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Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

Multimedia Tools and Applications 2024年 1-26页

作者： Singh, Ajay Dhanaraj, Rajesh Kumar Kumar, Santosh Abraham, Ajith School of Computer Science and Engineering Galgotias University Greater Noida India Pune India Department School of Computer Science Engineering & amp Technology Bennett University Uttar Pradesh Greater Noida India

Providing accurate and timely traffic information such as arriving time of train plays a significant part in intelligent train status prediction. Maximum-speed train status forecast is a significant topic as far as railway dispatching. In this paper proposes a novel train status prediction method based on the Hannan Quinn Quantum Grasshopper Optimized Attention Deep Learning (HQQGO-ADL) method introduced for accurate train status prediction. The proposed HQQGO-ADL is designed with three different processes namely preprocessing, feature selection, and prediction. This method can well capture the influence of discrete and continuous features in addition to spatiotemporal characteristics, therefore modeling train status prediction. At first, Mid-hinge Inter-Quartile Interpolation is utilized to perform preprocessing for eradicating or discarding the outliers with less train status identification time. Second, HannanQuinn Quantum Grasshopper Optimization is employed to capture the influence of discrete and continuous features on spatiotemporal characteristics, therefore modeling train status prediction. Besides, the maximum log likelihood discrete feature fine tuned to obtain continuous features. Finally, the Attention Deep Neural Train Status Prediction model is utilized for improved latency train status prediction. The overall analysis of HQQGO-ADL has better performance of higher train status prediction accuracy by 32% and lower train status prediction time, overhead, and error rate by 32%, 32%, and 27%as compared to existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home

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Journal of Reliable Intelligent Environments 2024年第2期10卷 123-136页

作者： Raeiszadeh, Mahsa Tahayori, Hooman Bahadori-jahromi, Ali Department of Computer Science and Engineering and IT Shiraz University Shiraz Iran School of Computing and Engineering University of West London London United Kingdom

Indoor electrical systems are aimed to provide comfort to the occupants. However, their operation is contingent on the presence or needs of the residents. Hence, to optimize energy consumption and guarantee the desired comfort level of residents, any indoor energy control system should consider the occupancy dynamism within houses and the occupants' behavior patterns. Moreover, there is a growing demand for localized and personalized comfort controls in residential buildings to improve the occupants’ satisfaction. This paper presents a Personalized Energy and Comfort Management system (PECMS) that optimizes building energy consumption and meanwhile maintains residents’ intended comfort levels by predicting their trajectories. Considering home thermal characterization, PECMS coordinates the building system devices and residents by anticipating residents’ behavior using an activity mining and tracking method. With this capability, efficient scheduling of the electrical devices would be achieved. PECMS is simulated and tested on a dataset from a real-world smart home project, including the home’s actual thermal zones, temperatures, and residents’ preferences. Comparative analysis is conducted to evaluate PECMS against existing methods and systems, showcasing its effectiveness in achieving the desired trade-off between energy consumption and occupant comfort levels. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Energy utilization

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Deep Learning-Based Detection and Severity Analysis of Corona and Pneumonia Using CNN with Wolf Optimization Algorithm 9

Deep Learning-Based Detection and Severity Analysis of Coron...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Kutti, M. Beula Karthick, T. Srmist Department of Computer Science And Engineering Chennai Kattankulathur India School of Computing Srmist Department of Datascience And Business System Chennai Kattankulathur India

ISBN: (纸本)9798350365092

Effective identification and severity analysis of two serious respiratory diseases-pneumonia and corona-are desperately needed, and this study attempts to fill that gap. Early diagnosis and treatment of many conditions depend critically on prompt intervention. Diagnoses are made slowly and subjectively by existing procedures, which frequently rely on human interpretation of medical imaging data. The existing methods for severity analysis do not have a cohesive structure. Our study presents an all-encompassing strategy to get around these restrictions. First, to improve the quality of the images, grayscale-based pre-processing is applied. Next, marker-based watershed segmentation is used to precisely locate anomalies. Feature extraction and categorization are done using a convolutional neural network, or CNN. Specifically, for severity analysis, we optimize the CNN parameters by including the Wolf Optimization Algorithm (WOA). Grayscale pre-processing guarantees the best possible picture quality;marker-based watershed segmentation improves the accuracy of lesion identification;CNN streamlines feature extraction;and WOA supports the study of disease severity. Python is used in the study's implementation, which takes advantage of its many machine learning frameworks and tools. The outcomes show how well the suggested methodology works for precisely identifying and evaluating the levels of corona and pneumonia. By integrating WOA with CNN, the shortcomings of current techniques are addressed and diagnostic accuracy and severity evaluation are improved. When compared to alternative approaches like CNN-SVM, Bayesian CNN, DCNN with BPNN, the suggested CNN with WOA (99.3%) shows an average accuracy gain of about 11.68%, demonstrating higher performance in disease diagnosis and severity analysis. This innovative method offers a methodical and effective framework for better patient treatment, which has the potential to advance the field of respiratory disease diagnostics. © 2

关键词： Extraction

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Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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A Real Time Prediction Model for Round Winners of Counter Strike:Global Offensive 3

A Real Time Prediction Model for Round Winners of Counter St...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Paul, Rosebell Sajeer, Rihan Vaishnav, M.V. Abraham, Savio Vinu Sahajan, Shreya SCMS School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350396157

This research study aims to create a full time winner predictor program for Counter-Strike Global Offensive tournaments that can predict the round winner of the game being spectated. The algorithm creates accurate predictions of the round winner. The data required for the prediction has to be extracted from the game servers. The data is collected from the game servers using an API. The prediction can be viewed live by the user with the help of a GUI. The project can provide CS:GO enthusiasts and bettors with real-time insights and predictions for round winners in competitive matches. system aims to enhance the user's understanding of match dynamics and support informed decision making. © 2024 IEEE.

关键词： Forecasting

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Machine Learning-Based Chronic Kidney Cancer Prediction Application: A Predictive Analytics Approach

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Biomedical Materials and Devices 2024年第2期2卷 1028-1048页

作者： Uddin, Khandaker Mohammad Mohi Nahid, Md. Nuzmul Hossain Ullah, Md. Mehedi Hasan Mazumder, Badhan Khan, Md. Saikat Islam Dey, Samrat Kumar Department of Computer Science and Engineering Dhaka International University Faculty of Computer Science and Engineering Patuakhali Science and Technology University School of Science and Technology Bangladesh Open University

Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlier prediction will be very effective to elevate its effect. In this paper, a machine learning-based method for diagnosing CKC at an early stage is proposed. SVM, Decision Tree, Random Forest, KNN, Native Bayes, Logistic Regression, LGBM, CatBoost, and AdaBoost are employed for classification purposes, and among these algorithms, Random Forest, AdaBoost, and LGBM give 99% accuracy. Data pre-processing and feature selection help improve the accuracy of the proposed model. The outcome of the comparative analysis of our proposed work with eight existing approaches proves the robustness and supremacy of the work. Furthermore, the best model is also used to develop a user-friendly web application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： AdaBoost Chronic kidney cancer Machine learning Random forest and LGBM Web application

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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An efficient attack detection and prevention approach for secure WSN mobile cloud environment

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Soft Computing 2024年第19期28卷 11279-11293页

作者： Vellela, Sai Srinivas Balamanigandan, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India

Wireless Sensor Network (WSN) is a network with a good organization that provides efficient communication services. However, high energy usage and attack data sometimes become the key challenges that degrade the entire network’s Performance. Therefore, in the present research, a novel Chimp-based Curriculum Learning (CbCL) is created for the WSN mobile cloud environment to reduce energy usage and detect the attack node with the CH selection. Initially, the required nodes are deployed in the network. The attack nodes are monitored and removed based on Chimp's optimal functions;optimal CH is selected for the data transfer. Finally, data is shared between the attack node and normal nodes. The created framework was tested in the Matlab environment, and the network efficiency parameters were calculated. To validate the improvement, the results are related to the existing models. The higher Performance of the CbCL satisfied the network’s efficient energy management and attack detection process. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Sensor nodes

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Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： computer vision

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Ornamental Portable Battlefield Surveillance using Generative Adversarial Networks 2

Ornamental Portable Battlefield Surveillance using Generativ...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Kaviya, K. Bhavani, R. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328202

This article investigates the sole use of generative adversarial networks (GANs) for portable battlefield surveillance equipment optimisation. High-resolution and clear pictures are produced by the strategic application of GANs for accurate object recognition, anomaly detection, and real-time video quality enhancement. In military applications, the resultant GAN-centric surveillance system exhibits increased accuracy and efficiency. We report major progress in object detection, anomalous activity detection, including video quality improvement. This GAN-centric approach significantly enhances situational awareness, allowing military personnel to make quick and accurate decisions in a variety of dynamic and varied operational settings. The study demonstrates how GANs may effectively advance military technology for surveillance on mobile platforms, strengthening national security standards in the process. The results highlight how GANs are uniquely adept in powerful improvements in military surveillance capabilities, which in turn improves their operational efficiency. In order to improve both the visual appeal and the efficacy of surveillance in a variety of military environments, this research paper suggests a novel way to improve portable battlefield surveillance systems through the integration of Generative Adversarial Networks (GANs). The technique emphasises discrete design and real-time processing capabilities for portable devices, with a focus on the creation and implementation of a GAN-based system. © 2024 IEEE.

关键词： Efficiency

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