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Aspect Level Songs Rating Based Upon Reviews in English

computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea 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

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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Effective solutions to prevent digital fraud by introducing electronic signature of PDF files 24

Effective solutions to prevent digital fraud by introducing ...

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24th International Conference on Control Systems and computer Science, CSCS 2023

作者： Anghel, Ionut Petrisor Radulescu, Dragos Marinescu, Ion Alexandru Polytechnic University of Bucharest Computer Science and Engineering Department Bucharest Romania National Institute for Research and Development in Informatics Digital Transformation and Governance Bucharest Romania

ISBN: (纸本)9798350313390

With the exponential growth of digital transactions, the creation and verification of electronic signatures contributes decisively to the creation of a safer environment for business development and innovation, based on security factors such as confidentiality, integrity, authenticity or non-repudiation. Implementing electronic signatures in PDF files can improve the prevention of digital fraud by providing an additional level of security and authenticity. For this reason, in addition to choosing a reliable electronic signature provider, it is necessary to establish clear signing procedures in order to minimize the risk of fraud. For this purpose, a software solution intended mainly for the academic environment is presented, which facilitates the electronic signature with legal value of PDF documents using a digital certificate. This application offers the possibility of simultaneously signing of multiples files in a folder, thus streamlining the process of digital signature and avoiding the use of third-party applications. © 2023 IEEE.

关键词： Quantum cryptography

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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Comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism 8th

Comprehensive Review of Capsule Networks with a Case Stud...

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8th International Symposium on Intelligent informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Faba Bean Disease Detection Using Deep Learning Techniques

Faba Bean Disease Detection Using Deep Learning Techniques

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2023 International Conference on Cyber Management and engineering, CyMaEn 2023

作者： Salau, Ayodeji Olalekan Abeje, Bekalu Tadele Faisal, Abeer Naser Asfaw, Tesfa Tegegne Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Haramaya University College of Computing and Informatics Department of Information Technology Ethiopia Institute of Technology Faculty of Computing Bahir Dar University Department of Computer Science Ethiopia University of Sumer College of Computer Science and Information Technology Al-Rifai Iraq

ISBN: (纸本)9781665493291

Faba bean (Vicia faba L.) is an important cash crop for animal and human consumption in many countries, especially Ethiopia due to its high protein content and high rate of production. It also improves soil fertility with the help of bacteria, which forms nodules on its roots. Ethiopia is the second largest faba bean producer;however, according to research, the yield is generally low. Among the constraints contributing to low productivity of the crop, disease is the most important. Even though there are countermeasures such as the use of chemical sprays, which is used to reduce the damage or eliminate the disease, an effective disease identification technique is still required. Chocolate spot, faba bean gal, and rust are the most common diseases of faba beans. To effectively detect the disease, in this paper, we propose an end-to-end CNN model for training before and after the dataset is preprocessed. The CNN end-to-end model is trained with raw images and achieves 92.1% training accuracy. Also, the model is trained with preprocessed data and it achieves a training accuracy of 98.14% for the end-to-end CNN classifier. The results show that the proposed approach is suitable for faba bean disease detection and can be employed to detect diseases of other crops. © 2023 IEEE.

关键词： Convolutional neural networks

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IAQ Monitoring System Optimizing Data-Driven Sensor Placement

IAQ Monitoring System Optimizing Data-Driven Sensor Placemen...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Gabriel Filios Sotiris Nikoletseas Ioannis Stivaros Department of Computer Engineering and Informatics University of Patras Greece Computer Technology Institute and Press "Diophantus" Greece

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Indoor Air Quality (IAQ) significantly impacts people’s health and comfort in buildings. Although IAQ research spans two decades, a comprehensive assessment of factors affecting indoor air pollution remains elusive. Recent efforts focus on real-time monitoring using virtual sensing, a computational technique in engineering and data science. This paper presents a novel IAQ monitoring system emphasizing dynamic sensor placement for enhanced efficiency. The system employs random sensor positions and calculates measurement predictability, allowing identification and removal of less useful sensors, reducing data volume, and saving energy. Multiple reduction strategies are available, depending on the target number of edge devices or the desired maximum prediction error. Importantly, the system operates locally, without relying on internet connectivity. It consists of edge devices using air quality sensors, a gateway for data gathering and algorithm initiation, by training and evaluating multiple different machine learning techniques to determine point combination predictability. Deployed in two indoor settings, one with HVAC and the other naturally ventilated, the system’s effectiveness is assessed, shortcomings identified, and conclusions drawn for future work.

关键词： Training Sensor placement Machine learning algorithms Volume measurement Soft sensors Position measurement Prediction algorithms

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Secure Blockchain Integration Approach for Knowledge Discovery in Industrial Internet of Things

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4772-4787页

作者： Rohan Tripathi Ritam Rao Vivekananda Bhat K. Abhishek Kumar Pandey Ashok Kumar Das Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea

The field of Industrial Internet of Things (IIoT) is now being duly recognized as a revolutionary area in industrial automation. With the budding interest of knowledge discovery that helps the businesses in applying technology on a large scale, researchers are now faced with several major security concerns. Blockchain integration is one of the suggested applications of technology that could help secure the data during transmission, storage, and knowledge discovery. Moreover, by integrating smart contracts, a secure architecture could also assimilate accountability during data exchange. Thus, we propose a four-layer security architecture that isolates IIoT devices from user-oriented layers and maintains a record of all IIoT devices registered in the organization to prevent malicious devices from corrupting the database and discovery process. Further, by choosing proof of authority (PoA), we ensure the fair functioning of the Blockchain nodes. Through proxy re-encryption for clients, PoA consensus for Blockchain nodes, and Whitelist-based access control for IIoT Devices, we ensure the legitimacy of all participating nodes. We also implement a prototype using a private Ethereum network with proof of authority consensus and present the time taken for the entire exchange and the gas (ethereum currency) consumed per exchange by the contracts. Also we implemented the secure knowledge discovery to understand the significance of the developed scheme. The results show that the exchange can be implemented in an IIoT environment and work with a reasonable amount of resource consumption.

关键词： Blockchains Industrial Internet of Things computer architecture Security Knowledge discovery computer science Cloud computing Prototypes Authentication Analytical models

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