptation models Computational modeling Large language models Memory management Data structures Data models Computational efficiency Problem-solving Sustainable development Optimization

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Voice-Based Age and Gender Recognition: A Comparative Study of LSTM, RezoNet and Hybrid CNNs-BiLSTM Architecture 15

Voice-Based Age and Gender Recognition: A Comparative Study ...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Nguyen, Nhut Minh Nguyen, Thanh Trung Nguyen, Hua Hiep Tran, Phuong-Nam Dang, Duc Ngoc Minh Fpt University AiTA Lab Dept. of Computing Fundamental Ho Chi Minh City Viet Nam Kyung Hee University Dept. of Computer Science and Engineering Yongin-si17104 Korea Republic of

ISBN: (纸本)9798350364637

In this study, we compared three architectures for the task of age and gender recognition from voice data: Long Short-Term Memory networks (LSTM), Hybrid of Convolutional Neural Networks and Bidirectional Long Short-Term Memory (CNNs-BiLSTM), and the recently released RezoNet architecture. The dataset used in this study was sourced from Mozilla Common Voice in Japanese. Features such as pitch, magnitude, Mel-frequency cepstral coefficients (MFCCs), and filter-bank energies were extracted from the voice data for signal processing, and the three architectures were evaluated. Our evaluation revealed that LSTM was slightly less accurate than RezoNet (83.1%), with the hybrid CNNs-BiLSTM (93.1%) and LSTM achieving the highest accuracy for gender recognition (93.5%). However, hybrid CNNs-BiLSTM architecture outperformed the other models in age recognition, achieving an accuracy of 69.75%, compared to 64.25% and 44.88% for LSTM and RezoNet, respectively. Using Japanese language data and the extracted characteristics, the hybrid CNNs-BiLSTM architecture model demonstrated the highest accuracy in both tests, highlighting its efficacy in voice-based age and gender detection. These results suggest promising avenues for future research and practical applications in this field. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Predictive Maintenance with AI: Applications and Impact

Enhancing Predictive Maintenance with AI: Applications and I...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Chitranjanjit Kaur Anurag Sharma dept. School of Engineering Design and Automation (Computer Science and Engineering) GNA University Phagwara India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Industrial maintenance is crucial for companies due to its significant impact on operational costs and efficiency. Many industrial firms find that a substantial portion of their expenses stems from equipment breakdowns, failures, or suboptimal performance over time. These costs can sometimes account for up to 50% of overall expenditures. Additionally, companies face various losses, including downtime from breakdowns, challenges in inventory and purchase management, and potential risks of injuries. The objective of this writing is to investigate how Predive maintenance is a footstep towards industry 5.0 where automation of predictive maintenance is evolved to autonomy in maintenance. In addition to it various machine learning algorithms are suggested to identify between healthy and poor data of a machine which is first step in Predictive maintenance then Deep learning algorithms to construct XAI (Explainable artificial Intelligence). Predictive maintenance, production scheduling, problem detection, predictive quality, and increased energy efficiency are highlighted in the typical use cases of the selected AI applications. Data from the real environment is transmitted to be virtually recreated. Efficient industrial maintenance helps mitigate these costs by reducing downtime, improving equipment reliability, and lowering the need for emergency repairs. By implementing a well-designed and optimized maintenance strategy, production plants can ensure their equipment operates as reliably as possible, minimizing disruptions and enhancing overall operational efficiency. Machine Learning (ML) methods have been appeared as a crucial tool in Predictive Maintenance (PdM) applications to prevent failures in equipment that make up the production lines. However, the performance of PdM applications depends on the appropriate choice of the ML method. To save expenses, find inefficiencies, duplicate tool tracking systems, and do other tasks, Digital Twin evaluates material utilization

关键词： Adaptation models Costs Machine learning algorithms Electric breakdown Stacking Companies Writing Vectors Maintenance Predictive maintenance

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Sentimental Analysis of Various TV Serials in Society

Sentimental Analysis of Various TV Serials in Society

引用

International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Ripa Sarkar Ratna R. Sarkar Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh Dept. of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

Sentiment analysis which involves identifying emotions in the text is a widely studied topic of natural language processing. Many researchers focus on social media content like posts, tweets and reviews for their studies. In this paper, a large number of data from comments about serials on social media and internet forums in Banglish is collected and analyzed. We use NLP techniques to do the sentiment analysis of the comments. This study explores how different kinds of serials have impacted society and concentrates on how they have influenced the creation of Banglish comments. This study aims to conduct a sentiment analysis of Banglish comments generated in response to different genres of serials and to analyze their potential societal implications. Along with Long Short-Term Memory (LSTM), we use several machine-learning algorithms such as Logistic Regression (LR), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes, Decision Tree (DT), AdaBoost, Random Forest (RF) and Support Vector Machine (SVM) to create this model. The SVM gives the highest accuracy among all of the machine learning algorithms.

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Unified Face Matching and Physical-Digital Spoofing Attack Detection

Unified Face Matching and Physical-Digital Spoofing Attack D...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision Workshops, WACVW 2025

作者： Kunwar, Arun Rattani, Ajita University of North Texas Dept. of Computer Science and Engineering Denton United States

ISBN: (纸本)9798331536626

Face recognition technology has dramatically trans-formed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating Hil:o attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model ro-bustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications. © 2025 IEEE.

关键词： digital attacks face matching physical attacks unified models

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An Automated Tracking System for Movement Analysis of C. elegans

An Automated Tracking System for Movement Analysis of C. ele...

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International Symposium on computer, Consumer and Control (IS3C)

作者： Yu-Hao Lee Jian-Feng Chiu An-Bang Liu Hsin-Ru Liu Wei-Min Liu Dept.of Computer Science and Information Engineering(CSIE) National Chung Cheng University Chiayi Taiwan Dept.of CSIE National Chung Cheng University Chiayi Taiwan Dept.of Medicine School of Medicine Tzu Chi University Hualien Taiwan Dept.of Medical Research Tzu Chi University Hualien Taiwan

C. elegans is one of the classic model organisms in neuroscience and biology, with its simple nervous system and transparent body serving as useful research tools. High-resolution imaging techniques allow for a detailed recording and analysis of C. elegans’ behaviors, such as locomotion, turning, probing, and jumping. These behaviors can be correlated with its neuronal activity and gene expression, thereby helping researchers understand the regulatory mechanisms of its behaviors and physiological processes. In this study, we propose an automated tracking system capable of rapidly and accurately collecting motion trajectory data of multiple C. elegans using skeletal extraction and SORT multi-object tracking algorithms. Furthermore, we introduce the use of Approximate Entropy to quantify the regularity and unpredictability of motion features, offering a novel approach for the analysis of C. elegans’ motion trajectories and movement features. This system also provides a convenient and automated tracking tool for related microorganism research fields.

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Technology and Community Interventions for food Security and Waste Reduction

Technology and Community Interventions for food Security and...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Arunkumar K Muthupandi G S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Food waste and insecurity using a multifaceted approach that includes nutrition-focused interventions in supportive housing, culturally appropriate food programs for Indigenous communities, and mobile applications to cut down on food waste. Geographical barriers, cultural significance, and logistical inefficiency are among the major barriers. This study assesses the impacts of digital food-waste apps, targeted nutritional interventions, and Indigenous food sovereignty initiatives using a mixed-methods approach. The integration of community-based and technology-driven solutions is unprecedented. The results highlight the worth of cooperative, sustainable approaches toward improving community well-being, environmental resilience, and food access.

关键词： Food waste Waste reduction Economics Food security Collaboration Medical services Mobile applications Global communication Cultural differences Resilience

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Blockchain Based E-Voting System Hosted on Cloud 3

Blockchain Based E-Voting System Hosted on Cloud

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Dalvi, Prajwal Prakash Mehare, Jayant Zade, Amol Gaikwad, Amit G H Raisoni University Dept. of Computer Science & Engineering Amravati India

ISBN: (纸本)9798331519582

Blockchain technology is becoming a great innovation at today's date due to its transparency, security and reliability. With the advancement in every aspect of life blockchain with its decentralized and distributed ledger system aims to provide immutability in digital transaction across the globe with the help of block structure joined together that stored the data in the form of hash. The data stored in block is next to immutable as if tried to change in a single block the hash of every block in the chain will change which makes it easy to identify. Due to its security and transparency the study describes its use in the e-voting system revolutionizing voting system with more security and transparency reducing logistic problem, solving storage issues, solves the problem of using extra man power in the large democracy like India. © 2025 IEEE.

关键词： Cloud computing

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Autism Spectrum Disorder Detection Based on Optimal Feature Selection Technique

Autism Spectrum Disorder Detection Based on Optimal Feature ...

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Smart Technologies, Communication and Robotics (STCR)

作者： Lakshaya Kaushik Ranjit Singh Manjit Singh Dept. of Computer Science & Engineering Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

A neurodevelopmental disorder autism spectrum disorder (ASD) still remains a challenge among research fields, and the current diagnostic procedures still need to be improvised for more precise detections. Although several investigations by involving the usage of different machine learning (ML) algorithms toward the identification of ASD among individuals, there is still a large gap in optimizing feature selection methods toward better accuracy and efficiency. Systematic comparisons among various feature selection methods are rather neglected in prior studies, so there is less potential for discovering the best diagnostic markers. In this light, our work brings forth a comprehensive framework that investigates three different kinds of feature selection methods-mutual information, wrapper-based selection by Random Forest, and LASSO regularization by XGBoost. Our methodology implements structured evaluations toward the assessment of the impact of each tool for the identification of detecting accuracy and computation. Also, comparative evaluation results that though wrapper-based selection increased the first model’s accuracy up to 90%, the use of XGBoost with LASSO regularization outperformed it with an accuracy of 99.8%. Results establish the importance of our approach in streamlining clinical assessment processes and identifying features about ASD that are crucial towards advancing data-driven detection methodologies.

关键词： Autism Accuracy Systematics Machine learning algorithms Computational modeling Pipelines Feature extraction Robots Random forests Mutual information

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