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Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

computer systems Science & engineering 2023年第9期46卷 3303-3319页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘corpus’refers to a huge volume of structured datasets containing machine-readable *** texts are generated in a natural communicative *** explosion of social media permitted individuals to spread data with minimal examination and filters *** to this,the old problem of fake news has *** has become an important concern due to its negative impact on the *** manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)*** perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite ***,a huge effort is required from the human side to generate the labelled training *** recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news *** unlock social media data,an automatic text classifier is highly helpful in the domain of *** current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)*** presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual *** attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding ***,the QCLSTM model is utilized for *** boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the *** proposed QCLSTM-FNC approach was experimentally validated against a benchmark *** QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.

关键词： English corpus fake news detection social media natural language processing artificial intelligence deep learning

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Internet of things for aquaculture in smart crab farming

Internet of things for aquaculture in smart crab farming

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2020 International Joint Conference on Civil and Marine engineering, JCCME 2020

作者： Pitakphongmetha, Jumras Suntiamorntut, W. Charoenpanyasak, S. Department of Computer Engineering Faculty of Engineering Digital Research and Innovation Institute Prince of Songkla University Thailand

Internet of Things (IoT) has been introduced and applied in many applications and become an emerging technology in digital era. The major economic in southern part of Thailand is aquaculture such as fishery, shrimp, pearl, mud crab and so on. The concept of IoT which is about connecting the sensors and gathering all necessary data to cloud platform allowing us to compute, control measure, and manage farm efficiently. Soft shell crab is most important ingredient in Chinese, Japanese, and co-fusion menu requiring in many premium restaurants. Unfortunately, the productivity of soft-shell crab industry is very small due to a low survival rate in raising process and lacking of small crab. In tradition al soft-shell crab farm, each small crab has been grown separately of a small box in the old shrimp pond. Farmer has to feed them individually and monitor them every 4 hours in order to avoid soft-shell crab turns to hard shell. In this work, thus, we propose to apply IoT and intelligent system to improve the productivity in the traditional soft-shell crab farm. Water quality sensors, motion sensors, and feeding system are designed and developed to enhance the survival capability of soft-shell crab farm. Meanwhile, with the same concept, our system can be applied in another aquaculture to be smarter farm and be precision aquaculture in the southern path of Thailand. © Published under licence by IOP Publishing Ltd.

关键词： Productivity

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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of AI in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of AI-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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Wireless control of a D.C. Motor Used for Driving a Conveyor Belt

Wireless control of a D.C. Motor Used for Driving a Conveyor...

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2024 International Conference on Circuit, systems and Communication, ICCSC 2024

作者： Livint, Petru Banu, Ioan-Viorel Culea, George Manale, Bouderbala Majout, Btissam Dusa, Alexandru University 'Vasile Alecsandri' of Bacau Department of Energetics and Science Computer Romania Sidi Mohammed Ben Abdellah University Fez Limas Laboratory Faculty of Sciences Dhar El Mahraz Morocco Sidi Mohammed Ben Abdellah University Fez Laboratory of Engineering Modellingand Systems Analysis Morocco 'Dunarea de Jos' University of Galati Department of Automatic and Electrical Engineering Romania

ISBN: (纸本)9798350365306

This article presents an experimental stand for the remote control of a D.C. motor used to drive a conveyor belt. This is based on two development boards Arduino Uno and two shields NRF24L01. The D. C. motor is supplied from a D.C. voltage source by means of the H type bridge. The emitter circuit sends the control signal generated through a potentiometer to the receptor circuit by means of radio waves at a frequency of 2.4 GHz. The receptor circuit receives the analogue signal emitted by the emitting circuit, transforms it into a PWM (pulse width modulation) signal and uses it for controlling the transistors of the H-bridge. For implementing and validating the remote control system of the D.C. motor two programs have been issued in the programming environment Arduino IDE for the two circuits. emitter and receptor. The remote control system of the D.C. motor has been running in the same way as the direct control of this. The remote control is used in the case of the electric actuation systems made with D.C. motors continuous used in spaces inaccessible to the human operator. © 2024 IEEE.

关键词： Belts

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed system Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed system Security Symposium, NDSS 2023. All Rights Reserved.

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An Enhanced Medical Diagnosis system for Malaria and Typhoid Fever Using Genetic Neuro-Fuzzy system

An Enhanced Medical Diagnosis System for Malaria and Typhoid...

引用

International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Salako, Dotun Patrick Farhaoui, Yousef Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia Morocco

ISBN: (纸本)9783031262531

The major challenge of inadequate healthcare services in developing countries has been linked to the unavailability of qualified medical personnel and inefficient diagnostic techniques adopted. The advent of technology in medicine has aided the improvement of medical processes including diagnosis. Many computerized systems have been implemented over the years for medical diagnosis some of which included machine learning techniques. But the existing methods lack the power of selecting the most relevant signs and symptoms of malaria and typhoid fever. Therefore, this study develops a system capable of Malaria and Typhoid Fever diagnosis using a Genetic Algorithm, a Neuro-Fuzzy Inference system called GENFIS. In order to train the NFIS, the GA module determines the optimal set of network parameters, saves them, and then distributes them to the appropriate hidden layer nodes. The study made use of the MATLAB environment to test and evaluate the system. The accuracy performance of the proposed model revealed an accuracy of 97.2%, thus performing better than some of the existing systems. If completely adopted, the proposed approach has the potential to lessen the major issues with NFIS systems. Furthermore, it might be used to address difficult issues in different fields. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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A Machine Learning-Based Model for Energy Efficiency Classification of an Unmanned Aerial Vehicle 1

引用

International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Awotunde, Joseph Bamidele Arowolo, Micheal Olaolu Imoize, Agbotiname Lucky Farhaoui, Yousef Adeniyi, Abidemi Emmanuel Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara Ilorin240003 Nigeria Department of Computer Sciences Landmark University Omu-Aran Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia Morocco Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria

ISBN: (数字)9783031262548

ISBN: (纸本)9783031262531

One of the most promising technologies for next-generation wireless networks is unmanned aerial vehicles (UAVs). They make excellent prospects for a range of applications due to their accessibility and capacity for establishing line-of-sight interactions with users. Because of the enormous amount of data available, artificial intelligence is rapidly developing and succeeding tremendously. As a result, a sizeable segment of the academic community has started integrating intelligence into the core of UAV networks by using machine learning (ML) approaches to address a range of drone-related problems. ML-based framework is designed to provide solutions to the different issues that have been time invariant when UAVs are utilized for communication. UAV-based delivery systems, real-time multimedia streaming, and intelligent transportation systems are all examples of this. The problems with wifi and security are revealed by this study. ML-based solution techniques are created to address these problems, enabling real-time secure operation of UAVs while leveraging wireless system resources in a flexible manner. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network

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Multimedia Tools and Applications 2024年 1-19页

作者： Erten, Mehmet Barua, Prabal Datta Dogan, Sengul Tuncer, Turker Tan, Ru-San Acharya, U.R. Laboratory of Medical Biochemistry Fethi Sekin City Hospital Elazığ Turkey University of Southern Queensland Brisbane Australia Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Department of Cardiology National Heart Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

Examining peripheral blood smears is valuable in clinical settings, yet manual identification of blood cells proves time-consuming. To address this, an automated blood cell image classification system is crucial. Our objective is to develop a precise automated model for detecting various blood cell types, leveraging a novel deep learning architecture. We harnessed a publicly available dataset of 17,092 blood cell images categorized into eight classes. Our innovation lies in ConcatNeXt, a new convolutional neural network. In the spirit of Geoffrey Hinton's approach, we adapted ConvNeXt by substituting the Gaussian error linear unit with a rectified linear unit and layer normalization with batch normalization. We introduced depth concatenation blocks to fuse information effectively and incorporated a patchify layer. Integrating ConcatNeXt with nested patch-based deep feature engineering, featuring downstream iterative neighborhood component analysis and support vector machine-based functions, establishes a comprehensive approach. ConcatNeXt achieved notable validation and test accuracies of 97.43% and 97.77%, respectively. The ConcatNeXt-based feature engineering model further elevated accuracy to 98.73%. Gradient-weighted class activation maps were employed to provide interpretability, offering valuable insights into model decision-making. Our proposed ConcatNeXt and nested patch-based deep feature engineering models excel in blood cell image classification, showcasing remarkable classification performances. These innovations mark significant strides in computer vision-based blood cell analysis. © The Author(s) 2024.

关键词： Image classification

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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A Double-Side Self-Tuning IPT Converter Immune to Parameters Variation Under Misalignment Condition 50

A Double-Side Self-Tuning IPT Converter Immune to Parameters...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zou, Bowei Huang, Zhicong Iam, Io-Wa Chi-Seng, Lam Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781665464543

For inductive power transfer (IPT) systems, the loosely coupled transformer (LCT) is a crucial component. Variations in the air gap can lead to fluctuations in the parameters of the LCT, such as self-inductance and mutual inductance, caused by positional deviations of the ferrite cores on both sides. However, such variable LCT parameters can damage the resonant tank, thus impacting transmission efficiency and output stability. To address this issue, this paper proposes a self-tuning S/S IPT system based on switch-controlled capacitors (SCCs). The system utilizes gradient descent methods independently on double-side to achieve decoupled tuning control. This approach effectively mitigates the impact of LCT parameter fluctuations without requiring wireless communication. Furthermore, all switches in the system operate at fixed frequencies and implement soft switching. Finally, a prototype is constructed to validate the effectiveness of the proposed system. © 2024 IEEE.

关键词： Energy transfer

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