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VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity

computers, Materials & Continua 2023年第11期77卷 1617-1644页

作者： Junqiang Jiang Zhifang Sun Xiong Jiang Shengjie Jin Yinli Jiang Bo Fan School of Information Science and Engineering Hunan Institute of Science and TechnologyYueyang414006China Department of Computer and Information Science Linköping UniversityLinköping58183Sweden Department of Safety and Quality Hunan Construction Investment GroupChangsha410004China

The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf *** with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these ***,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early *** solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this *** first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering *** 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.

关键词： Intelligence optimization algorithm grey wolf optimizer(GWO) manhattan distance symmetric coordinates

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The Societal Implications of False Positives Using Machine Learning 6th

The Societal Implications of False Positives Using Machine L...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Dubey, Anurag Mishra, Aryan Tayal, Divyansh Yadav, Aarush Jamshed, Aatif Department of IT ABES Engineering College AKTU Lucknow India Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9789819734412

The article aims to investigate the societal consequences of false positive detection through a systematic review leveraging ML (machine learning) techniques. It seeks to assess the impact of inaccurate detections on various aspects of society, such as healthcare, security, and privacy, to inform better decision-making and policy development. The study employs a comprehensive dataset encompassing diverse sources and topics to train and validate the model's efficacy. Results showcase promising outcomes, indicating that sentiment analysis can effectively contribute to the identification of misleading information. The proposed approach not only demonstrates robust performance in distinguishing between genuine and fake news but also provides insights into the emotional underpinnings of deceptive content. This research contributes to the evolving field of fake news detection, offering a nuanced perspective that aligns sentiment analysis with the imperative of fostering a more informed and resilient information landscape. The purpose of this work is to propose a notion and methods for identifying misinformation. The process of gathering news items and then utilizing SVM (SVM(Support Vector Machine)) to categorize them as either authentic or fake is accomplished through the utilization of ML (machine learning) and natural language-processing processes. Comparing the results of the proposed model with those of the existing models is the purpose of this comparison. With an accuracy rating of 93.6%, the model that was provided is performing efficiently and accurately identifying the outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Optimizing the Parameters of Fluidized Bed Dryer 9

Optimizing the Parameters of Fluidized Bed Dryer

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Soni, Binjal Padhiyar, Jay Ragupathy, A.G. Choudhury, Amitava Bharti, Santosh Pandit Deendayal Energy University Department of Computer Science And Engineering Gandhinagar India Zydus Group Department of Digitalization And Automation Ahmedabad India

ISBN: (纸本)9798350394474

The present study centres on the application of an all-encompassing methodology for optimizing the parameters of Fluidized Bed Dryer (FBD). First, a dataset with various FBD attributes are subjected to Principle Component Analysis (PCA). Subsequently, a variety of regression algorithms, including multiple linear regressors, support vector classifiers, decision trees, random forests, and gradient boosting regressors, are employed to analyze the identified key attributes. These attributes comprise the actual values of inlet air flow, inlet air temperature, exhaust air temperature, loss on drying, product temperature, and total drying time. When it comes to predicting the total drying time, the gradient boosting regressor performs better than others. A novel optimization technique called Bayesian optimization is used to find the optimized values of highly correlated features with the total drying time in order further maximize efficiency. This integrated approach uses both sophisticated machine learning algorithms for predictive modelling and optimization techniques to fine-tune specific parameters that are critical for the Fluidized Bed Dryer. The study's findings provide useful information about enhancing FBDs' overall performance and efficacy, which advances drying processes in a variety of industries. © 2024 IEEE.

关键词： Decision trees

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Smart Web Dictionary using OCR 1

Smart Web Dictionary using OCR

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Verma, Sahil Ashraf, Azhar Rawat, Babita Malhotra, Atul Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali14030 India Chandigarh University Department of Computer Science & Engineering Chandigarh India Uttaranchal University Dehradun India Lovely Professional University Phagwara India

ISBN: (纸本)9798350373042

Language and Linguistics has a significant impact on our daily processes that are larger than we perceive. Language is a book that we utilize to convey ideas. Nowadays language has developed so much that writing games have played an important role in shaping our social systems that allow us to work together and be together. Due to the diverse nature of the environment and the environment around our planet, we have unparalleled dynamic language structures that make it difficult for one multilingual person to understand and for another vernacular, resulting in poor communication. Poor communication can lead to misconceptions and confusing issues that can be handled properly. Thus, contact and transmission are main part, particularly in this new era. In this paper we will explain more about the pipeline system that applies a merger of CNN, NLP and NLTK, provides 94% worldwide perfections, questions and the trading based on plans. © 2024 IEEE.

关键词： Natural language processing systems

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Detecting Android Malware with Convolutional Neural Networks and Hilbert Space-Filling Curves

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SN computer science 2024年第7期5卷 1-25页

作者： Mbungang, Benedict Ngaibe Ali Wacka, Joan Beri Tchakounte, Franklin Polatidis, Nikolaos Nlong II, Jean Michel Tieudjo, Daniel Department of Mathematics and Computer Science University of Ngaoundere Ngaoundere Cameroon Cybersecurity with Computational and Artificial Intelligence Research Group University of Ngaoundere Ngaoundere Cameroon Department of Computer Science University of Buea Buea Cameroon School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

computer vision techniques have advanced greatly in recent years through deep learning, achieving unprecedented performance. This has motivated applying deep learning to malware detection through image-based approaches to circumvent extensive feature engineering for diverse threats. However, existing work converting Android binaries to rectangular images neglects the intrinsic byte sequence structure, introducing spurious spatial relationships that weaken detection accuracy. To address this, space-filling curves have mapped binaries to images while preserving ordering. This paper proposes a novel method using Hilbert space-filling curves to visualize and classify Android apps. Bytecode is extracted from Dalvik Executable (DEX) files and transformed to grayscale images via Hilbert coding for model training. Additionally, a novel and balanced image dataset is proposed consisting of Hilbert transformations for 4995 benign and 4995 malicious Android apps randomly sampled from the AndroZoo repository. Experiments using this dataset evaluated pre-trained InceptionV3, VGG16, ResNet50 and EfficientNetB0 via transfer learning. A custom Convolutional Neural Network (CNN) was also trained from scratch. InceptionV3 achieved the highest performance at 97.99% accuracy, 98.50% precision, 97.50% recall and 97.99% F1-score. Comparative assessment with previous image-based malware detection research indicates our approach outperforms state-of-the-art approaches. By leveraging Hilbert space-filling curves to map binaries to images while preserving sequential relationships, detection accuracy is improved over methods introducing extraneous spatial representations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Android Hilbert space-filling curve Image Malware detection

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Visual Robotic Manipulation with Depth-Aware Pretraining

Visual Robotic Manipulation with Depth-Aware Pretraining

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Li, Jinming Wang, Wanying Peng, Yaxin Shen, Chaomin Zhu, Yichen Xu, Zhiyuan School of Science Shanghai University Department of Mathematics China Midea Group China School of Computer Science East China Normal University China

ISBN: (纸本)9781665481090

Recent work on visual representation learning has shown to be efficient for robotic manipulation tasks. However, most existing works pretrained the visual backbone solely on 2D images or egocentric videos, ignoring the fact that robots learn to act in 3D space, which is hard to learn from 2D observation. In this paper, we examine the effectiveness of pretraining for vision backbone with public-available large-scale 3D data to improve manipulation policy learning. Our method, namely Depth-aware Pretraining for Robotics (DPR), enables an RGB-only backbone to learn 3D scene representations from self-supervised contrastive learning, where depth information serves as auxiliary knowledge. No 3D information is necessary during manipulation policy learning and inference, making our model enjoy both efficiency and effectiveness in 3D space manipulation. Furthermore, we introduce a new way to inject robots' proprioception into the policy networks that makes the manipulation model robust and generalizable. We demonstrate in experiments that our proposed framework improves performance on unseen objects and visual environments for various robotics tasks on both simulated and real robots. © 2024 IEEE.

关键词： Contrastive Learning

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Intelligent Technique for Smart Locust Swarm Detection and Prevention Shield for Agricultural Fields 2

Intelligent Technique for Smart Locust Swarm Detection and P...

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Mehta, Kapil Sood, Vandana Mohindru Aayushi, Aayushi Chhabra, Pratham Chandigarh Group of Colleges Department of Computer Science and Engineering Punjab Mohali India Chitkara University Institute of Engineering & Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350322064

In today's world, the agriculture industry has a lot of room for growth by incorporating the newest technical progressions and decision support systems to have enough surplus food output to feed the growing population. To perform and monitor numerous agricultural tasks in the fields, smart devices, remote sensors, the Internet of Things (IoT), and UAVs/Drones can be utilized to meet market demand for food and sustain the planet's survival. Since ancient times, locust and pests have destroyed fields and caused considerable problems with food security. Both historical and modern literature has reported serious outbreaks. Innovative technologies, such as the Internet of Things (IoT), offer a lot of promise to improve agricultural operations. Farmers will be able to make more informed decisions in less time, allowing them to manage their crops more effectively while lowering costs and boosting yields. The offered paper covers several topics linked to IoT-based technologies, as well as the many types of devices and sensors that can be used to detect locust and prevent crop destruction. The Smart Locust Swarm Detection and Prevention Shield is a machine-assisted repellent system in which Speech Recognition Sensors are utilized to detect the voice of a locust swarm using a Noise Compensated Model capable of detecting the ideal vocal qualities even in loud environments. This paper explains the components required to detect the presence of a locust swarm, including Voice Recording sensors, Motor Module Controller, GSM Module, Microphone, Solar Panel Directional Speakers, and Voice Recognized Input Signal transmitted by the Motor Module Controller. This paper looks at how the IoT is being utilized in agriculture to achieve smart and precise farming. Conventional survey methods fell far short of delivering the accurate forecasting and ongoing locust tracking that was needed. Traditional survey methodologies fall far short of meeting the need for precise forecasting and real-ti

关键词： Speech recognition

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Delay and Cost Aware Adaptive Deployment and Migration of Service Function Chains in 5G 23

Delay and Cost Aware Adaptive Deployment and Migration of Se...

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10th International Conference on Networking Systems and Security, NSysS 2023

作者： Munshi, Joy Sultana, Sumaya Hassan, Md. Jahid Roy, Palash Razzaque, Md. Abdur Kabir, Syed Ahsanul Department of Computer Science and Engineering Green University of Bangladesh Narayanganj Dhaka Bangladesh Green Networking Research Group Department of Computer Science and Engineering University of Dhaka Dhaka Dhaka Bangladesh

ISBN: (纸本)9798400708787

With the increasing demand of 5G network applications, efficient placement of virtual network functions (VNFs) of the service function chaining (SFC) is a crucial problem to address for providing real-time services at reduced deployment costs. However, limited effort has been made in the literature works to jointly minimize resource usage cost and service delay in the 5G network through migration of VNFs when and where it is required. In this paper, we have developed an optimization framework for VNF placement of SFC requests, which brings a trade-off between the aforementioned two objectives. The developed framework is a multi-objective linear programming (MOLP) problem that takes into consideration the dynamic pricing scheme of the resources and user application demands. The experimental findings demonstrate that the proposed system outperforms state-of-the-art approaches in terms of service latency and resource usage costs as high as 10% and 15%, respectively. © 2023 ACM.

关键词： Network function virtualization

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Unveiling Early Signs: A Deep Learning Approach for Pre-symptomatic Disease Detection in Medical Imaging 5

Unveiling Early Signs: A Deep Learning Approach for Pre-symp...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Sharma, Avinash Barua, Tarkeshwar Garg, Prachi Parul, Goyal Saxena, Rini Mohuddin, Khalid Chandigarh Group of Colleges Department of Computer Science and Engineering Mohali140307 India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Maharishi Markandeshwar Deemed to be University Department of Computer Science and Engineering Mullana Ambala India King Khalid University College of Business PO Box-3247 Abha61471 Saudi Arabia

ISBN: (纸本)9798350376685

Catching diseases early is a big challenge in healthcare. Traditional methods often wait for symptoms to appear, which can be too late. Medical images, like X-rays and CT scans, can give us a look inside the body to find problems early. This study uses deep learning (DL) to analyze these images and find subtle signs of disease before symptoms show up. The researchers created a new DL model that can work with different types of medical images. This model uses a type of AI called convolutional neural networks (CNNs) to learn complex patterns in the images. These patterns might be too small for humans to see, but they could be signs of an early stage of disease. One big problem with using DL for early disease detection is that we don't have many medical images of people who have the disease before they show symptoms. This study explores ways to get more data, like using existing data in a new way or using less labeled data. Another challenge is understanding how the DL model makes its decisions. This is important in healthcare because we need to know why the model thinks someone might be sick. The study looks into ways to make the model more explainable. The researchers tested their model on many different types of medical images and found that it can accurately identify early signs of disease. They also explored how this model could be used in real-world medical settings to improve patient care. This research shows great promise for preventing diseases and improving patient outcomes. © 2024 IEEE.

关键词： Clinical Integration Data Augmentation Data Scarcity Earlier Diagnosis Early Diagnosis Enhanced Treatment Efficacy Ethical Considerations (data privacy, bias) Explainable AI (XAI) Improved Patient Outcomes Medical Imaging (X-ray, CT scan Model Interpretability MRI) Performance Metrics (accuracy, sensitivity, specificity) Potential Benefits Pre-symptomatic Disease Detection Preventative Medicine Reduced Healthcare Costs Semi-supervised Learning Transfer Learning

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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

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computer Systems science & Engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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