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Comparative Analyais of CNN Architectures for Deep Fake Detection

Comparative Analyais of CNN Architectures for Deep Fake Dete...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Parth Wani Sachin Chavan Siddhesh Paithankar Diptee Ghusse Sunita Barve School of Computer Engineering MIT Academy of Engineering Pune India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

DeepFake technology, driven by advanced generative models, threatens online authenticity and privacy. This paper presents a comparative analysis of three convolutional neural network (CNN) architectures—VGGFace16, DenseNet-121, and a custom CNN model—for DeepFake image detection. The study utilizes the 140k Faces Dataset, comprising 70,000 real and 70,000 synthetic images, and the Real and Fake Face Detection Dataset from Yonsei University, ensuring a diverse and well-balanced training set while accounting for computational constraints. Feature extraction from the final convolutional layers, dimensionality reduction via Principal component Analysis (PCA), and classification with a Support Vector Machine (SVM) using a polynomial kernel form the core methodology. DenseNet-121 achieved the highest accuracy (97%) on grayscaled images, while the augmented custom CNN balanced accuracy (86%) and interpretability, attaining a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 0.953. PCA visualizations confirmed the models’ ability to distinguish real from fake images. The findings underscore dataset selection’s role in model performance and the necessity of resource-efficient training. Future work will expand dataset diversity, explore cross-dataset validation, and leverage advanced computational resources to enhance generalization, contributing to more robust DeepFake detection systems.

关键词： Support vector machines Training Deepfakes Accuracy Computational modeling Computer architecture Feature extraction Polynomials Convolutional neural networks Principal component analysis

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Time-aware and Semantic Resume Analysis for Talent Similarity Calculation

Time-aware and Semantic Resume Analysis for Talent Similarit...

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Canmei Huang Li Cheng Feng Yao College of Systems Engineering National University of Defense Technology Changsha China

Talent similarity calculation is an important and useful research problem for precise talent search in online recruitment. However, the semantic information and time-aware factor in the talent resume make it challenging to obtain effective talent similarity. In this paper, we proposed a time-aware and semantic resume analysis based talent similarity calculation method, termed TSRA. TSRA mainly learns the similarity between two key parts in the talent resume, i.e., skills and work experiences. The pre-trained language model is firstly employed to generate word embeddings to catch the semantic information in the resume. A greedy matching approach is adopted for skills similarity calculation to align the skill sets with different sizes. For work experience similarity, TSRA transforms the work experiences into a sequence with a time series component. Then a dynamic programming algorithm is utilized to integrate the job title embedding, duration factor, and freshness factor. Finally, talent similarity is regarded as the weighted sum of these two types of similarities. The comprehensive experimental study shows that TSRA significantly outperforms state-of-the-art methods.

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Investigating Low & High Transfers Training for Multi-task Face Identification Using a Semi-supervised Neuronal Networks

Investigating Low & High Transfers Training for Multi-task F...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Durga Prasad Kavadi Asif Hasan Rajesh Tanna Rajesh Singh Jayapal Lande Mohit Tiwari Department of CSE IcfaiTech (Faculty of Science and Technology) IFHE Hyderabad India Department of Psychology Aligarh Muslim University Aligarh Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology Duvvada Uttaranchal Institute of Technology Uttaranchal University India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

Recently years have seen a significant increase in research on visual identification issues including identification, aging, sexuality, & expression identification. This same classification of the majority of non-verbal Living beings Interactions scenarios became dependent on their implementation in robotics systems. Deep convolutional neuronal systems have demonstrated to be efficient in this respect when analyzing various face expressions, but at a heavy price: they need a sizable quantity of task-specific labelled information to attain maximal generalisation. A uniform semi-supervised deep network system is suggested in this research to deal with this issue. Our combination model consists of a set of permutation networks that fine-tunes the high-level faces notions for the identification of individuality, time of life team, sex, & facial expressions, as well as an unsupervised deep generative confrontational connection that gets to know basic character traits of face *** the way you look. To avoid the need for a significant quantity of highly labelled learning information, our system uses continuous laterally interconnections among the convolutional streams so that it can exchange the high-abstraction specifics of all of such jobs. We suggest a number of tests to assess each component of our hybrids system, particularly the influence of the progressing interconnections on acquiring the individual image identification problems, & we find that our framework outperforms task-specific systems.

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Software defect prediction using dimensionality reduction and deep learning 3

Software defect prediction using dimensionality reduction an...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Massoudi, Massoud Jain, Nameet Kumar Bansal, Pranay Parwan University Software Engineering Department Charikar Afghanistan Delhi Technological University Department of Computer Engineering Delhi India

ISBN: (纸本)9780738111834

Software Defect Prediction is a significant angle to guarantee programming quality. Deep Learning methods can likewise be utilized for the same. In this paper, we propose to extract a bunch of expressive features from an underlying arrangement of essential change measures using different dimensionality techniques namely PCA and Kernel PCA, and then train a classifier based on the extracted features using Decision tree and Artificial Neural Networks. We utilize five open source datasets from NASA Promise Data Repository to play out this comparative study. For evaluation, we have used three widely used metrics: Accuracy, F1 scores and Areas under Receiver Operating Characteristic curve. It is found that Kernel PCA along with Artificial Neural Network as a classifier outperformed all the other techniques. © 2021 IEEE.

关键词： Principal component analysis

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Demand Side Management System using Programmable Logic Controllers

Demand Side Management System using Programmable Logic Contr...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： R. Prem Kumar C. Mohanraj R. Karthikeyan J. Prameeth Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Coimbatore India

Demand Side Management (DSM), which regulates consumer energy usage to lower peak demand and boost system reliability, is a crucial component of contemporary power systems. This study suggests a novel method for programming. The proposed control device for DSM implementation employs programmable logic controllers (PLCs). The proposed system includes a central control unit that communicates with multiple smart meters installed in households and commercial buildings via PLCs. The control unit collects real-time data on energy consumption and uses it to manage the demand in a coordinated and efficient manner. The design and implementation of the proposed DSM system using PLCs. Also, this study discusses about the hardware and software components used in the proposed system, including the PLCs, communication protocol, and user interface. Further, this study describes the control algorithms that are used to manage the demand and its implementation using ladder logic programming. Finally, this study shows the findings from simulations and experimental tests performed to assess the efficiency and dependability of the suggested DSM system. Customers can also monitor their energy use with the system’s user interface, and they can change their usage habits in reaction to demand signals. The proposed system provides accurate and timely control of energy consumption and allows consumers to adjust their energy usage in response to changing demand signals. The system is also easy to implement and can be easily scaled to meet the needs of large power distribution networks. The suggested DSM, in general PLCs have the potential to increase the effectiveness and dependability of any system while benefiting both utilities and consumers.

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Active Defense Strategy for Elastic Power Grid Considering Load Importance

Active Defense Strategy for Elastic Power Grid Considering L...

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Energy, Power and Electrical Technology (ICEPET), international conference on

作者： Qingyu Fu Yulong Zhao Xinyu Li Yuhan Wu Ying Wang Chunming Liu School of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

In recent years, typhoons have been occurring more and more frequently in southeastern China, which has a significant impact on the safe operation of power systems. The traditional active defense strategy does not consider the load importance easily leads to the masking phenomenon, so this paper proposes an active defense strategy that considers the load importance to solve the problem that the actual resilience is smaller than the calculated resilience when the important load is out of power. In this paper, firstly, a typhoon wind field model is established to simulate the effect of typhoon on power components. Second, a Markov decision process is used to model the system state transitions. Again, component failure probabilities and typhoon spatial and temporal characteristics are considered to provide a generative scheduling strategy for each possible system state. Various system constraints are considered in the scheduling of the actual system, including the generator creep rate and the importance of different loads. Finally, the proposed strategy is validated under various impact scenarios using an IEEE 9-node system. The results show that the method in this paper improves the resilience of the grid compared to the minimum load-cutting strategy and avoids masking due to the outage of important loads.

关键词： Wind Tropical cyclones Power supplies Markov decision processes Creep Power grids Generators

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Leveraging generative Adversarial Networks for Improved Vehicle Accident Recognition in Urban Areas

Leveraging Generative Adversarial Networks for Improved Vehi...

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Advancement in Technology (ICONAT), international conference for

作者： Shiva Mehta Danish Kundra Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Using surveillance systems to identify vehicle accidents is crucial in emergency response and efficient traffic control since these events occur frequently. This piece of research introduces a new approach to identifying car accidents. This paper proposes a model incorporating a pre-trained CNNGAN to enhance the performance of identifying accidents in surveillance videos. The characteristics of the proposed model were evaluated by training and testing it on a dataset including $\mathbf{1 0, 0 0 0}$ photographs divided into five different types of accidents. Our proposed CNN outperformed the CNN-GAN model and the conventional CNN method with a classification accuracy of 0.95 a precision of 0.94, a recall value of 0.93, and an $F 1$ score of 0. 93. In regards to the enhancement of the quality of the images, it was observed that the GAN component was essential. This helped improve the CNN’s feature extraction, as revealed by the high confusion matrix figure and improved the ROC curve with a larger AUC. In this paper, the structure of the proposed model is discussed alongside its training processes (data augmentation and normalization), and a detailed evaluation of the model’s performance against other models proposed in past studies is provided. Future work will focus on expanding the sample of different environmental conditions to cover a broader spectrum and comparing the possibility of using real-time data processing to integrate the model into the real-time monitoring system. The first goal involves developing an efficient system that can accurately diagnose and classify automobile crashes in various settings, thus enhancing society’s general security and the strategy for managing traffic flow.

关键词： Training Accuracy Surveillance Traffic control generative adversarial networks Data models Real-time systems Convolutional neural networks Automobiles Accidents

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A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time

A Scalable Platform to Collect, Store, Visualize and Analyze...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Pravin Ramdas Kshirsagar Dhoma Harshavardhan Reddy Mallika Dhingra Dharmesh Dhabliya Ankur Gupta Department of Artificial Intelligence S B Jain College of Engineering and Technology Nagpur Maharashtra India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Mumbai Maharashtra India Department of Mathematics and Statistics Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India

Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. By using the programming interface key, Twitter data for this article was collected from Twitter. Similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection.

关键词： Social networking (online) Blogs Multimedia Web sites Organizations Footwear programming Reflection

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Monadic programming Featured Teaching Innovation of Compilation Experiments: SONG et al.: Monadic programming Featured Compilation Experiments 2021

Monadic Programming Featured Teaching Innovation of Compilat...

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3rd international conference on Intelligent Medicine and Image Processing, IMIP 2021

作者： Song, Lihua Wang, Haitao College of Command Control Engineering Army Engineering University of PLA China Nanjing Audit University Jinshen College China

ISBN: (纸本)9781450390354

Principles and techniques of compilation is an important core course in computer science, of which compilation experiments are a key component. By implementing a compiler by themselves, students will gain a deeper understanding of how theories and techniques related to compilation interact with each other. However, the widely adopted programming languages in experiments expose too many low-level details like data structures, interfaces, and intermediate results. This often leads to programs obscure and unintuitive, which not only raise the difficulty of completing experiments, but also raise the difficulty of understanding and relating programs to involving algorithms. To address this issue, we introduced monadic programming which is from functional language background. It shields minor details by a set of monad operators to enable programmers to focus on the main ideas. The similarity between a monadic program and the algorithm behind it also makes it possible for teachers to give effective, comprehensive feedback on students' programs in time. The teaching reform based on this idea is introduced in detail. And the past three years of practice have shown promising results. © 2021 ACM.

关键词： Functional programming

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Physical Computational Models for Horizontal Learning in STEM 53

Physical Computational Models for Horizontal Learning in STE...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Liu, Ting Quek, Francis Hordemann, Glen Texas A&m University College StationTX United States

ISBN: (纸本)9798350336429

'What are we learning this for?' is a common refrain heard in education. Showing how engineering practices are relevant to everyday needs like building science models in K-12 classes may lead to greater motivation for students in STEM learning. This paper investigates how horizontal integration of different streams of learning may enable students to see why and how each element of knowledge is relevant to the whole. By combining microcontroller-based electronics, programming, and 3D design and fabrication to build science models, students may develop a stronger appreciation of how the component technologies interrelate (internal contextualization) and how these integrated computational models contribute to the enterprise of science (external contextualization). Furthermore, students in science classrooms see how computation can make physical models dynamic to represent changes and relationships more effectively. The implicit representational science models and the incorporation of technology and computation into a regular science curriculum make the knowledge transferable beyond an individual lesson. Students learn the component technologies cumulatively through a sustained process of engagement across multiple science modeling projects. The major challenges of this approach are how the science modeling projects may be designed and what factors have to be considered in the designs to realize this learning progression. To answer these questions, this paper describes a rigorous design process for the Physical Computational Models (PCMs) that incorporates science model correctness, progression of learning across all streams, pedagogical efficacy, classroom dynamics and manufacturability. In addition, we implemented a year-long project using our Horizontal Learning (HL) approach at two local public schools. This paper reports in detail two PCMs we developed for authentic 5th and 6th grade classrooms to illustrate the integration of the learning streams in science, compu

关键词： 5th and 6th grade classrooms Computation engineering education Horizontal learning Leaning progression Science models STEM integration Sustained learning

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