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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Active twistoptics: on-chip multidimensional dynamic control for data hypercube reconstruction

Active twistoptics: on-chip multidimensional dynamic control...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Tang, Haoning Lou, Beicheng Du, Fan Gao, Guangqi Zhang, Mingjie Ni, Xueqi Yacoby, Amir Cao, Yuan Fan, Shanhui Mazur, Eric School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States National Institute for Materials Science Namiki 1-1 Ibaraki Tsukuba305-0044 Japan Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States

Our MEMS-integrated twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, achieving high-accuracy, multidimensional light manipulation with significant potential in recon... 详细信息

ISBN: (纸本)9781957171395

关键词： Nanophotonics

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Integrating Advanced Healthcare AI into Higher Education of Smart Cities: Skin Cancer Classification with Custom Vision Transformers 2nd

Integrating Advanced Healthcare AI into Higher Education of ...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Usman, Syed Muhammad Shah, Syed Nehal Hassan Dicheva, Nevena Ur Rehman, Ikram Zaib, Samia Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad44000 Pakistan Faculty of Computing and Artificial Intelligence Air University Islamabad44000 Pakistan School of Computing and Engineering University of West London London United Kingdom

ISBN: (纸本)9789819783441

In today’s smart cities, it’s essential to combine advanced healthcare with education. Our research introduces a groundbreaking method for detecting skin cancer, using a new type of artificial intelligence called a Vision Transformer. This model analyzes a wide range of skin images from different people and accurately identifies skin cancer, especially melanoma and benign *** beyond traditional techniques like convolutional neural networks, our approach uses advanced attentionmechanisms to improve accuracy by focusing on key areas in the skin images. The model has been thoroughly tested with specific criteria to ensure it can reliably detect skin cancer while reducing errors. This research is particularly important as skin cancer rates are increasing worldwide, and early, accurate detection is vital for saving lives. Our study focuses on providing assistance to healthcare practitioners for skin disease diagnostics as well as to assist in providing training and education to healthcare researchers. The proposed method outperforms in terms of ROC curve analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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Dynamic Multi-Workflow Scheduling: A Comparative Analysis of Real-time Data

Dynamic Multi-Workflow Scheduling: A Comparative Analysis of...

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Congress on Evolutionary Computation

作者： Sugandha Rathi Deepti Mehrotra Renuka Nagpal Gautam Srivastava Department of Computer Science and Engineering Amity University Noida India Department of Information Technology Amity University Noida India Department of Math and Computer Science Brandon University Canada Department of Computer Science and Math Lebanese American University Beirut Lebanon

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Fog computing stands as a recent and significant technological advancement in the realm of computing technologies, addressing several limitations inherent in cloud computing and yielding diverse optimizations. As the demand for the payper-use model rises, dynamic workflow scheduling has emerged as a prominent trend for handling incoming workflow tasks. This paper shows a comparative analysis of three optimization algorithms - Genetic Algorithm(GA), Particle Swarm Optimization(PSO), and Grey Wolf Optimization(GWO) - with a focus on two crucial parameters: total cost and resource utilization. The study employs dynamic datasets to ensure the practicality and applicability of the results across various problem domains, ultimately contributing to their optimization. The findings of the comparative analysis are thoroughly examined and discussed in the results section.

关键词： Costs Processor scheduling Heuristic algorithms Metaheuristics Dynamic scheduling Market research Real-time systems

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Machine Learning-Based Approach for Fake News Detection

Journal of ICT Standardization

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Journal of ICT Standardization 2022年第4期10卷 509-530页

作者： Gururaj, H.L. Lakshmi, H. Soundarya, B.C. Flammini, Francesco Janhavi, V. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy Of Higher Education Manipal India Department of Information Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Artificial Intelligence and Machine Learning Alva's Institute of Engineering and Technology Mangalore India IDSIA USI-SUPSI Department of Innovative Technologies University of Applied Sciences and Arts of Southern Switzerland Switzerland Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news articles present in the dataset. © 2022 River Publishers.

关键词： Classification (of information)

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Mobile Application for Personalized Food Recommendation 7

Mobile Application for Personalized Food Recommendation

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Jalali, Aryan Arjun Manoj, Kumar P. Amulya, N. Siddiqua, Ayesha Singh, Alok Kumar Hari Krishna, S.M. M. S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665421683

Personalization in business products has always proven to be a powerful marketing approach by which businesses collect data from users, analyze data and use Machine Learning (ML) algorithms to deliver relevant personalized content to the user. ML has shown to be a ground breaking technology that can be used in the everyday decision-making processes of our lives. A personalized food recommender caters to the exact need and taste of the user taking in account factors like the weather, temperature, type of diet and time of the day. This paper illustrates the design of the application that takes inputs from the user, analyzes the data and feeds it to the machine learning algorithm. The algorithm works on the concept of content-based filtering and a collaborative filtering model. An item feature similarity and item-user similarity matrices are created. Based on these values most similar food items are recommended on the application. © 2022 IEEE.

关键词： Collaborative filtering

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Real-Time Safety Monitoring for Construction Sites Using RFID and Visual Recognition Technologies

Real-Time Safety Monitoring for Construction Sites Using RFI...

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： Noeal Rajeev Thaleeparambil Abiya Biju Deva Krishna Boppuru Rudra Prathap V Rekha Vandana Reddy Department of Computer Science and Engineering Christ (Deemed to be University) Bengaluru India Department of Computer Science and Engineering M. S. Ramaiah University of Applied Sciences Bengaluru India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system combines sophisticated components such as RFID tags and Raspberry Pi, solenoid locks, servo motors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition components improve the system's functionalities, enabling wireless connectivity instantaneous data transmission, and verification of appropriate safety gear application. Solenoid locks and servo motors ensure regulated access to hazardous areas, responding to authenticated safety compliance records. PIR sensors sense motion, differentiating between authentic presence and mere nearness. The methodology outlines the necessary hardware and software criteria, procedures for system initialization, evaluation phases, server connectivity setup, access control enactment, and session closure protocols. It details the seamless integration and verification of hardware components, backend connectivity, identity and safety adherence verification, data encoding, and session termination processes. This research aims to upgrade safety surveillance in construction environments, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous improvement in workplace safety protocols.

关键词： Visualization Protocols Accuracy Regulation Safety Sensors Solenoids Servomotors Monitoring Wireless fidelity

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Improving Performance and Explainability of Sensor-Based Human Activity Recognition 9

Improving Performance and Explainability of Sensor-Based Hum...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Significant progress has been made in developing systems that can automatically recognize human activities, largely thanks to deep learning models and sensor data. However, high performance and comprehensibility are often at odds in these systems. The aim of this research is to enhance both the accuracy and understandability of models that use sensors for human activity recognition (HAR). We present a new convolutional neural network design which takes input from accelerometers and gyroscopes across multiple data streams. This allows efficient capture of time and frequency characteristics. Additionally, we put forward an attention module that identifies informative patterns in the sensor signals to shed light on the model's predictions. Experiments were conducted on a benchmark dataset of common daily movements gathered from ubiquitous sensors. Our model achieves state-of-the-art accuracy exceeding prior deep learning techniques. The attention visuals provide transparency into which sensor segments are most relevant for accurate activity classification. This validates improved model interpretability. Our multi-channel architecture pushes sensor-based HAR forward through heightened predictive performance. We offer original perspectives to create reliable, precise systems for HAR. © 2024 IEEE.

关键词： Wearable sensors

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