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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sanjana, D.N. Soumya, V. Sneha, L. Shama, G. Pooja, M.R. Vidyavardhaka College of Engineering Computer Science And Engineering Mysuru India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Current railway infrastructure faces the critical challenge of preventing looping incidents, where trains unintentionally re-enter previously traversed tracks. To address this, we propose a robust and comprehensive IoT framework for real-time looping detection and intervention. The proposed framework integrates a network of strategically positioned sensors, a centralized data processing unit, intelligent actuators, and a comprehensive alert system. Sensors such as weight sensors or acoustic detectors capture precise train data, which is then securely transmitted and analyzed for abnormal patterns indicative of looping. Upon detection, actuators like signal control systems and track switches are triggered to prevent further incidents. Simultaneously, alert systems notify relevant personnel to ensure swift response. By prioritizing redundancy, compliance, and user-friendly interfaces, this framework guarantees long-term reliability and operational efficiency. Through thorough testing and training, we pave the way for the effective implementation of this IoT solution, ultimately enhancing railway safety and preventing catastrophic looping incidents. © 2024 IEEE.

关键词： Redundancy

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S-DrivingRecords: Blockchain Based Enhancing Trust and Transparency in Driving Records Using Hyperledger Fabric

S-DrivingRecords: Blockchain Based Enhancing Trust and Trans...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Chowdhury, Shovon Das Ahmed, Tanjil Amin, Ruhul Chowdhury, Rahat Ahmed Tanjim, Shah M.D. Lysuzzaman, Md Sylhet Engineering College Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350350289

The escalating growth in population has led to a substantial increase in both private and public transportation on roads. Consequently, the surge in accidents, traffic rule violations, and other traffic-related offenses has become a daily concern. Addressing these issues is crucial for ensuring the safety of passengers and enhancing transparency in driver recruitment, especially when considering violations that may have occurred abroad. This paper introduces S- DrivingRecords, focusing on pivotal functionalities such as the registration of cases related to traffic rule violations or accidents, case withdrawal, and the sharing of driver history with external Decentralized Applications (DApps). The system facilitates case registration through a dedicated traffic DApp, enabling drivers to share their records with other entities, be they companies or individuals. This feature allows individuals to conduct thorough checks on a driver's history before making decisions such as hiring or booking. The implementation employs Hyperledger Fabric as a distributed ledger to ensure the security of data, while the use of Hyperledger Cacti facilitates seamless collaboration with different systems. © 2024 IEEE.

关键词： Blockchain

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Learner Phase of Partial Reinforcement Optimizer with Nelder-Mead Simplex for Parameter Extraction of Photovoltaic Models

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Journal of Bionic engineering 2024年第6期21卷 3041-3075页

作者： Jinpeng Huang Zhennao Cai Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065SichuanChina Department of Artificial Intelligence Wenzhou PolytechnicWenzhou325035China

This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed *** LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of ***,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO *** comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been *** accuracy and stability of simulated data and real data in the parameter extraction of PV systems are *** to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent *** further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and *** summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.

关键词： Partial reinforcement optimizer Learner phase Nelder-Mead simplex algorithm Parameter extraction

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FarmScan: Smart Rotten Fruit and Vegetable Detector 5th

FarmScan: Smart Rotten Fruit and Vegetable Detector

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5th International Conference on Artificial Intelligence and Speech Technology, AIST 2023

作者： Trivedi, Shivani Yadav, Ankit Gupta, Suraj Kumar Gupta, Shivam Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9783031751660

Quality evaluation of fruits and vegetables is a vital factor in maintaining qualities of food and ensures consumer satisfaction. Freshness is an essential asset of fruit and vegetable quality that impacts the physical health and motivation of consumers. This paper presents a transformative approach to revolutionizing quality evaluation in fruits and vegetables by applying image processing and machine learning algorithms, specifically utilizing the GrabCut algorithm for background subtraction, The traditional subjective methods of assessing product quality are replaced with objective and data-driven techniques. Through case studies, the efficacy of this approach is demonstrated, showcasing its potential to enhance quality assessment precision and efficiency. This innovative fusion of image processing and machine learning presented a promising avenue to redefine industry standards, ensuring higher consumer satisfaction and reduced wastage through consistent and reliable quality evaluation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： background subtraction grab cut algorithm Image processing image segmentation

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An IoT-Based Energy Conservation Smart Classroom System

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Intelligent Automation & Soft Computing 2023年第3期35卷 3785-3799页

作者： Talal H.Noor El-Sayed Atlam Abdulqader M.Almars Ayman Noor Amer S.Malki College of Computer Science and Engineering Taibah UniversityYanbu46421-7143MedinahKSA Department of Computer Science Faculty of ScienceTanta UniversityTantaEgypt College of Computer Science and Engineering Taibah UniversityMedinahKSA

With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an inﬂuential role in energy conservation such as the Internet of Things(IoT).In this article,we describe the design and implementation of an IoT-based energy conser-vation smart classroom system that contributes to energy conservation in the edu-cation *** proposed system not only allows the user to access and control IoT devices(e.g.,lights,projectors,and air conditions)in real-time,it also has the capability to aggregate the estimated energy consumption of an IoT device,the smart classroom,and the building based on the energy consumption and cost model that we ***,the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company(SEC)***,the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't *** feasibility and beneﬁts of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering,Taibah University,Yanbu *** results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.

关键词： Energy consumption energy conservation energy cost Internet of Things(IoT) smart classroom

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Enhancing Speech Recognition in Noisy Environments Through Multiscale-Multichannel Feature Learning and Adaptive Noise Resilience 3

Enhancing Speech Recognition in Noisy Environments Through M...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sengottaiyan, N. Sathishkumar, S. Rajesh Sharma, R. Sungheetha, Akey Ghantasala, G. S. Pradeep Karthika, K. Alliance University Department of Computer Science and Engineering Bangalore India Builders Engineering College Department of Computer Science and Engineering Kangeyam Tiruppur India Sri Sairam College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518592

Speech Emotion Recognition (SER) has seen much research done recently, but little is being done to minimize the effect of environmental noise on the predictions. Existing SER models primarily aim to learn the best feature representations of speech from clean datasets while neglecting the pragmatic constraint when it is deployed in adverse acoustic settings. The variations in signal-to-noise ratios, background noises and acoustic interferences make existing SER systems less robust. Here, we focus on a new method that combines adaptive feature learning and noise-resilient techniques using a multiscale-multichannel framework. The objective is to help common SER models generalize better in different acoustic conditions, thus improving the accuracy and robustness of SER in noisy real-life settings. Therefore, these SER models are still not efficient against challenging acoustic conditions and the research area lacks addressing it => thus we utilize state-of-the-art IDCNNs and also explore novel data augmentations to help develop SER systems with great capability for arbitrary acoustical channels that fills the gap of very limited related works till now. © 2024 IEEE.

关键词： Background noise

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Performance of pre-trained deep learning models for land use land cover classification using remote sensing imaging datasets

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Environmental Earth sciences 2025年第11期84卷 1-36页

作者： Haider, Irfan Khan, Muhammad Attique Masood, Saleha Algamdi, Shabbab Ali Alasiry, Areej Marzougui, Mehrez Nam, Yunyoung Department of Computer Science HITEC University Taxila Pakistan Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia IRC for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Software Engineering College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of

This study presents a comparative analysis of ten pre-trained convolutional neural network (CNN) models, evaluated across three remote sensing datasets: EuroSat, NWPU, and Earth Hazards (Land Sliding). We investigate the interplay between model architecture and classifier selection by incorporating five different neural network (NN) classifiers, emphasizing their impact on predictive accuracy and computational efficiency. Due to its densely connected architecture, DenseNet201 achieved the highest accuracy—97% on EuroSat, 99.40% on NWPU, and 97.80% on Earth Hazards. In contrast, MobileNetV2, while slightly less accurate, demonstrated superior computational efficiency, recording the shortest prediction times of 39.943 s on EuroSat, 27.482 s on NWPU, and 2.8986 s on Earth Hazards. Additionally, classifier choice significantly influenced performance, with the Wide NN classifier excelling in diverse datasets and the Medium NN classifier optimizing speed. Our findings underscore the importance of balancing accuracy and efficiency in selecting CNN models for remote sensing applications, suggesting future research should explore ensembling techniques and lightweight models to enhance performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Software design

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Environmental sound classification using convolutional recurrent neural network and data augmentation

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Multimedia Tools and Applications 2025年 1-23页

作者： Bansal, Anam Garg, Naresh Kumar Computer Science and Engineering GZS Campus College of Engineering and Technology Maharaja Ranjit Singh Punjab Technical University Punjab Bathinda151001 India

Environmental sound classification(ESC) is the trending research area. ESC categorizes sounds such as dog barking, gunshots, and children playing in the surroundings. Due to overlapping sound signals, the presence of several audio sources while recording audio, and different distances from audio sources to the microphone make this problem complex. This study proposes a robust model for ESC, which can help in crime investigation systems, security warning systems, and the development of smart homes and hearing aids. Researchers have designed numerous frameworks for classifying surrounding events. Various techniques for ESC have been used in the past, but they are either computationally intensive or provide less accuracy. A hybrid model consisting of Convolutional Neural Network and Recurrent Neural Network for ESC is proposed to provide an accuracy of 99.89%, which is the highest till now, as far as we know. The model is a combination of both models;it is called CRNN. CRNN has already been used in a few past studies, but raw waveforms are used, and the accuracy attained is quite low. The publicly available Dataset UrbanSound8 K is used. Augmentation techniques are used to overcome the scarcity of datasets. The cepstral features are extracted and input to the CRNN. CRNN is encouraged due to its ability to capture spatial and temporal dependencies of environmental sound waves. Various hyperparameters, such as the number of LSTM layers, number of filters, batch size, momentum, and number of neurons in the LSTM layer, are altered to find the best value for hyperparameters for ESC. It is found that 0.5 momentum, 128 filters, 512 neurons in the LSTM layer, 256 batch size, and one LSTM layer give the highest accuracy. Another dataset, ESC- 10, is used to validate the model. It is found that the proposed model provides considerable accuracy for ESC- 10, even though it is lower than in the case of UrbanSound8 K. In the future, the model can be applied to different applications

关键词： Recurrent neural networks

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Application of 3D Magnetic Resonance Imaging in the Characterization of Brain Tumors 2

Application of 3D Magnetic Resonance Imaging in the Characte...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Samender Kishore, Lalit Badhoutiya, Arti Sagar, Pramod Kumar Saraswat, Laxmi Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India SRMIST Delhi Ncr Department of Computer Application Modinagar UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Raj Kumar Goel Institute of Technology Srmist Department of Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

MRI has revolutionized the analysis and remedy of disorder by using imparting precise and accurate images of soft tissue. This technology has been similarly superior by means of the improvement of 3-D MRI, which permits improved detection and characterization of tumors and improves accuracy of diagnosis and remedy. 3D MRI has been found to be in particular beneficial within the characterization of brain tumors as it offers a greater detailed assessment of tumor size, shape and composition, in comparison to traditional imaging techniques including X-ray or CT. This information can help in identifying the nature of the tumor that may impact its remedy and analysis. Furthermore, 3D MRI is useful for figuring out the chance of malignant invasion, which can provide important information for affected person control. Additionally, the advanced decision of 3-D MRI permits better delineation of areas of healthy and diseased tissue that can assist clinicians in making plans complex surgeries. (3-D) MRI has emerged as an increasingly more critical tool inside the characterization of brain tumors. The three-D imaging affords distinct anatomical visualization of the tumor and it's courting to surrounding systems, allowing for extra effective remedy making plans. Furthermore, the potential to visualize tumor tissue parameters together with edema, necrosis, and perfusion may be used to decide whether a tumor is malignant or no longer. 3D MRI has additionally enabled the evaluation of tumor vascularization, which serves as a vital prognostic indicator. The statistics received from 3-D imaging can then be used to be expecting the recurrence of a tumor following treatment, supporting to manual decisions on comply with-up remedies. The reduction in test time, decrease value, and better resolution of 3-D MRI in contrast to standard x-ray imaging also makes the technology more and more attractive for brain tumor management. © 2024 IEEE.

关键词： Attractive Characterization Superior Traditional

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An Efficient Investigation of Cloud Computing Security with Machine Learning Algorithm 7

An Efficient Investigation of Cloud Computing Security with ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Senthilkumar, R. Yasotha, S. Manochithra, P.M. Senthil, J. Sivakumar, G. Shree Venkateswara Hi Tech Engineering College Computer Science And Engineering Tamilnadu Erode India Sri Eshwar College of Engineering Computer Science And Engineering Tamilnadu Coimbatore India Sri Krishna College of Engineering And Technology M.Tech Computer Science And Engineering Tamilnadu Coimbatore India Erode Sengunthar Engineering College Computer Science And Engineering Tamilnadu Erode India

ISBN: (纸本)9798350359299

Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network systems susceptible, attackers have increased their determinations. Improving the security of such network systems is very crucial. Using Deep Learning (DL) methods with an Intrusion Detection System (IDS) framework is put into action in this research. The Gated Recurrent Unit (GRU) as well as Support Vector Machine (SVM) are used in this study for attack detection. Therefore, the implementation of SoftMax in the GRU model's last output layer, this study introduces a linear SVM. The NSL-KDD dataset is taken into account to evaluate the proposed IDS framework's performance. In addition, when the number of features increases, current IDSs have poor test accuracy ratings when it comes to identifying new attacks. Each dataset's feature space was reduced using an XGBoost (XGB)-based feature selection approach in this research. After that, 22 detailed characteristics were selected from the NSL-KDD dataset using XGB. The results show that, the proposed IDS paradigm outperformed other approaches. © 2024 IEEE.

关键词： Support vector machines

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