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Key Analysis of Integer Factorizing based Public-Key Cryptosystems Using Machine Learning 23

Key Analysis of Integer Factorizing based Public-Key Cryptos...

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Proceedings of the 2023 7th international conference on advances in Artificial Intelligence

作者： Mohammad Ahmad Alia Yousef Jaradat Abdul Razzak Alshehadeh Faculty of Science and IT Al-Zaytoonah University of Jordan Jordan Faculty of Engineering and Technology Al-Zaytoonah University of Jordan Jordan Faculty of Business Al-Zaytoonah University of Jordan Jordan

ISBN: (纸本)9798400708985

this paper discusses the possibilities of using machine learning models to analyze the keys in public key encryption algorithms that are based on various mathematical hard problems. Given their distinct domains within computer science, machine learning and public key cryptosystems may appear unrelated at first glance. Nonetheless, there are intriguing correlations and potential points of convergence between these two domains. In general, machine learning models have the potential to help with differential cryptanalysis, a technique that uses statistical differences between plaintext-ciphertext pairs to deduce parts of the key. As a result, breaking encryption keys using machine learning is a complex and evolving research area that investigates cryptanalysis machine learning techniques. While public key encryption algorithms were designed to be secure against various attacks such as brute force, advances in computing power and machine learning have raised concerns about the potential emergence of new attack vectors.

关键词： Cryptology Key Analysis Machine learning NP-Hard Problem Public key Encryption

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Deep Learning Approaches for Solving Challenges in computer Vision Applications and Exploring Evolving Patterns

Deep Learning Approaches for Solving Challenges in Computer ...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Sujay Gejji B. R. Supreeth Pathan Firoze Khan S. Venkat K Pradeepa S. Kaliappan Department of Electronics & Communication Engineering S. G. Balekundri Institute of Technology Belagavi Karnataka India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore Department of Computer Science & Technology Madanapalle institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. the results of the study show significant improvements: the accuracy of Enhanced CNNs was $81.6 \%$, the accuracy of GANs was $14.8 \%$, the accuracy of Hybrid CNN-RNN models was $86.2 \%$, the accuracy of ViTs was $79.2 \%$, and the accuracy of transfer for self-supervised learning techniques was $76.8 \%$. these findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. these advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality.

关键词： computer vision Visualization Accuracy Computational modeling Surveillance Self-supervised learning Transformers Generative adversarial networks Object recognition Image classification

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Designing XR into Higher Education using Immersive Learning Environments (ILEs) and Hybrid Education for Innovation in HEIs to attract UN's Education for Sustainable Development (ESD) Initiative 7

Designing XR into Higher Education using Immersive Learning ...

引用

7th IEEE international conference on advances in Computing, Communication and Control, ICAC3 2021

作者： Alam, Ashraf Indian Institute of Technology Kharagpur Rekhi Centre of Excellence for the Science of Happiness Kharagpur India

ISBN: (纸本)9781665426343

Numerous studies have shown the efficacy of incorporating extended reality (XR) into higher education. In light of Covid-19 pandemic and the need to improve the virtual presence of remote students and workers, university authorities prioritize the integration of different XR alternatives into education. To choose the most appropriate virtual reality (VR) platforms and apps, instructional designers (IDs) must possess analytical, design, implementation, and evaluation skills. this article shows how unique identifiers may be used to assist in the creation of high-level resources. On the one hand, we offer recommendations for well-established design models, design approaches, and methodologies, and on the other hand, we suggest templates for research and funding leadership, as well as for evaluating XR for optimum usability in higher educational institution's applications. © 2021 IEEE.

关键词： computer aided instruction

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Reverse Engineering-A Method for Analyzing Malicious Code Behavior 7

Reverse Engineering-A Method for Analyzing Malicious Code Be...

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7th IEEE international conference on advances in Computing, Communication and Control, ICAC3 2021

作者： Bhardwaj, Vivek Kukreja, Vinay Sharma, Chetan Kansal, Isha Popali, Renu Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Chitkara University Himachal Pradesh India

ISBN: (纸本)9781665426343

Millions of dangerous programs are encountered in everyday life as a result of increasing computer science advances. Malware is any harmful code that affects hardware, software, a user, or a network. Various malware analysis methods assume that some of the malware's broken code is accessible, which is not necessarily the case. Malware-based cyber-Attacks are reported on a daily basis by security specialists all around the world. Malware analysis is the process of cleaning target PCs and devices to remove malware's destructive capability. Software reverse engineering is the process of assessing a software system in its entirety or in part in order to extract design, knowledge, code, and implementation information about the program without having to look at the source code. In this research paper, we present the results of malware analysis on infected binary files using static and dynamic analysis. © 2021 IEEE.

关键词： Reverse engineering

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Using a Hybrid-Domain Attention Mechanism with YOLOv8 for Victim Detection in Disasters

Using a Hybrid-Domain Attention Mechanism with YOLOv8 for Vi...

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computer Information science and Application technology (CISAT), international conference on

作者： Ali Ahmed Huan Wang Hashem Almadwami Amira Abo Kaf Mohammed Al-Soswa School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China School of Information Engineering Chang’an University Xi’an Shaanxi China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

the disaster response and management domain constitute a formidable challenge to resilience and technological innovation. Prompt identification and localization of victims are paramount for search and rescue teams during emergencies. However, the intricacy of disaster scenes, often obscured by haze and cluttered backgrounds, exacerbates the difficulty in distinguishing victims. this paper introduces an algorithm to detect and locate victims within affected areas. Leveraging the acclaimed YOLOv8 model, renowned for its exceptional speed and accuracy, this study advances the one-stage detector, subsequently termed DSP-YOLOv8, through enhancements tailored to bolster detection precision. By integrating DCNv2, SE, and SPDConv modules into YOLOv8, optimizing feature capture for complex shapes and establishing adaptive spatial and channel dependencies, thus enhancing detection across various scales and occlusion levels. the model was trained and tested on a dataset comprising 5100 images of victims. the efficacy of the developed model was assessed by comparative evaluations Faster R-CNN, SSD, YOLOv5, YOLOv6 and YOLOv8. the resultant model achieved a high accuracy rate of 95.11 percent. Performance evaluation during training and testing phases was conducted based on established metrics, including mean Average Precision (mAP), precision (P), recall (R), and Precision-Recall (PR) curves. the findings from our testing regimen underscore the proposed catastrophe victim identification technique’s efficacy, particularly in highly cluttered scenarios.

关键词： YOLO Training Performance evaluation Adaptation models Technological innovation Accuracy Shape Disasters Testing Resilience

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IoT Applications in Wildlife Conservation: Tracking and Protecting Endangered Species

IoT Applications in Wildlife Conservation: Tracking and Prot...

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international conference of Electronics, Communication and Aerospace technology (ICECA)

作者： R.Reena Roy Ramakrishnan Raman M. Amanullah Vijay Kumar Pandey E.N. Ganesh School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Symbiosis Law School NOIDA Symbiosis International (Deemed University) Pune Maharashtra India Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Chennai Tamil Nadu India

Human activity degrades natural environments, and wildlife protection is crucial. Recent technological advances have improved animal monitoring and preservation activities. this research provides a real-time wildlife monitoring and analysis system using the IoT (IoT) and machine learning, specifically the Random Forest algorithm. the proposed method uses a network of IoT devices, including motion sensors and cameras. In real-time, these sensors generate massive volumes of diverse data regarding animal movement, behavior, and environmental variables. the Random Forest technique manages this data flow and gets significant insights since it can handle high-dimensional datasets and missing values. A large dataset of animal activity, environmental, and habitat characteristics is used to train the machine learning model. the Random Forest model outperforms existing classification algorithms in a rigorous assessment procedure, allowing exact species identification. Wildlife conservationists and academics may utilize the system's user-friendly interface to engage with data and see real-time analytics. this interface helps animal population protection by supporting decision-making. It shows the usefulness of the suggested IoT-based wildlife monitoring system and Random Forest algorithm in real-time species recognition and behavior analysis. technology and advanced machine learning may boost conservation efforts and ensure a sustainable coexistence between people and animals in their natural environments.

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Should Robots Replace Teachers? Mobilisation of AI and Learning Analytics in Education 7

Should Robots Replace Teachers? Mobilisation of AI and Learn...

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7th IEEE international conference on advances in Computing, Communication and Control, ICAC3 2021

作者： Alam, Ashraf Indian Institute of Technology Rekhi Centre of Excellence for the Science of Happiness Kharagpur India

ISBN: (纸本)9781665426343

the purpose of this study was to determine how artificial intelligence (AI) impacts education. the study's focus was restricted to the use and impact of artificial intelligence in administration, education, and learning, and was founded on a narrative and framework created via exploratory research for assessing AI. the use of literature review as a research strategy and methodology helped in accomplishment of study's goal, which was achieved making use of a qualitative research technique. AI is an area of research in which computers, robots, and other technologies are programmed to exhibit human-like intelligence, as characterised by cognitive skills such as learning and adaptation, as well as decision-making capabilities. According to the findings, AI has been widely embraced and used in a number of ways by organisations, especially educational institutions, in a range of circumstances and settings. AI began with computer technology, progressed to intelligent online and web-based education, and finally, embedded computer systems were used to perform instructional tasks autonomously or collaboratively with teachers, in conjunction with other technologies such as humanoid robots and web-based chatbots. these platforms have allowed instructors to improve the overall quality of their instructional work while also improving their efficiency and speed for administrative duties such as evaluating students' work. However, since the systems are based on machine learning and adaptability, curriculum and materials can be customised to meet the unique requirements of individual students, resulting in improved absorption and retention and a more pleasant overall learning experience. © 2021 IEEE.

关键词： E-learning

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Planning of Electric Vehicle Charging Stations for Four Wheeled Passenger Vehicles in Guwahati: An Approach through Genetic Algorithm

Planning of Electric Vehicle Charging Stations for Four Whee...

引用

international conference on computer Applications In Electrical Engineering-Recent advances (CERA)

作者： Siddhartha Harsh Chaturvedi School of Energy Science & Engineering Indian Institute of Technology Guwahati India Department of Electrical & Electronics Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal India

ISBN: (数字)9798350305005

ISBN: (纸本)9798350305012

Strategically designing and placing electric car charging stations helps urban customers accept electric vehicles as their main form of transportation. However, policy economics, charging demand, user comfort, and road traffic circumstances affect the position and setup of these electric car charging stations. this study presents a charging station placement and capacity optimization methodology. the goal is to increase operator income and reduce user billing costs. In addition, the road time-consuming index is used to analyze the effect of road congestion on the user’s extra charging expenditures, improving user satisfaction. A modified non-dominated sorting genetic algorithm (NSGA-II) is used to construct charging stations with varied constraints in this study. the paper identifies Guwahati’s urban road network charging station locations and sizes for 2030 electric car charging needs.

关键词： Roads Evolutionary computation Charging stations Pareto optimization Electric vehicle charging Vectors Automobiles

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Towards Cross-Modal Point Cloud Retrieval for Indoor Scenes 30th

Towards Cross-Modal Point Cloud Retrieval for Indoor Scenes

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30th international conference on MultiMedia Modeling, MMM 2024

作者： Yu, Fuyang Wang, Zhen Li, Dongyuan Zhu, Peide Liang, Xiaohui Wang, Xiaochuan Okumura, Manabu Beihang University Beijing China Tokyo Institute of Technology Tokyo Japan Delft University of Technology Delft Netherlands Beijing Technology and Business University Beijing China

ISBN: (纸本)9783031533013

Cross-modal retrieval, as an important emerging foundational information retrieval task, benefits from recent advances in multimodal technologies. However, current cross-modal retrieval methods mainly focus on the interaction between textual information and 2D images, lacking research on 3D data, especially point clouds at scene level, despite the increasing role point clouds play in daily life. therefore, in this paper, we proposed a cross-modal point cloud retrieval benchmark that focuses on using text or images to retrieve point clouds of indoor scenes. Given the high cost of obtaining point cloud compared to text and images, we first designed a pipeline to automatically generate a large number of indoor scenes and their corresponding scene graphs. Based on this pipeline, we collected a balanced dataset called CRISP, which contains 10K point cloud scenes along with their corresponding scene images and descriptions. We then used state-of-the-art models to design baseline methods on CRISP. Our experiments demonstrated that point cloud retrieval accuracy is much lower than cross-modal retrieval of 2D images, especially for textual queries. Furthermore, we proposed ModalBlender, a tri-modal framework which can greatly improve the Text-PointCloud retrieval performance. through extensive experiments, CRISP proved to be a valuable dataset and worth researching. (Dataset can be downloaded at https://***/CRISPdataset/CRISP.) © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Pipelines

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Code Mixed Information Retrieval for Gujarati Script News Articles 7th

Code Mixed Information Retrieval for Gujarati Script News Ar...

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Joshi, Payal Joshi, Dhaval Department of Information and Communication Technology Veer Narmad South Gujarat University Gujarat Surat India

ISBN: (纸本)9783031379390

Large amount of digitized content is being created and made available online in many Indian languages. Searching from such content is difficult because of lack of regional language input tools and interface. Further documents may also contain commonly used English words written using Indian script which we refer as code mixed content. One of the easier methods of query input could be typing query’s words using Roman script mixing words of Indian language and English in single script which we refer as code mixed queries. For various Indian languages work is done on single language (code) transliterated search but code mixed information retrieval is very less explored area. Gujarati is an Indian regional language spoken by 55 million people in the world, but very less work is done for code mixed information retrieval and it lacks transliteration pair corpus. To search code mixed contents we need to identifying language of a word and we have to back transliterate it from Roman to Indian language script. Moreover certain words belong to both the languages. All these issues are major challenges in code mixed IR especially for Indian languages. A novel hybrid approach is proposed in this paper for Gujarati words transliteration which combines the use of Roman script Gujarati Soundex based match with alternative use of machine learning based improved CRF with post processing. A huge transliteration pair corpus of Gujarati script is created for words of both languages. Analysis of approaches for language identification and transliteration is presented in this paper along with compared retrieval result for different transliteration approaches and obtained an overall increase of 17% in MAP as compared to other approaches. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

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