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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Nalini, N. Chaudhary, Ankur Surendran, S. Muthuraja, M. Ahmed, Ibrar Nandhini, T.J. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Noida Institute of Engineering and Technology Department of Computer Science Engineering Delhi India Tagore Engineering College Department of Computer Science Engineering Chennai India Kongu Engineering College Department of Computer Science Engineering Erode India Chennai India

ISBN: (纸本)9798350321425

Network Intrusion Detection (NID) examines data from the network in search of malicious activity to identify illegal access. This investigation will center on Network Intrusion Detection (NID) technology, its progression, and the strategic value it provides. The most common types of criminal activity involving computers are illegal access, theft, and denial-of-service assaults. In recent years, experts in computer security have developed many cutting-edge solutions to protect hosts and networks from these types of threats. Both the military and commercial sectors have a requirement for intrusion detection technology. This is because intrusion detection is the most essential study field for the future of network information security. The idea of pre-processing data before training algorithms is presented in this study. The data that has been preprocessed reveals that the present Random Forest model performs better than alternative ANN models. The NSL-KDD dataset model has an accuracy of 99.12% after data pre-processing and feature extraction have been performed. © 2023 IEEE.

关键词： Feature extraction

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Mitigating Racial Bias in Facial Expression Recognition Using Enhanced data Diversification Techniques 15

Mitigating Racial Bias in Facial Expression Recognition Usin...

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

作者： Sidana, Sagar Thakur, Sujata Negi Kumar, Sandeep Gomathi, R.D. Sinha, Anurag Saini, Arshroop Singh Raj, Petheru Dixit, Karan Kumar, Vikas Al-Khayyat, Ahmed Department of computer science and engineering Maharshi Dayanand University Haryana Rohtak India Department of computer science and engineering Graphic Era Hill University India Department of computer science and engineering Aryan Institute of Technology India Department of English Kongu Engineering College Perundurai Tamil Nadu Erode India School of Computing and information Science IGNOU India New Delhi India Secondary education Emerald Heights School India Reliance Jio Platforms Ltd Bangalore India Department of computer science and engineering Kiet Group of Institutions India Department of computer science and engineering Islamic University Iraq

ISBN: (纸本)9798350370249

With countless new methods and further advancements in computer vision, applying the technology to the everyday lives of people has become inevitable. One such use of computer vision models is recognizing facial patterns and expressions. However, as recent studies have indicated, many models interpreting facial expressions have developed biases towards certain races. This is likely a result of skewed training data;the model may have been trained with more images of a certain race than others. Overall, this can cause inaccuracy when the model is presented with people from different backgrounds. To promote a diverse group of data that the model can process, I present a way to balance biases. First, I fine-tuned a hybrid facial recognition model of state-of-the-art convolutional neural network (CNN) models called DeepFace on the emotion recognition dataset FER2013 to recognize the emotions on people’s faces. Next, using FairFace, which is modeled after the large-scale CNN ResNet-34, I extrapolated the races and facial structures for each type of individual, which I then used to determine the bias and skew within the dataset. I did this by comparing the model’s accuracy when applied to different races. Then, I used a user-instructed stable diffusion model called InstructPix2Pix to modify the images and create a more well-represented and diverse dataset. With this method, I was able to balance the dataset effectively and reduce bias in the image recognition model. However, due to issues with the FER2013 dataset images, many of the transformed images were disfigured, meaning the emotion data within the images was lost. This caused the overall accuracy of the model to drop significantly. Future research can evaluate this method using InstructPix2Pix on different computer vision tasks or with different datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Task Scheduling Optimization in Cloud Computing by Rao Algorithm

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computers, Materials & Continua 2022年第9期72卷 4339-4356页

作者： A.Younes M.KhElnahary Monagi H.Alkinani Hamdy H.El-Sayed Faculty of Computers and Information Department of Computer ScienceSohag UniversitySohag82524Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the ***,task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user’s services demand modification *** main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence *** heterogeneous multiprocessor systems,task assignments and schedules have a significant impact on system *** the heuristic-based task scheduling algorithm,the different processes will lead to a different task execution time(makespan)on a heterogeneous computing ***,a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce(makespan).In this paper,we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing *** basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best *** evaluate our algorithm’s performance by applying it to three examples with a different number of tasks and *** experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

关键词： Heterogeneous processors RAO algorithm heuristic algorithms task scheduling multiprocessing cloud computing

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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computer Modeling in engineering & sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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A Sustainable WSN System with Heuristic Schemes in IIoT

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computers, Materials & Continua 2022年第9期72卷 4215-4231页

作者： Wenjun Li Siyang Zhang Guangwei Wu Aldosary Saad Amr Tolba Gwang-jun Kim School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Computer and Information Engineering Central South University of Forestry and TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityEgypt Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more ***, the upgrading of 5G network will cause a variety of issues increase,one of them is the increased cost of coverage. In this paper, we proposea sustainable wireless sensor networks system, which avoids the problemsbrought by 5G network system to some extent. In this system, deployingrelays and selecting routing are for the sake of communication and *** main aim is to minimize the total energy-cost of communication underthe precondition, where each terminal with low-power should be charged byat least one relay. Furthermore, from the perspective of graph theory, weextract a combinatorial optimization problem from this system. After that,as to four different cases, there are corresponding different versions of theproblem. We give the proofs of computational complexity for these problems,and two heuristic algorithms for one of them are proposed. Finally, theextensive experiments compare and demonstrate the performances of thesetwo algorithms.

关键词： Industrial Internet of Things sustainable wireless sensor network system combinatorial optimization problem heuristic algorithms

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High-Performance Intelligent System for Real-Time Medical Image Using Deep Learning and Augmented Reality 4th

High-Performance Intelligent System for Real-Time Medical Im...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

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computer Systems science & engineering 2023年第7期46卷 125-135页

作者： S.Navaneethan P.Siva Satya Sreedhar S.Padmakala C.Senthilkumar Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Information Technology Seshadri Rao Gudlavalleru Engineering CollegeKrishna521356India Department of Computer Science Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennai602105India

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so *** pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and *** Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of *** improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil *** proposed method is based on very deep architecture and many tricks from recently developed popular *** results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge ***,the region-based black box method for determining pupil center coordinates was *** proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

关键词： BAT algorithm IRIS datasets DCNN pupil detection black box method

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Symbolic Regression Enhanced Decision Trees for Classification Tasks 38

Symbolic Regression Enhanced Decision Trees for Classificati...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Fong, Kei Sen Motani, Mehul Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

ISBN: (纸本)1577358872

We introduce a conceptually simple yet effective method to create small, compact decision trees – by using splits found via Symbolic Regression (SR). Traditional decision tree (DT) algorithms partition a dataset on axis-parallel splits. When the true boundaries are not along the feature axes, DT is likely to have a complicated structure and a dense decision boundary. In this paper, we introduce SR-Enhanced DT (SREDT) – a method which utilizes SR to increase the richness of the class of possible DT splits. We evaluate SREDT on both synthetic and real-world datasets. Despite its simplicity, our method produces surprisingly small trees that outperform both DT and oblique DT (ODT) on supervised classification tasks in terms of accuracy and F-score. We show empirically that SREDTs decrease inference time (compared to DT and ODT) and argue that they allow us to obtain more explainable descriptions of the decision process. SREDT also performs competitively against state-of-the-art tabular classification methods, including tree ensembles and deep models. Finally, we introduce a local search mechanism to improve SREDT and evaluate it on 56 PMLB datasets. This mechanism shows improved performance on 77.2% of the datasets, outperforming DT and ODT. In terms of F-Score, local SREDT outperforms DT and ODT in 82.5% and 73.7% of the datasets respectively and in terms of inference time, local SREDT requires 25.8% and 26.6% less inference time than DT and ODT respectively. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Decision trees

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Harnessing CNN Technology for Enhanced Sign Language Recognition in the Deaf and Dumb Community

Harnessing CNN Technology for Enhanced Sign Language Recogni...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Gondhule, Ram Chandra, Khushal Dhule, Chetan Agrawal, Rahul Morris, Nekita Chavan G H Raisoni College of Engineering Department of Data Science Nagpur440016 India Nagpur440016 India

ISBN: (纸本)9798350319019

The domain of sign language recognition poses many challenges to researchers. In recent years, however, several advancements have been made due to the increased availability of data. The objective of this study is to present a novel design for Human-computer Interaction system that will address the daily challenges faced by the deaf and dumb. The proposed solution would be helpful to people who live in remote areas and do not have the accessibility to sign language translators. It would also be beneficial to the deaf people who have to attend interviews and work in offices where there are people who don't know sign language. This paper highlights the potential to create a practical application that can be used to recognize sign language and access important information. © 2024 IEEE.

关键词： Convolutional neural networks

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Accelerating urban scene understanding for autonomous vehicles with Multi-GPU training of Semantic Segmentation 5

Accelerating urban scene understanding for autonomous vehicl...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Jayasimhan, Anusha Pabitha, P. Sri Sivasubramaniya Nadar College of Engineering Department of Computer Science and Engineering Chennai India Anna University Mit Campus Department of Computer Technology Chennai India

ISBN: (纸本)9798331504403

An important challenge in the field of computer vision revolves around semantic segmentation, which involves the partitioning of an image into distinct semantic regions or objects. This paper introduces a semantic segmentation model utilizing the well-regarded Cityscapes dataset for urban scene comprehension, coupled with the UNet architecture. Additionally, a PyTorch-based approach is developed to facilitate efficient training of the model on multiple GPUs within a single node. The UNet architecture comprises an encoder and a decoder pathway, responsible for feature extraction and subsequent upsampling, respectively. Customized for the Cityscapes dataset, the architecture employs a combination of convolutional and pooling layers to adeptly capture both global and local contextual details. To streamline the training process of the proposed model, a PyTorch-based strategy for single node multi-GPU distributed data training is outlined. This strategy utilizes the *** module, permitting the model to be trained on multiple GPUs within a single node. The proposed single node multi-GPU distributed data training technique not only accelerates the training process but also enhances efficiency. In comparison to a single GPU setup, it demonstrates the potential to cut down training time by as much as 50%. This advancement paves the way for more rapid and effective training of complex models, contributing to the advancement of semantic segmentation tasks in computer vision. © 2024 IEEE.

关键词： Semantic Segmentation

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