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Adaptive Tasmanian Devil Optimization algorithm based efficient task scheduling for big data application in a cloud computing environment

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Multimedia Tools and Applications 2024年 1-20页

作者： Mishra, Ashis Kumar Mohapatra, Subasis Sahu, Pradip Kumar Biju Patnaik University of Technology Odisha Rourkela India School of Computer Science Odisha University of Technology and Research Odisha Bhubaneswar India Department of Information Technology Veer Surendra Sai University of Technology Odisha Burla Sambalpur India

One of the most difficult issues in cloud computing is scheduling tasks on appropriate resources on the *** is significant because multiple tasks may need to be efficiently scheduled across different virtual machines to maximize resource utilization and minimize makespan. As a result, various efforts have been made to use metaheuristic algorithms to tackle the task scheduling problem. However, these techniques may occasionally experience early convergence and be trapped in local search. This research proposes a multi-objective-based task scheduling in cloud computing for big data applications to address these issues. To accomplish this goal, the adaptive Tasmanian Devil Optimization (ATDO) method is created in this study, with a focus on resolving challenging optimization issues. Following that, the opposition-based learning technique (OBL) is combined with TDO to maintain the population diversity and improve convergence on the ideal answer. In addition, cost, makespan,and resource utilization are taken into account when designing the multi-objective function (MOF). The proposed strategy included efficient solution representation, efficient fitness function derivation, TDO, and OBL operators. The effectiveness of the strategy is examined using several evaluation metrics, and its efficacy is compared with those of other *** proposed method takes a minimum time of 2134 ms for scheduling 1000 tasks and 20.97 degree of imbalance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cloud platforms

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Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

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computer Systems science & Engineering 2023年第6期45卷 2795-2810页

作者： R.Sabitha C.Gokul Prasad S.Karthik Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatoreTamilnaduIndia

In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,***,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to *** recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and *** traditional models,the solution has been made for defending the networks,only to specific ***,in real-time applications,the kind of attack that is launched by the adversary is not ***,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be *** that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various ***,for efficient model design,the work includes the part of feature evaluation of some general attacks of *** IDRM also includes determination of optimal secure paths and Node security for secure routing *** performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN *** is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.

关键词： Enhanced security model wireless sensor networks improved defensive routing mechanism secure paths node security

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Idecomp: imbalance-aware decomposition for class-decomposed classification using conditional GANs

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Discover Artificial Intelligence 2023年第1期3卷 31页

作者： Buczek, Patryk Zidan, Usama Gaber, Mohamed Medhat Abdelsamea, Mohammed M. School of Computing and Digital Technology Birmingham City University Birmingham100190 United Kingdom Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Department of Computer Science Faculty of Computers and Information Assiut University Assiut71515 Egypt

Medical image classification tasks frequently encounter challenges associated with class imbalance, resulting in biased model training and suboptimal classification performance. To address this issue, the combination of class decomposition and transfer learning has proven to be effective in classifying imbalanced medical imaging datasets. Nevertheless, in order to further augment the performance gains achieved through the utilisation of class decomposition within deep learning frameworks, we propose a novel model coined imbalance-Aware Decomposition for Class-Decomposed Classification (iDeComp) model. By incorporating a conditional Generative Adversarial Network (GAN) model, iDeComp is capable of generating additional samples specifically tailored to underrepresented decomposed subclasses. This paper investigates the application of iDeComp using two different medical imaging datasets. iDeComp selects underrepresented samples from the training set of the sublevel classes within each dataset, which are then employed to train separate conditional Deep Convolutional GAN (DCGAN) models and verification models. The conditional DCGAN model is responsible for generating additional samples, while the verification model critically evaluates the appropriateness of the synthesised images. Subsequently, the resulting augmented samples are utilized to train the classification model. To assess the effectiveness of iDeComp, we employ various evaluation metrics including accuracy, precision, recall, and F1 score. The results obtained from our experiments clearly indicate that iDeComp outperforms existing approaches in terms of classifying both imbalanced datasets. © The Author(s) 2023.

关键词： Generative adversarial networks

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Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

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Intelligent Automation & Soft Computing 2023年第4期36卷 911-927页

作者： Kisaezehra Muhammad Umer Farooq Muhammad Aslam Bhutto Abdul Karim Kazi Department of Computer Science and Information Technology NED University of Engineering&TechnologyKarachi75270Pakistan Department of Civil Engineering NED University of Engineering&TechnologyKarachi75270Pakistan

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime *** in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every *** of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are *** most cases,this leads to serious brain injuries in people present at the site in general and the workers in *** is one of the leading causes of human fatalities at construction *** the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-deﬁned personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not ***,such monitoring through technological aids or automated tools is very much *** present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in *** can indicate whether a worker is wearing a hat or *** proposed system usesﬁve different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 *** results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

关键词： Object detection computer-vision personal protective equipment(PPE) deep learning industry revolution(IR)4.0 safety helmet detection

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MSFNet: Multi-Modal Semantic Fusion Network for Herb Recommendation 9

MSFNet: Multi-Modal Semantic Fusion Network for Herb Recomme...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Li, Haobo Jia, Pengtao Li, Chaoyang Cui, Kegang Li, Yanjin College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China Xi'an Hospital of Traditional Chinese Medicine Department of Proctology Xi'an China Xi'an Hospital of Traditional Chinese Medicine Department of Information Technology Xi'an China

ISBN: (纸本)9798350376548

Graph Neural Networks (GNNs) have been extensively utilized in the field of traditional Chinese medicine (TCM) herb recommendation. Nevertheless, the current TCM herb recommendation algorithms have not adequately harnessed the semantic information of symptoms, constraining the system's precision and individualization capabilities. In response, we introduced the multimodal semantic fusion network for herb recommendation (MSFNet), designed to profoundly explore and integrate the semantic characteristics of symptoms. The Semantic Fusion Decoder (SFDec) incorporates a dual-attention mechanism, blending the semantic features of symptoms with the interaction dynamics of TCM herbs. Concurrently, the Symptom Feature Enhancer (SFEnh) guarantees the retention and augmentation of semantic information throughout network dissemination. Experiments conducted on the authentic dataset from Xi'an Traditional Chinese Medicine Hospital have demonstrated that MSFNet outperforms the optimal baseline method, KDHR, by 3.6% in Precision@10, validating the model's efficacy and superiority. © 2024 IEEE.

关键词： Medicinal chemistry

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Efficient computer Vision Inference using Modular Neural Network Techniques

Efficient Computer Vision Inference using Modular Neural Net...

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2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023

作者： Sitepu, Ade Clinton Liu, Chuan-Ming National Taipei University of Technology Electrical Engineering and Computer Science Taipei Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350316803

Deep learning, especially neural networks, has become significant achievements in the realm of artificial intelligence (AI), including areas such as natural language processing, computer vision, and speech recognition. Furthermore, the impressive performance of deep learning models is often followed by a significant drawback: their high computational complexity. This limitation poses a challenge when deploying these models on resource-constrained devices like Internet of Things (IoT) devices. In this paper, we implement and demonstrate the concept of modularity into Deep Neural Networks (DNNs) to reduce the redundant operations and minimize the loss of its performance. © 2023 IEEE.

关键词： Image recognition

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Crop Classification using Convolutional Neural Network 15

Crop Classification using Convolutional Neural Network

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, K. Gopalakrishnan, K. Balaji, S. Jeevanantham, J. Aakhila Hayathunisa, M. Department of Information Technology Velalar College of Engineering Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Kongu Engineering College Erode India

ISBN: (纸本)9798331300579

Deep learning is a powerful machine learning technique that has the potential to revolutionize agriculture. By leveraging deep learning models, we can develop innovative solutions to critical agricultural challenges, such as crop classification, weed percentage estimation, and crop quality identification. Deep learning models can be trained on remote sensing data to accurately classify different crop types. This information can be used by farmers and agricultural stakeholders to monitor and manage their crops more effectively. For example, crop classification models can be used to identify areas of a field that are underperforming or at risk of disease. Weed infestations can significantly reduce crop yields and quality. Deep learning models can be trained on high-resolution imagery and sensor data to accurately estimate weed percentages within fields. This information can be used to develop precise and timely weed management strategies, reducing the need for excessive herbicide usage and minimizing the environmental impact. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. This information can help farmers make informed decisions about harvesting, storage, and distribution. Assessing crop quality is crucial for optimizing harvest decisions and ensuring the delivery of high-quality produce to consumers. Deep learning techniques, such as image analysis and spectroscopy, can be employed to identify various quality parameters in crops, such as size, color, ripeness, and the presence of diseases or blemishes. The continued development and integration of deep learning technologies into agriculture hold promise for advancing global food production and agricultural sustainability. © Grenze Sc

关键词： Weed control

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A Deep Learning Framework with Cross Pooled Soft Attention for Facial Expression Recognition

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Journal of The Institution of Engineers (India): Series B 2022年第5期103卷 1395-1405页

作者： Bodapati, Jyostna Devi Naik, D S Bhupal Suvarna, B. Naralasetti, Veeranjaneyulu Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi India Department of Information Technology Vignan’s Foundation for Science Technology and Research Vadlamudi India

Facial Expression Recognition (FER) is at the heart of Human–computer Interaction (HCI) and has received a lot of attention in the field of computer vision. We present a novel attention-based deep neural network for recognizing facial expressions from images. Initially, regions such as eye-pair, mouth and face are cropped, independently passed through the pre-trained Xception network to obtain deep representations. All of these descriptors may not have same influence while recognizing the type of expression, and some of them may require special attention over others depending on the type of expression. We incorporate attention mechanism into the model to automatically learn the amount of attention to be paid to each descriptor. These attention-based features obtained from all the three regions are then fused using the proposed Cross Average Pooling (CAP) layers to produce a compact and discriminatory representation that ultimately leads to better identification of facial expressions. The proposed cross average pooled soft attention results in compact and discriminatory representations for facial images, allowing for more accurate predictions. The proposed approach is evaluated on two benchmark datasets (JAFFE and CK+), and the experimental results reveal that the proposed model outperforms existing models with an accuracy of 97.67 and 97.46% on JAFFE and CK+ datasets, respectively. © 2022, The Institution of Engineers (India).

关键词： Deep neural networks

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Deep-Q-Based Reinforcement Learning Method to Predict Accuracy of Atari Gaming Set Classification

Deep-Q-Based Reinforcement Learning Method to Predict Accura...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Gobinathan, N. Ponnusamy, R. V.R.S. College of Engineering and Technology Department of Computer Science and Engineering India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

A pivotal acquisition model designed to formulate control strategies directly from high-level perceptual data using reinforcement learning is introduced by us. The training of this model, a convolutional neural network, was done using a different Q-learning algorithm. An output is produced in the form of a value function that indicates anticipated future earnings after processing raw image inputs. Recent demonstrations of cutting-edge performance in reinforcement learning tasks have been made by these neural networks, which leverage Deep Q-Networks (DQNs) based on the Q-Learning algorithm. The planning process for DQNs is significantly different from that of standard deep neural networks, resulting in significant challenges in specific reinforcement learning scenarios. © 2023 IEEE.

关键词： Reinforcement learning

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Autoencoder for Epileptic Seizure Recognition: An Unsupervised Approach 15

Autoencoder for Epileptic Seizure Recognition: An Unsupervis...

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15th International Conference on Innovations in information technology, IIT 2023

作者： Nawaz, Hassan Din, Ikram Ud Almogren, Ahmad Malik, Abdul Mohiz Nawaz, Ali University of Haripur Department of Information Technology Pakistan King Saud University College of Computer and Information Sciences Department of Computer Science Riyadh Saudi Arabia United Arab Emirates University College of Information Technology Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

Anomaly detection in electroencephalogram (EEG) seizure recognition is crucial for timely and effective patient intervention. Traditional machine learning techniques often depend on well-labeled data and defined anomaly patterns, which are not always feasible or available in practical situations. This research investigates the application of autoencoders, a form of advanced learning model, to detect irregularities in EEG data, and compares its performance against a traditional classification algorithm and the Isolation Forest model. We utilize the publicly available EEG seizure recognition dataset from the UCI repository for our experiments. The autoencoder is designed to understand a concise version of normal EEG readings and identify anomalies based on the reconstruction error. The evaluation is based on the AUC-ROC score, a commonly used performance measure. Our findings show that the autoencoder performs much better than the traditional classification algorithm and the Isolation Forest model based on the AUC-ROC score, indicating its superior ability to differentiate between regular and unusual EEG readings. This indicates a hopeful potential of autoencoders in EEG seizure recognition and other anomaly detection tasks, especially in scenarios with limited labeled data or undefined anomaly patterns. © 2023 IEEE.

关键词： Electroencephalography

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