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3rd International Conference for Emerging Technology, INCET 2022

作者： Joshi, Yashodhan Vyas, Yatharth Ghone, Tejas Soni, Vatsal d'Mello, lynette D.J. Sanghvi College of Engineering Computer Engineering Maharashtra Mumbai India

ISBN: (纸本)9781665494991

The existing Intel's 8086 microprocessor emulators are hampered by significant short falling that includes : The existing emulator software does not support operating systems such linux and mac OS. Students could not use the emulator on devices other than desktops since the emulator required was bundled as an installer file for Windows. The emulator required several disparate windows to read the execution of programs along with values of registers, flags, and memory, thus hampering the overall user experience. The 8086 Emulator with Rust and WASM improves upon these issues by providing a web platform with a simple and adjustable interface compatible across various screen sizes and operating systems and a command-line platform for the accustomed programmers and SSH command line users. In a world where many devices support web browsers, a web version makes it very convenient for the global user base, especially when it performs without sacrificing speed or correctness;allowing students to test and run their programs on any device, at any place, as long as they have a web browser. © 2022 IEEE.

关键词： Web browsers

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Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing

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SN computer Science 2024年第4期5卷 363页

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Amar, Oumkeltoum Chekroun, Mohamed Hasidi, Oussama lachihab, Oussama Computer Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakech Morocco SEIA Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco R & amp D and Engineering Center Reminex Managem Group Marrakech Morocco Computer and Systems Engineering Laboratory (L2IS) Cadi Ayyad University Marrakech Morocco

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional long Short-Term Memory (ConvlSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvlSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte ltd 2024.

关键词： data augmentation Industrial data analysis Machine learning Mineral processing

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An Innovative Method for Identifying Web-Based Attacks in Intrusion detection Systems: The RNN-lSTM Model 3

An Innovative Method for Identifying Web-Based Attacks in In...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Yamini, Kalva Bharti, Rakesh Shetty, Monali Nitesh Jadhav, digambar Kothari, Arati Srija, N. Chennai India Lovely Professional University Department of Physical Education Punjab Phagwara India Fr.Conceicao Rodrigues College of Engineering Department of Computer Engineering Mumbai India Dr. D. Y. Patil Institute of Technology Pimpri Department of Computer Engineering Pune India Ajeenkya D y Patil University School of Engineering Pune India M. Kumarasamy College of Engineering Department of Information Technology Karur India

ISBN: (纸本)9798350354171

Information security systems are more important than ever before due to the fast evolution of information technology. losses are inevitable as a result of new security holes appearing in private and commercial systems as a result of technological progress. data security in digital archives, where material is always accessible, has lately been a big concern. Intrusion detection and prevention systems are currently used as security measures within this framework. Online application security is the focus of this approach, which proposes a hybrid real-time intrusion prevention solution. Following this specific sequence will ensure proper model training, feature extraction, and preprocessing. For the preprocessing phase, the proposed approach used prior HFS method to combine the best features of the filter and wrapper techniques. Further, the issue with filter-ranking that involved redundant feature extraction was addressed by implementing the suggested filter-based subset evaluation (FBSE). In order to train the model, the proposed approach utilized an RNN-lSTM technique. When compared to two state-of-the-art methods, RNN and lSTM, the proposed methodology has the upper hand. After using the method, accuracy increased by 96.04%. © 2024 IEEE.

关键词： Network intrusion

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Neural Network-based Prognostic Model for Cerebrovascular Accident using CT Scans 2

Neural Network-based Prognostic Model for Cerebrovascular Ac...

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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCdS 2023

作者： Fiona, Mary Rajeswari, M. Amalasweena, M. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India R.M.D. Engineering College Department of Electronics and Communications Engineering Kavaraipettai India

ISBN: (纸本)9781665491990

Cerebrovascular accident (CVA), stroke has a 5.5 million yearly mortality rate, making it the second most common cause of death in the world. So, it is inevitable to find a method that can efficiently predict the stroke with high accuracy and considerably in less time. Medical imaging, such as brain MRI or CT scans, helps to provide considerable information on stroke prediction, but a manual prediction can be time-consuming and considerably imprecise. Stroke prediction with empirical attributes can be beneficial but a CT scan provides detailed and accurate images of the brain and helps in the prediction of the stroke in the exact location occurrence. This study employs predicting the stroke by training the CT image dataset using the four efficient algorithms, CNN-EfficientNetB0, Random Forest, Support Vector Machines (SVM), and decision Trees. With the help of performance measures, the models' performance is assessed. Our results showed that the early prediction of stroke with the CNN's EfficientNet-B0 architecture provides high accuracy, sensitivity, and specificity. Hence the proposed method aids in providing an efficient approach for the early prediction of stroke with less time-consuming and comparatively more precise classification. © 2023 IEEE.

关键词： Support vector machines

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Exploring Advancements in 6G Wireless Networks for the Future of High-Speed Connectivity 2

Exploring Advancements in 6G Wireless Networks for the Futur...

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

作者： Suresh, J. Kumari, V Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu david, d. Beulah Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology ThandalamTamil Nadu Chennai India

ISBN: (纸本)9798350364699

High-speed connectivity technologies that aim to be faster and more secure have been explored so that there will be a change of the very nature of wireless communication. This article presents advances concerning the high-speed connection component of the sixth-generation wireless networks, which is simply denoted as 6G. different performance parameters of 6G are compared with 4G (lTE) and 5G (5G) through multiplicity of research analyses done across disciplines. Our findings suggested that 6G networks are incredibly dynamic and will change how the internet is used. These SixG networks are due to accompany massive data transmission rates of 100 Gbps, five times higher than 5G's 20 Gbps. latency also approaches same level as 6G where on average 1 millisecond is noticed compared to 5G's 10 and 4G's 50. Server speed and its response time in milliseconds will expose room for a number of real-time applications from only augmented reality imaging to the world of driverless automobiles and interactive games. Our study outlines the major consequences of the coming health shift. 4G allowed only for slow/limited improvements in healthcare whereas the 5G made telemedicine possible. On the other hand, 6G reportedly offers "precision remote surgery", allowing surgeons to be located remotely of a patient while still performing a high precision surgery. Attitude of people becomes completely unaware when they are briefed about concepts of latest generation known as 6G. Before studying about 6G, the number of those are eagerly delighted are 20%, having no idea on how it will be is 50%, while the other 30% are skeptics. Meanwhile, once people are better cognizant of 6G, fanatics raise up from 60 percent and sceptics drop till 10 percent. The significant shift is a reflection of 6G technology in the stakeholders' mind. © 2024 IEEE.

关键词： 5G mobile communication systems

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Integrated Ensemble Framework for Real-Time Building Stability Monitoring using Accelerometers and Strain Gauges 8

Integrated Ensemble Framework for Real-Time Building Stabili...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nithila, Ezhil E. Sundararajan, S. Haribabu, K. Krishnan, R. Santhana Raj, J. Relin Francis Sankar, G. Ram Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350376425

Structural health monitoring is critical for ensuring the safety and integrity of buildings, especially in seismic zones or environments subject to high loads. This paper presents the "StrucSense Ensembler,"an advanced ensemble learning model designed to monitor building stability by integrating time-series data from BMA280 accelerometers and spatial data from strain gauges. The StrucSense Ensembler combines the strengths of long Short-Term Memory (lSTM) networks for analyzing temporal accelerometer data and Convolutional Neural Networks (CNNs) for interpreting spatial strain data. By leveraging this hybrid approach, the Model delivers accurate predictions of structural stability, enabling proactive decision-making. The ensemble is evaluated using various metrics, including accuracy, precision, recall, and F1 score, and is demonstrated to outperform traditional models like Random Forest, Gradient Boosting Machines, and AdaBoost. The proposed StrucSense Ensembler effectively identifies potential risks such as deformation, cracks, and load imbalances, offering actionable insights for timely intervention and maintenance. This innovative approach provides a robust framework for real-time structural monitoring, significantly enhancing the safety and reliability of buildings. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT botnet detection from software defined network using American zebra optimization algorithm with SSRNN-ElM

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International Journal of Information Technology (Singapore) 2025年第2期17卷 959-967页

作者： Bindu, Nalluri Venkata Madhu Nassa, Vinay Kumar Vasuki, P. Manikandan, G. Jeena, R. Mahaveerakannan, R. Department of CSE-AIML Malla Reddy Engineering College (Autonomous) Medchal Secunderabad Malkajgiri (Dist) India Ellenki College of Engineering and Technology (ECET) Patelguda (v) Ameenpur (m) Sangareddy (D) Hyderabad Telangana India Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

Software-defined networking (SdN) offers centralized control over large-scale networks, enhancing flexibility and enabling tailored network applications. However, SdN introduces new security vulnerabilities, including the risk of botnet attacks, which can compromise systems and steal data. This paper proposes a novel approach for detecting botnet attacks in SdN environments by leveraging a hybrid model that integrates recurrent neural networks (RNNs) and extreme learning machines (ElMs). The proposed method utilizes RNNs for feature learning and ElMs for classification, with the American zebra optimization algorithm (AZOA) optimizing ElM weights. This hybrid SSRNN-ElM (supervised subset recurrent neural network—extreme learning machine) approach is evaluated using N-BaIoT dataset and performance metrics, demonstrating effective detection of complex botnet attacks with high accuracy. The results demonstrate that the hybrid model effectively identifies complex botnet attacks, achieving a detection accuracy of 96.60%. The integration of recurrent neural networks (RNNs) for feature learning and extreme learning machines (ElMs) for classification, along with optimization by the American zebra optimization algorithm (AZOA), significantly improves detection performance as compared to the recent existing studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： American zebra optimization algorithm (AZOA) BotNet attack detection Extreme learning machine (ElM) Recurrent neural network (RNN) Software-defined network (SdN)

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AI-Enhanced Farming: A Real-Time Monitoring App powered by IoT 5

AI-Enhanced Farming: A Real-Time Monitoring App powered by I...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Gayathri, R. Raaj, R. Sathesh Balaanand, M. Kumar, J. Arun Sasikala, N. Rajesh, K. Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Tagore Institute of Engineering & Technology Department of Computer Science and Engineering Salem India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Harohalli India Ssm Institute of Science and Technology Department of Electronics and Communication Engineering Dindigul India

ISBN: (纸本)9798331504403

The Constant challenge faced by the farmers is to maintain their crops and enhance productivity. despite various methods and solution evolving, still farmers can't upgrade to it. Farmers are not aware of the modern agricultural methods. The major cause is that the software and solution developed are of higher complexity. To upgrade them, technology is the foremost important tool to stand up in this modern world. To help them with their problem and adapt to the modern world, the development of software with inbuilt AI and user-friendly features (in agriculture) is needed. This study proposes a novel Agri-tech app, which is a IoT based app used by the farmers for weather forecasting, soil moisture and nutrient content analysis. The developed application also recommends the crop type that can be cultivated in the soil by using the database of soil moisture and nutrient content. IoT sensors fixed in the field are used to detect the moisture level in the soil and notify the farmers to irrigate the land. It recommends the farmer to add manures and fertilizers to soil by calculating the amount of nitrogen, phosphorous, and oxygen contents present in the soil. Using these technical innovation farmers can monitor their fields over a system or smart phone. Moreover, the proposed approach can promote crop diversity and rotation for healthier soil, encourage sustainable practices like organic farming. © 2024 IEEE.

关键词： Soil moisture

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A Simplified Policy Modification Framework By External provider For Human Medical Record Sharing

A Simplified Policy Modification Framework By External provi...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Pandi, C. Siva Rama lingham, N. Anish, T.P. Aruna, R. Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi India Computer Science and Engineering R.M.K College of Engineering and Technology India

ISBN: (纸本)9798350305708

The great flexibility and accessibility of cloud computing make it a good fit for storing and managing Personal Health Records (PHR), which are often large and complex datasets. Personal health data, on the other hand, include incredibly sensitive and private information, which raises serious security and privacy concerns. There should also be a flexible and secure access plan for PHR owners' outsourced data. Although Some business cloud platforms offer symmetric or public key encryption as an add-on feature, these conventional encryption techniques are not well-suited for data outsourcing due to the high overhead of key management and maintenance. We create and put into practise a secure, granular access control system with minimal policy modifications for outsourced PHRs. On top of AES and Cypher Text encryption, our suggested method is constructed. In order to provide comprehensive policy change tracing, a policy versioning system is also presented. In the conclusion, we evaluated the effectiveness of the suggested scheme by analysing its performance. © 2023 IEEE.

关键词： access control AES performance evaluation PHR policy update policy versioning proxy re-encryption

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detection and Prevention of Animal in Farmland using IOT-Ml

Detection and Prevention of Animal in Farmland using IOT-ML

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Patil, Supriya R. deshpande, Pradeep A. Noola, daneshwari A. Biradar, Arpita S. Vijapur, Keerti Veerendrababu Dept. of ISE B.L.D.E.A’s P.G. Halakatti Engineering College Vijayapura India

ISBN: (纸本)9798331505134

The IOT animal detection and prevention system or farm management is a promising solution to the challenge of animal intrusion, which can significantly impact crop yield and livestock health. The system uses a network of sensors deployed across the farm premises to monitor animal movement and behavior in real-time. These sensors, equipped with motion detectors, infrared sensors, and cameras, continuously gather data and transmit it to a central processing unit. Advanced algorithms, including machine learning and computer vision techniques, analyze the data to distinguish between harmless wildlife and potential threats. Upon detecting the unauthorized animal presence, the system triggers preventive measures, such as sound alarms, flashing lights, or automated water sprayers. Alerts are sent to farm managers or personnel via mobile applications or other communication channels, enabling swift response and intervention. The IoT platform provides valuable insights into animal behavior patterns and trends, facilitating proactive farm management strategies. By analyzing historical data, farmers can identify high-risk areas prone to animal intrusion and implement targeted preventive measures. Continuous monitoring enables early detection of emerging threats, minimizing potential damage and losses. Implementing an IoT-based system offers numerous benefits, including enhanced farm security, improved resource utilization, and optimized operational efficiency. © 2024 IEEE.

关键词： Motion sensors

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