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Optimizing mechanical strength and thermal stability of sisal fiber-plastic composites by high-temperature treatment

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Interactions 2025年第1期246卷 1-15页

作者： Mungle, Nischal P. Jadhav, Varsha D. Kulkarni, Sumant Khanderao D.S.Vijayan Saminathan, Rajasekaran Satishkumar, P. Department of Mechanical Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India

In this experiment, high-temperature polyethylene terephthalate (PT) was mixed with epoxy resin (ER) that had been thinned with acetone. Sisal fibers were coated with the resulting product. Composites of Coated treated sisal fibre-reinforced PT (CTSF-PT) were made using a constant fiber loading of 10 wt% and processing temperatures of 230, 250, and 270ºC. The fibers were treated with NaOH. These composites were then characterized for thermal, mechanical and morphological performances. When comparing findings obtained at 250 and 270ºC, the optimal processing temperature was found to be 230ºC. This temperature resulted in the best thermal and mechanical properties, and excellent interfacial adhesion among the coated fibers and the PT. In comparison to the uncoated SF, the coated composites showed a 9.5% reduction in degradation onset temperature and a 13% improvement in decomposition temperature following resin coating, according to thermogravimetric analysis. The CSF-PT composites surpassed the uncoated composites by 2.18% and 2.8% in terms of glass transition (Tg) and melting temperature (Tm) measured by differential scanning calorimetry (DSC), with peak values at 122.2 °C and 265.1 °C, respectively. Coated Sisal-reinforced PT (CSF-PT) and coated-treated PT (CTSF-PT) composites outperformed raw Sisal (SF-PT) composites in terms of overall qualities and morphological properties, regardless of processing temperature. Applying resin to natural fibers enhances their heat resistance and the bond between the coated fibers and the plastic matrix, making them better suited for engineering plastic composites. Applications involving high temperatures, such as construction and automotive industries are well-suited to the resulting composites. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Automotive industry

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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

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Computer Modeling in engineering & sciences 2025年第2期142卷 1233-1288页

作者： Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Center for Artificial Intelligence Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Medicine Universiti Kebangsaan Malaysia Medical CentreCheras56000Kuala LumpurMalaysia School of Engineering and Computing MILA UniversityNilai71800Negeri SembilanMalaysia School of Computer Science University of Nottingham-Malaysia CampusSemenyih43500SelangorMalaysia

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem *** hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search *** selection hyper-heuristics have different imple-mentation ***,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different *** question is how to effectively employ them to produce an efficient search ***,the algorithms that competed in the inaugural CHeSC have not been collectively *** work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is *** analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter *** review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these *** effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic *** are also compared with recent trends in *** work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关键词： Hyper-heuristics search algorithms optimization heuristic selection move acceptance learning diversification parameter control

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Create Coding Challenges and Activities that Foster the Development of Computational Thinking

Create Coding Challenges and Activities that Foster the Deve...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Sakshi Gojaria Vedika Kolambkar Aditi Pandey Pratibha Sajwan Department of Artificial Intelligence and Data Science Thakur College of Engineering and Technology Mumbai India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This compilation introduces a series of coding challenges and activities strategically crafted to nurture computational thinking skills across different proficiency levels. These exercises accommodate both individual and collaborative learning preferences targeting essential elements like problem-solving, algorithmic design, pattern recognition abstraction, and logical reasoning. These coding challenges are for everyone, from beginners to advanced learners, and can be used with different programming languages. They help build skills starting from basic problem-solving to more complex tasks like breaking down advanced problems. These challenges specifically draw inspiration from practical domains like data science, artificial intelligence, and software engineering which encourages learners to explore and understand the practical connections between theoretical concepts and real-world applications which gives a thorough grasp of computational thinking in action.

关键词： Systematics Focusing Information processing data science Encoding Teamwork Pattern recognition Problem-solving artificial intelligence Software engineering

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Deep Neural Networks for Image-Based Indian Sign Language Recognition: A Comprehensive Review and Practical Analysis 1

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2nd International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Kaur, Harleen Bedi, Sanjam Kaur Rao K, Mallikharjuna Lekhana, M.A. Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Addressing the communication barriers faced by individuals with vocal and hearing disabilities, sign language serves as a visual means of expression. However, the limited understanding of sign language among the general population creates significant challenges in various settings, such as banks, airports, and supermarkets. To overcome this, a sign language recognition (SLR) system is essential. This study focuses on developing a real-time word-level sign language recognition system that translates sign language into text, with a specific emphasis on Indian sign language (ISL) to cater to the needs of the deaf and hard-of-hearing community in India. Our research presents an ISL-based sign language recognition system, enabling users to capture hand movement images through a web camera, with the system accurately predicting and displaying the corresponding sign name. The captured images undergo several processing stages, incorporating computer vision techniques like grayscale conversion, dilatation, and masking. To achieve robust recognition, a convolutional neural network (CNN) is trained on the collected dataset, demonstrating exceptional performance with a 99% accuracy rate. This research contributes to the advancement of assistive technologies by providing an effective solution for real-time ISL recognition, promoting inclusive communication and accessibility for individuals with hearing and vocal impairments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fusion for Enhanced Performance 9

Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fus...

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9th International Conference on Information science, Computer technology and Transportation, ISCTT 2024

作者： Liu, Yi-Zhi Lo, Tsung-Ming Zhang, Wan-Ying Zhang, Hong-Tao Lin, Zi-Jing Liu, Xin-Yan Chen, Yu Nanfang College-Guangzhou School of Electrical and Computer Engineering Guangzhou China Dongguan University of Technology Guangdong-Taiwan College of Industrial Science & Technology Dongguan China Guangzhou Xinhua University Department of Artificial Intelligence and Data Science Dongguan China

ISBN: (纸本)9798350388435

In recent years, the monkeypox virus has become a global health concern. Monkeypox is highly transmissible and usually results in severe skin damage and systemic complications. Therefore, accurate and rapid detection of monkeypox skin symptoms is crucial for timely diagnosis and treatment, as well as for informing outbreak control strategies and vaccine development efforts. In this paper, we propose an efficient method for monkeypox image recognition using deep learning techniques that integrate ResNet and MobileNet models. By integrating the residual learning architecture of ResNet and the deep detached convolutional design of MobileNet, our method achieves an ultrahigh classification accuracy of 99.3%, along with a mean absolute error (MAE) of 0.0119 and a root mean square error (RMSE) of 0.1558. ©2024 IEEE.

关键词： Adversarial machine learning

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Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

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Computers, Materials & Continua 2024年第6期79卷 4741-4762页

作者： Zhenyu Qian Yizhang Jiang Zhou Hong Lijun Huang Fengda Li Khin Wee Lai Kaijian Xia School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China Department of Medical Imaging The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Department of Neurosurgery The Changshu Affiliated Hospital of Soochow UniversityChangshu215500China Department of Scientific Research The Changshu Affiliated Hospital of Soochow UniversitySuzhou215500China Changshu Key Laboratory of Medical Artificial Intelligence and Big Data Suzhou215500China Department of Biomedical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical *** deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical *** MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature ***,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering *** address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss *** experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC *** results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these ***,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

关键词： Deep subspace clustering multiscale network structure automatic hyperparameter tuning semi-supervised medical image clustering

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Classification of Brain Tumours in Medical Images Through the Application of Machine Learning

Classification of Brain Tumours in Medical Images Through th...

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data, Computation and Communication (ICDCC), International Conference on

作者： Heena Kuntal Ebtasam Ahmad Siddiqui Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Jaipur India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Early detection of brain tumors is crucial, yet existing segmentation methods have not yielded satisfactory results. Brain tumors are deadlier than many other cancers, primarily because their complex structures often merge with normal tissues, complicating classification. This study explores machine learning techniques to classify brain tumors—specifically Glioma, Meningioma, and Pituitary tumors—using an MRI scan dataset. We employ convolutional neural networks (CNNs) and ensemble approaches, integrating advanced pre-processing, feature extraction, and model optimization for improved accuracy. Our models achieve state-of-the-art precision, recall, and F1 scores, outperforming traditional methods. Future work will expand the dataset and incorporate multimodal imaging and federated learning to improve generalization and privacy.

关键词： Support vector machines Training Accuracy Magnetic resonance imaging Brain tumors Nearest neighbor methods Brain modeling Convolutional neural networks Reliability Medical diagnostic imaging

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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Computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Implementing Catboost Algorithm for Allergen Cross Contamination Detection in Food Industry 1

Implementing Catboost Algorithm for Allergen Cross Contamina...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sudharson, D. Kumar, D. Satheesh Dubey, Aman Kumar Kumar, B. Arun Vaishali, V. Balavedhaa, S. Kumaraguru College of Technology Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Hindusthan College of Engineering &technology Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Master of Bussiness Administration Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Cse Tamil Nadu Coimbatore India Kumaraguru College of Engineering Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359756

In the food industry, allergen cross-contamination is a serious problem that necessitates innovative approaches to detection and prevention. In order to tackle this problem, this paper investigates the possibilities of machine learning (ML) techniques, focusing on CatBoost, XGBoost, and LightGBM in particular. Through an analysis of datasets from food processing establishments, the research develops prediction models that leverage on the distinct advantages of these machine learning methods. The models' comparative evaluations demonstrate how well they identify allergen cross-contamination risks, improving food safety protocols. This paper demonstrates the significant role of CatBoost, XGBoost, and LightGBM in strategically controlling allergen cross-contamination and offers insightful information about the applicability of ML. In the final analysis, these results provide a framework for the advancement of risk-mitigating intelligent systems that ensure consumer safety and support the industry's broader food safety regulations. © 2023 IEEE.

关键词： Food safety

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