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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

Classification of Real and Deepfakes Visual Samples with Pre...

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proceedings of the 7th international conference on Advances in computing and Data Sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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6th international conference on Variable Neighborhood Search, ICVNS 2018

6th International Conference on Variable Neighborhood Search...

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6th international conference on Variable Neighborhood Search, ICVNS 2018

ISBN: (纸本)9783030158422

the proceedings contain 23 papers. the special focus in this conference is on Variable Neighborhood Search. the topics include: General Variable Neighborhood Search for Scheduling Heterogeneous Vehicles in Agriculture;detecting Weak Points in Networks Using Variable Neighborhood Search;a Variable Neighborhood Search with Integer Programming for the Zero-One Multiple-Choice Knapsack Problem with Setup;A VNS-Based Algorithm with Adaptive Local Search for Solving the Multi-Depot Vehicle Routing Problem;skewed Variable Neighborhood Search Method for the Weighted Generalized Regenerator Location Problem;using a Variable Neighborhood Search to Solve the Single Processor Scheduling Problem with Time Restrictions;An Evolutionary Variable Neighborhood Descent for Addressing an Electric VRP Variant;a Variable Neighborhood Descent Heuristic for the Multi-quay Berth Allocation and Crane Assignment Problem Under Availability Constraints;a Variable Neighborhood Search Approach for Solving the Multidimensional Multi-Way Number Partitioning Problem;on the k-Medoids Model for Semi-supervised Clustering;A General Variable Neighborhood Search with Mixed VND for the multi-Vehicle multi-Covering Tour Problem;A Hybrid Firefly - VNS Algorithm for the Permutation Flowshop Scheduling Problem;Studying the Impact of Perturbation Methods on the Efficiency of GVNS for the ATSP;A GVNS Algorithm to Solve VRP with Optional Visits;complexity and Heuristics for the Max Cut-Clique Problem;A VNS Approach to Solve Multi-level Capacitated Lotsizing Problem with Backlogging;how to Locate Disperse Obnoxious Facility Centers?;Basic VNS algorithms for Solving the Pollution Location Inventory Routing Problem;less Is More: the Neighborhood Guided Evolution Strategies Convergence on Some Classic Neighborhood Operators.

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Addressing the Quality Challenges of Prescription Lensing for AR/MR Devices 6

Addressing the Quality Challenges of Prescription Lensing fo...

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6th conference on Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality-AR, VR, MR

作者： Brown, Erin Eisenberg, Eric Radiant Vision Syst 18640 NE 67th Court Redmond WA 98052 USA

ISBN: (纸本)9781510686069;9781510686076

Roughly half of the world's population requires prescription lenses. To make AR/MR devices practical for these people, makers of smartglasses face a two-fold challenge. First, they need to incorporate display technology with customizable optics that match each wearer's unique prescription. Approaches include devices manufactured with permanent custom-made prescription lenses, after-market secondary lens inserts, curved waveguide optics, headsets designed to fit over a user's existing prescription glasses, and built-in mechanical myopia dials that allow the user to make adjustments. Clearly, the option to embed the display technology directly into prescription lenses will be preferred by daily glasses wearers and is expected to be the nominal path selected by AR/MR device manufacturers to make these devices innocuous and mainstream. thousands of prescription permutations for near-sightedness, far-sightedness, and/or astigmatism exist, thus the second challenge for device makers is how to perform effective quality testing for the wide variety of AR/MR device optics. Accurate measurement and evaluation of visual performance characteristics such as MTF, uniformity, and distortion requires a display measurement system to compensate for the device's corrective optics. For practical reasons, the measurement solution must also be efficient to test devices at production speed. this paper reviews the approaches currently being used for quality testing of AR/MR devices with embedded prescription lenses, including mechanical methods such as reverse compensation optics, and software algorithms to compensate for prescription lensing. We will describe a novel, patent-pending metrology solution, and present research results of testing methods for measuring prescription optics and MTF in device displays. Additionally, suggested metrology focus range requirements are presented and a discussion of the advantage of using diopters as a core unit of measurement-typical "working distance"

关键词： Display metrology display measurement NED HMD augmented reality AR/MR smartglasses field of view FOV MTF prescription vision prescription compensation optics prescription lenses myopia astigmatism

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Emotion-Based Media Recommendation System

Emotion-Based Media Recommendation System

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE international conference on

作者： Sayon Chakraborty Shubham Shreya Singh Mandeep Kaur Nitin Rakesh Monali Gulhane Department of Computer Science & Engineering SET Sharda University India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Nagpur India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

Emotion is a state of physical arousal that is subjective and private to the individual. Human emotions are divided into fear, anger, surprise, sadness, happiness, etc. this emotional umbrella encompasses a wide range of other emotions, including joy and contempt; they are subtle emotions. When it comes to recognizing emotions and present mental states, facial expressions are crucial (i.e., emotional and spiritual). Detecting these emotions can be difficult at times, as even minor variances might result in distinct displays. To detect emotions and produce good results, neural networks and machine learning were applied. Machine learning algorithms have proven to be very useful in pattern recognition and classification and thus can also be used for mood detection. With the development of digital music technology, it is also necessary to develop a personalized music recommendation system to recommend music to users. Emotions are an essential part of the human psyche. It has many different types, and the main purpose of our proposed system is to distinguish them. For this, our proposed system is based on the proposed structure of video content that is related to human emotions. Recommendation system based on user logs or history. the recommendation technique we use is not fully personalized nor is it considered a point of user content. therefore, the solution to this problem is to recommend personalized content based on emotional characteristics. According to letter of our proposed system, taking into account aspects such as age, gender and emotional characteristics, our main motivation is to control the problem of accuracy in applications running. Our proposed emotion-based media recommendation system is a novel and innovative approach that has the potential to revolutionize the way we interact with digital media.

关键词： Emotion recognition Portable computers Webcams Face recognition Personal voice assistants Nose Machine learning Media Feature extraction Recommender systems

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Digital Twin Driven Blockchain Based Reliable and Efficient 6G Edge Network

Digital Twin Driven Blockchain Based Reliable and Efficient ...

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international conference on Distributed computing in Sensor systems (DCOSS)

作者： Mehmet Ozgen Ozdogan Levent Carkacioglu Berk Canberk Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey Aselsan Inc. Ankara Turkey

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

With the rapid development of intelligent devices in wireless communication, the fifth generation (5G) mobile networks have limited high data rates, low latency, high avail-ability demands. the sixth-generation (6G) mobile network can use Digital-twin (DT) techniques to meet these demands. DT is the virtual representation of physical aspects such as 6G edge nodes. DT optimize the 6G edge nodes parameters using artificial intelligence (AI) and especially machine learning (ML) algorithms. However, AI and ML bring along privacy and security concerns. therefore, user data must be protected from unauthorized persons during the 6G edge network recovery and expansion phases. In this paper, we proposed a new reliable Digital Twin-based 6G edge network recovery framework using Blockchain technology. We applied the Transfer Learning (TL) technique to improve our proposed framework’s performance. We ensured data privacy and security using TL and Blockchain.

关键词： 6G mobile communication Wireless communication Privacy Machine learning algorithms Transfer learning Sensor systems Blockchains

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A Real-Time IoT Cryptograph Communication of Chaotic-ADPCM Coding Method for IoT Applications

A Real-Time IoT Cryptograph Communication of Chaotic-ADPCM C...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Khalid ismail Iman R. I. Boby Md. Sazib Mollik Imran Chowdhury Sahidur Rahman Wobeydul Haq Ankur Khaizuran Abdullah Department of Computer Science and Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Electrical & Electronic Engineering (EEE) Northern University Bangladesh Dhaka Bangladesh Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Business, education, and research have formed a strong connection with the Internet of things (IoT), an open-access collective communication service offered by Internet-connected devices. though it seems to be a good deal in terms of cost and functionality, it is still the most dangerous communication channel due to high hacking rates and the ease of high data loss. Even though contemporary software is becoming more sophisticated for data and security, it still needs greater protection in a real-time setting. Utilizing extensive security measures may increase safety, but doing so will jeopardize the real-time environment. this study introduces a highly efficient encryption framework that combines chaotic maps with the Adaptive Differential Pulse Code Modulation (ADPCM) coding technique. the proposed algorithm ensures real-time secure communication for Internet of things (IoT) applications while complying with ITU-T Recommendation Point G.726 and ANSI T1.303-1989 standards. It supports ADPCM transcoding algorithms at connection speeds of 16, 24, 32, and 40 kbps. the chaotic map technique was selected for its robust encryption capabilities using random variables and ADPCM for its high energy economy in transcoding and is characterized using two parallel encode functions per frame, which may be executed by small memory. the proposed encryption algorithm will perform three main operations. Firstly, it will be able to generate chaotic values from distinct initial conditions using two chaotic logistic maps. Secondly, it will transform these values into binary tables using a random encoding condition. Finally, it will execute the basic proposed ADPCM process to enhance security further.

关键词： Chaotic communication Transcoding Transforms Real-time systems Software Encryption Random variables

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2019 6th international conference on systems and Informatics

2019 6th International Conference on Systems and Informatics

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international conference on systems and Informatics (ICSAI)

Presents the front cover or splash screen of the proceedings record.

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

Presents the front cover or splash screen of the proceedings record.

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11th international conference on Multimedia and Network Information systems, MISSI 2018

11th International Conference on Multimedia and Network Info...

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11th international conference on Multimedia and Network Information systems, MISSI 2018

ISBN: (纸本)9783319986777

the proceedings contain 58 papers. the special focus in this conference is on Multimedia and Network Information systems. the topics include: Comparative analysis of accuracy of fuzzy clustering methods applied for image processing;an efficient method for sign language recognition from image using convolutional neural network;Audio/speech coding based on the perceptual sparse representation of the signal with DAE neural network quantizer and near-end listening enhancement;the features and functions of an effective multimedia information retrieval system (MMIR);youTube timed metadata enrichment using a collaborative approach;improving the responsiveness of geospatial web applications through client-side processing;isotone galois connections and generalized one-sided concept lattices;Transfer learning in GMDH-type neural networks;modelling of taxi dispatch problem using heuristic algorithms;modern approaches to multi-dimensional visual signals analysis;malware detection using black-box neural method;intrusion detection and risk evaluation in online transactions using partitioning methods;Prediction of autism severity level in Bangladesh using fuzzy logic: FIS and ANFIS;a framework for analyzing academic data;verifying usefulness of algorithms for WordNet based similarity sense disambiguation;comparison of paragram and glove results for similarity benchmarks;calculating optimal queries from the query relevance file;survey on neural machine translation into Polish;Multilingual and intercultural competence for ICT: Accessing and assessing electronic information in the global world;selection of most suitable secondary school alternative by multi-criteria fuzzy analytic hierarchy process;new applications of sound and vision engineering;comparison of the novel classification methods on the reuters-21578 corpus;sparse solution to inverse problem of nonlinear dimensionality reduction.

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thE IMPACT OF thE DIGITAL ECONOMY ON RENEWABLE ENERGY CONSUMPTION AND GENERATION: EVIDENCE FROM EUROPEAN UNION COUNTRIES 22

THE IMPACT OF THE DIGITAL ECONOMY ON RENEWABLE ENERGY CONSUM...

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proceedings of the 6th international conference on Future Networks & Distributed systems

作者： Gavkhar Karimovna Sultanova Rano Abdullaevna Djuraeva Suriya Telmanovna Turaeva Department of International Economics University of World Economy and Diplomacy Uzbekistan

ISBN: (纸本)9781450399050

the article aims to provide an empirical investigation of the relationship between the digital economy and renewable energy consumption and generation based on a panel data analysis of 25 European Union (EU) countries for the period 2017–2021. the results indicate that the growth of the digital economy positively influences the transition to clean energy. At the same time, the control variables do not behave uniformly. thus, urbanization has the strongest positive effect on the transition to renewable energy sources (RES). Carbon dioxide emissions also have a positive effect on renewable energy consumption and generation. However, an increase in primary energy consumption per capita leads to a decrease in the production and consumption of RES. thus, while there is a positive relationship between the digital economy and the transition to RES, there is an ambiguous impact of urbanization, carbon emissions, and primary energy consumption on green energy in the EU.

关键词： CO2 emissions government effectiveness index urbanization renewable energy generation renewable energy consumption digital economy primary energy consumption

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Evaluation of KNN-NMF Algorithm for Recommendation systems in E-Commerce

Evaluation of KNN-NMF Algorithm for Recommendation Systems i...

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IEEE conference on systems, Process & Control (ICSPC)

作者： Hew Sok Yen Samuel-Soma M. Ajibade Muhammed Basheer Jasser Bayan Issa Yoke Leng Yong Faizah Mohammed Bashir Emmanuel Falude Yakubu Aminu Dodo Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) School of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia Faculty of Informatics Engineering University of Aleppo Syria School of Information and Communication Technology HELP University Persiaran Cakerawala Subang Bestari Seksyen Shah Alam Malaysia Department of Decoration and Interior Design Engineering College of Engineering University of Hail Hail Kingdom of Saudi Arabia Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur Malaysia Architectural Engineering Department College of Engineering Najran University Najran Kingdom of Saudi Arabia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Due to growth in e-commerce, recommender systems have gained importance in providing recommendations to the user to help him or her make a choice. these systems not only assist the users in searching through the extensive product list but also increase businesses’ productivity by recommending products. the aim of this project is to suggest a hybrid recommender system which combines the aspects of collaborative and content-based filtering so as to enhance the efficiency of the recommendation system in e-commerce. In addition, it is to evaluate the performance of this model with the individual collaborative and content-based models. the study involved creating the CF and CBF models with the various algorithms like the Matrix Factorization, KNN, SVD, and the TF-IDF vectorization. the best models from CF and CBF were then combined with a Hybrid Filtering (HF) model by using weighted and switching combination to enhance the model’s effectiveness. the performance of the CF, CBF and the HF models was compared, in order to check if the proposed hybrid approach is better than the existing models. the results of the experiment show that the hybrid switching model provided better results than the CF and CBF models on their own. the hybrid filtering model had also managed to reduce the RMSE to $\mathbf{1. 0 6 5}$ and improved precision to 83.2%. this demonstrates the advantage of the proposed model over the traditional CF and CBF approaches. Our study validates that the hybrid filtering recommender system, especially the switching combination strategy, outperformed the traditional standalone models by integrating the benefits of both collaborative and content-based filtering. this provides a good starting point for future work and there are many directions in which the research can be expanded.

关键词： Productivity Machine learning algorithms Filtering Collaboration Switches Hafnium Nearest neighbor methods Electronic commerce User preference Recommender systems

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