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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Jagadam, Naveen Goyal, Mayank Kumar Ravulakollu, Kiran Kumar Sharan, Bhagwati Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Ceo N2force Technologies Inc. NapervilleIL60563 United States School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India School of Technology Woxsen University Hyderabad India School of Engineering and Sciences Srm University Department of Computer Science and Engineering AP Amaravati India

ISBN: (纸本)9789380544472

Video game development process is different from other software projects while yet being related to them in software engineering. It is distinctive in that it incorporates the work of teams from several fields (art, music, acting, programming, etc.), and that it seeks to create a compelling gaming experience by using prototypes and iterations. This research study focuses on game development and how it is accomplished utilizing a game engine. The process of creating/developing games from a concept is known as game development. Game engines are software programs that help game developers create games. The literature study discusses about the various game engine. The utility of the 'Unity' gaming engine is investigated in this paper. Unity may be utilized in different fields as study subjects, but there can be certain frequent benefits & obstacles of utilizing it in different case studies. Students learn which game engine to use based on their specific requirements. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Search engines

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A Nudity Detection Algorithm for Web-based Online Networking Platform 10

A Nudity Detection Algorithm for Web-based Online Networking...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Jagadam, Naveen Ravulakollu, Kiran Kumar Goyal, Mayank Kumar Sharan, Bhagwati Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Ceo N2force Technologies Inc. NapervilleIL60563 United States School of Technology Woxsen University Hyderabad India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India School of Engineering and Sciences Srm University Department of Computer Science and Engineering AP Amaravati India

ISBN: (纸本)9789380544472

Social media and online platforms have become an integral part of the day-to-day life of the people and the organizations, which shows a lot of diversity. Many online platforms allow users to share media extensively, like images, videos, and audio but not all the media content shared on the internet is safe for work or should be restricted from particular platforms. Images with nudity are of big concern when it comes to online media sharing and social platforms must take serious measures to restrict users to share images with nudity. The rate of growth of nudity content over the internet or social media is very high. It affects the mental and physical health of individuals. In this paper, a novel method for nudity detection in images is presented. We aim to develop a Machine Learning algorithm for nudity detection in a community networking platform and how it should be integrated with such a platform so that posted images can be thoroughly checked for nudity and if detected how it should be dealt with. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Social networking (online)

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Combining State-of-The-Art Pre-Trained Deep Learning Models: A Noble Approach for Bangla Sign Language Recognition Using Max Voting Ensemble

SSRN

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SSRN 2024年

作者： Humaun, Md. Kabir Musa Miah, Abu Saleh Hadiuzzaman, Md. Shin, Jungpil Department of Computer Science and Engineering Bangamata Sheikh Fojilatunnesa Mujib Science & Technology University Jamalpur2012 Bangladesh School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Fukushima965-8580 Japan Software Engineer at HeavyTask LLC DallasTX United States

Sign language is a crucial medium of communication for individuals with hearing impairments. Recently, many researchers have been working to develop an automatic sign language recognition system for English, Arabic, and Japanese languages to ease communication complexity between deaf and non-deaf communities. However, few systems have been developed for Bangla sign language(BdSL), and most of the existing ones may face difficulties in achieving satisfactory performance. While recent advances in deep learning have dramatically improved image classification tasks, including sign language recognition, ensemble methods offer a pathway for further enhancing BdSL accuracy. This study introduces a cutting-edge approach employing the Max Voting Ensemble Technique for robust BdSL recognition. We incorporate a range of cutting-edge, pre-trained deep neural networks, including Xception, InceptionV3, DenseNet121, ResNet50, and MobileNetV2. These models have been extensively trained on BdSL datasets, achieving individual accuracies ranging from 90.20% to 99.00%. Our method leverages the synergistic capabilities of these models by combining their complementary features to elevate classification performance further. In our approach, input images undergo preprocessing for model compatibility. The ensemble integrates the pre-trained models with their architectures and weights preserved. For each skin lesion image under examination, every model produces a prediction. These are subsequently aggregated using the Max Voting Ensemble Technique to yield the final classification, with the majority-voted class serving as the conclusive prediction through comprehensive testing on a diverse dataset. Our ensemble outperformed individual models, attaining 96.52%, thus demonstrating superior BdSL reliability and accuracy. We evaluated the effectiveness of our proposed method on the BdSL-49 and BdSL-38 datasets to ensure its generalizability. Our ensemble method delivers a robust, reliable and ef

关键词： Deep neural networks

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Automated Classification of Medical Images Using Convolutional Neural Networks 15

Automated Classification of Medical Images Using Convolution...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pillai, N. Muthuvairavan Manimala, S. Rongali, Aruna Sri Gugnani, Anchit Kumar, Senthil A. Sriramakrishnan, G.V. Computer Science and Business Systems R.M.D Engineering College Thiruvallur district Kavarapettai India Computer science and engineering University SJCE JSS Science and technology University Karnataka Mysore India Sr. Software Engineer Department Information Technology Emphasis/Information Technology Ph.D University The country University of The Cumberlands United States Physiotherapy Amity University Rajasthan Jaipur India Artificial Intelligent department Shri Vishnu Engineering College for Women AP Bhimavaram India Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort. ©2024 IEEE.

关键词： Convolutional neural networks

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Cloud-Based Digital Twin for Cybersecurity Threat Prediction

Cloud-Based Digital Twin for Cybersecurity Threat Prediction

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Venkata Ashok Kumar Boyina Thiyagarajan Mani Chettier Dhaval Patolia Neha Gupta Independent Researcher Cumming GA USA Independent Researcher South Windsor CT USA Senior Software Engineer Mastercard Ofallon MO USA School of Computer Science and Engineering Geeta University

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Cyber threats are evolving into the ever-skyscraper-oriented monster with increasing scope and complexity and demand for contemporary security models that focus on live threat detection and response. In this study, a Cloud-Based Digital Twin (CBDT) approach is proposed for predicting and mitigating cybersecurity threats by employing machine learning in the digital twin domain to bolster cyber resilience. The framework proposed becomes a virtualized cloud environment replicating the IT infrastructure of the organization and allowing simulation, detection, and prediction of cyber threats in real time. Through the integration of machine learning models like Random Forest, Convolutional neural networks (CNN), and Long Short-Term Memory (LSTM), the CBDT framework enhances the processes of anomaly detection, threat classification, and incident response automation. LSTM has been shown to offer a best-in-class detection rate (96.1%) of a wide variety of cyber threats and consistently minimizes time to response vs. traditional intrusion detection systems. Furthermore, deployments in the cloud open new capabilities around scale, connecting vast threat intelligence data, and rapid analysis. Conclusively, the investigation highlights the promise of digital twins in cybersecurity, providing a proactive and adaptable defense strategy against the ever-changing landscape of cyber vulnerabilities.

关键词： Cloud computing Threat assessment Real-time systems Digital twins Convolutional neural networks Time factors computer security Long short term memory Anomaly detection Random forests

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Application of YOLOv11 Classification for Efficient Waste Segmentation in Australia's Recycling Facilities

Application of YOLOv11 Classification for Efficient Waste Se...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)

作者： Akash Das Jarin Sayma Amar Nath Barman K. M. Asieb Hasan Dept. of Computer Science Federation University Australia Ballarat Australia Dept. of Information Technology Victorian Institute of Technology Melbourne Australia Software Engineer Glichin’ Dhaka Bangladesh

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

This paper investigates the use of YOLOvll, an advanced deep learning model, for waste classification in Aus-tralian recycling facilities, aiming to improve sorting accuracy and reduce contamination in line with Australia's National Waste Policy. Utilizing the RealWaste dataset, which contains over 10,000 annotated images of waste materials collected at the Whyte's Gully Waste and Resource Recovery facility in Wollongong, NSW, this study demonstrates YOLOvll's superior performance across various metrics. The YOLOvll variants achieved top-l classification accuracies ranging from 82.39% to 85.95% and top-5 accuracies from 99.37% to 99.79 % . The highest-performing model, YOLOvllm-cls, achieved a top-l accuracy of 85.95% and an inference time of 0.5545 ms, balancing classification precision with computational efficiency. These results significantly improve upon prior models used in similar tasks, showcasing YOLOvll ‘s potential to automate waste classification, reduce human error, and enhance recycling operations.

关键词： Deep learning Waste materials Electric potential Accuracy Computational modeling Geoscience Recycling Australia Remote sensing Sorting

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Mixture of Attention Heads: Selecting Attention Heads Per Token

Mixture of Attention Heads: Selecting Attention Heads Per To...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhang, Xiaofeng Shen, Yikang Huang, Zeyu Zhou, Jie Rong, Wenge Xiong, Zhang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University China Sino-French Engineer School Beihang University China Mila University of Montreal Canada Wechat AI Tencent China

Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly focused on the feedforward layer in Transformer architecture. This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism. MoA includes a set of attention heads that each has its own set of parameters. Given an input, a router dynamically selects a subset of k attention heads per token. This conditional computation schema allows MoA to achieve stronger performance than the standard multi-head attention layer. Furthermore, the sparsely gated MoA can easily scale up the number of attention heads and the number of parameters while preserving computational efficiency. In addition to the performance improvements, MoA also automatically differentiates heads' utilities, providing a new perspective to discuss the model's interpretability. We conducted experiments on several important tasks, including Machine Translation and Masked Language Modeling. Experiments have shown promising results on several tasks against strong baselines that involve large and very deep models. © 2022 Association for Computational Linguistics.

关键词： Modeling languages

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Improving Multi-Organ Cancer Diagnosis through a Machine Learning Ensemble Approach 7

Improving Multi-Organ Cancer Diagnosis through a Machine Lea...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Khan, Mohammad Shahbaz Dixit, Rohit R Majumdar, Anurima Madhavi Koti, Valli Bhushan, Sushant Yadav, Vipul Operations and Regulatory Affairs Children's National Hospital Sr R & D DevOps Engineer WashingtonDC United States Senior Data Scientist Siemens Healthineers India Guru Nanak Institute of Technology Department of Electronics and Communication India GIET Degree College Department of Computer Science India Mewar University Department of Computer Science Chittorgarh India Software Developer Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350340600

Numerous methods for feature selection and classification have been developed, and the process of identifying cancer becomes challenging when the quantity of the information is enormous. The machine learning-based ensemble technique was created to address the weaknesses of the previous *** poor survival rates after diagnosis, cancer is a common cause of death that is reported in many industrialized and developing nations. The probability that cancer will be successfully treated rather than allowed to get worse increases with early identification and therapy. Identification of the afflicted regions and significant factors that influence cancer growth is the foundation for the majority of cancer diagnoses. There are several techniques for feature selection and classification, and when there is a large amount of data, finding cancer becomes difficult. The machine learning-based ensemble methodology was developed to solve the shortcomings of the earlier approach. This research has the potential to bring about significant advancements in healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving patient outcomes while addressing ethical and fairness considerations. Its impact extends beyond the research domain, benefiting patients, healthcare systems, and the broader field of artificial intelligence in healthcare. © 2023 IEEE.

关键词： Feature Selection

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia computer science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search

arXiv

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arXiv 2024年

作者： Li, Zehan Zhang, Jianfei Yin, Chuantao Ouyang, Yuanxin Rong, Wenge State Key Laboratory of Complex & Critical Software Environment Beihang University China Sino-French Engineer School Beihang University China School of Computer Science and Engineering Beihang University China

Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. To validate its effectiveness, we propose a modality-agnostic contrastive pre-training approach to improve the alignment of text and code representations of current code language models. Compared to previous models that primarily employ bimodal and unimodal pairs extracted from CodeSearchNet for pre-training, our model exhibits significant performance improvements across a wide range of code retrieval benchmarks. © 2024, CC BY.

关键词： Benchmarking

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