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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： El Bakri, Ahmad Yehia, Abdullah Ali, Mariam Osama, Reem Adel, Zeyad Gamal, Othman Saleh, Sherine Nagy Arab Academy for Science Technology and Maritime Transport Computer Engineering Department Alexandria Egypt

ISBN: (纸本)9798350378511

The Internet is now a vital part of our daily life including young children who watch their favorite cartoons using different streaming platforms. Unfortunately, these platforms often host content deemed inappropriate for children due to limited regulations. Manually reviewing the vast content is impossible, leaving parents with limited options to protect their children. Since parents in the Arabic culture have different beliefs on the content their children should be exposed to, and cannot rely on generic Kids' filters, accordingly 'The Eye' is proposed. The Eye is a streaming platform that allows children to watch videos while providing parents full flexibility to choose the exact content flags they deem inappropriate, ensuring their children enjoy a safe and age-suitable entertainment environment. The proposed model presents a hybrid CNN-RNN model to classify video content based on the predefined flags. A dataset was meticulously collected serving the different flags needed to train the proposed model and accordingly, the proposed trained model achieved an accuracy of approximately 67% on the test data. The model is deployed as a cloud-based service utilizing AWS infrastructure for efficient video processing and classification. © 2024 IEEE.

关键词： Video streaming

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Secure Student Record Management using Visual Cryptography in Educational Systems 9

Secure Student Record Management using Visual Cryptography i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Kiranchandu, E. Gireesh, C. Kiran, G. Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

Cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, have considerable computational burden and energy requirements owing to their extensive key sizes. This study examines the efficacy of Elliptic Curve Cryptography (ECC) as a superior solution for data security in cloud environments. The suggested method utilizes ECC's reduced key sizes to attain improved computational efficiency and energy saving, all while upholding rigorous encryption standards. A comparison research indicates that ECC surpasses RSA in performance and resource efficiency, rendering it especially appropriate for extensive cloud systems handling significant amounts of sensitive information. This research examines the implementation of the Advanced Encryption Standard (AES) algorithm in Cipher Block Chaining (CBC) mode for image encryption. The AES-CBC technique efficiently safeguards images by transforming them into encrypted formats, so obstructing illegal access. Comparative assessments using alternative encryption methods highlight AES's enhanced security and computational efficacy. Collectively, these findings underscore the capability of ECC and AES to overcome the shortcomings of conventional encryption, providing secure and resource-efficient solutions for contemporary cloud computing issues. © 2024 IEEE.

关键词： Cloud security

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Highly efficient ternary organic solar cells with excellent open-circuit voltage and fill factor via precisely tuning molecular stacking and morphology

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science China Chemistry 2024年第3期67卷 963-972页

作者： Dinghong Sun Zhenyu Chen Jianfeng Zhang Wei Song Jingyu Shi Jintao Zhu Yuanyuan Meng Fei Jin Shuncheng Yang Ziyi Ge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Faculty of Material Science and Chemical Engineering Ningbo UniversityNingbo 315211China Zhejiang Provincial Engineering Research Center of Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology&EngineeringChinese Academy of SciencesNingbo 315201China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049China Department of Chemical and Environmental Engineering University of Nottingham NingboNingbo 315100China

The micro-morphology and molecular stacking play a key role in determining the charge transport process and nonradiative energy loss, thus impacting the performances of organic solar cells(OSCs). To address this issue, a non-fullerene acceptor PhC6-IC-F with alkylbenzene side-chain, possessing optimized molecular stacking, complementary absorption spectra and forming a cascade energy level alignment in the PM6:BTP-eC9 blend, is introduced as guest acceptor to improve efficiency of ternary OSCs. The bulky phenyl in the side-chain can regulate crystallinity and optimizing phase separation between receptors in ternary blend films, resulting in the optimal phase separations in the ternary films. As a result, high efficiencies of 18.33% as photovoltaic layer are obtained for PhC6-IC-F-based ternary devices with excellent fill factor(FF) of 78.92%. Impressively, the ternary system produces a significantly improved open circuit voltage(V_(oc)) of 0.857 V compared with the binary device,contributing to the reduced density of trap states and suppressed non-radiative recombination result in lower energy loss. This work demonstrates an effective approach for adjusting the aggregation, molecular packing and fine phase separation morphology to increase V_(oc) and FF, paving the way toward high-efficiency OSCs.

关键词： alkylbenzene side-chain morphology regulation ternary blends organic solar cells

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Based on Transfer Learning a Performance Analysis and Comparative Study of CNN for Accurate Plant Disease Detection

Based on Transfer Learning a Performance Analysis and Compar...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Nithin, S. Nishanth, M.S. Kodipalli, Ashwini Rao, Trupthi Kumaraswamy, S. Global Academy of Technology Dept of Computer Science and Engineering Banglore India Global Academy of Technology Dept of Artificial Intelligence and Data Science Banglore India Global Academy of Technology Department of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350300826

This paper investigates the use of Convolutional Neural Networks (CNNs) for plant disease diagnosis, utilizing the notion of transfer learning to develop a deep CNN network at a low cost. The dataset utilized includes 30,000 pictures from 18 distinct categories. Several architectural models, including ResNet50, ResNet50V2, VGG16, VGG19, EfficientNetB0, MobileNet, MobileNetV2, NASNetMobile, and DenseNet, were created using transfer learning methodologies. The models are trained on the target plant disease dataset while keeping the features learnt from large-scale image datasets like ImageNet. Performance criteria such as accuracy, precision, and recall are used to assess the models' accuracy in detecting various plant diseasesx. Furthermore, the research investigates the effect of dropout on accuracy and other model parameters in the context of transfer learning. Finally, the outcomes of a tailored model with and without batch normalization are compared. © 2023 IEEE.

关键词： Convolutional neural networks

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Cryptocurrency Trading Bot with Sentimental Analysis and Backtracking Using Predictive ML 10th

Cryptocurrency Trading Bot with Sentimental Analysis and Bac...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Srinivas Murthy, Abhishek Akshay, A. Hanji, Bhagyashri R. Computer Science and Engineering Global Academy of Technology Bangalore India

ISBN: (纸本)9789811974540

Algorithmic trading is a process of converting a trading strategy into computer code which buys and sells the shares or performs trades in an automated, fast, and accurate way. Sentiment analysis is a powerful social media tool that enables us to understand its users. It is an important factor because emotions and attitudes toward a topic can become actionable pieces of information useful in understanding market trends, saving time and effort by the means of automation. Bringing together the art of sentimental analysis of social media and backtracking of historical price data, the paper, coupled with state-of-the-art APIs from leading crypto exchanges, is set to predict the best options and place trade orders, taking into account variables set by the user such as STOP LOSS and risk profiles. Combining the historical data from Binance APIs, coupled with sentimental analysis of Twitter tweets, our work aims at delivering highly accurate trade orders and executing them in real time without any human intervention. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Commerce

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A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

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computer Modeling in engineering & sciences 2023年第10期137卷 363-383页

作者： Yanjun Zhang Yongqiang He Jingbo Zhang Yaru Zhao Zhihua Cui Wensheng Zhang School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China Department of Big Data and Intelligent Engineering Shanxi Institute of TechnologyYangquan045000China School of Computer Science Beijing University of TechnologyBeijing100124China The Institute of Automation Chinese Academy of Sciences(CAS)Beijing100049China

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited ***,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging *** resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction *** includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood ***,the OPBS consists of two parts:the selection of blocks and the optimization of prediction *** combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video *** addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on ***,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as *** maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current *** proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction *** results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

关键词： Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm

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A Double Layer Encryption for Communication using Cryptographic Algorithms 2

A Double Layer Encryption for Communication using Cryptograp...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Ragavan, K. Mohankumar, B. Vivekrabinson, K. Arun, R. Arumuga School of Computer Science and Engineering Vellore Institute of Technology Vellore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798350328202

Cryptography is a fundamental field in computer science that focuses on securing communication and protecting sensitive information from unauthorized access. It involves the use of mathematical algorithms and techniques to transform plaintext into ciphertext, rendering it unintelligible to anyone without the proper decryption key. Cryptographic systems play a crucial role in ensuring confidentiality, integrity, and authenticity of data in various domains, including finance, healthcare, e-commerce, and communication networks. In this paper, a double-layer encryption method is proposed that uses the Vigenere and Polybius ciphers to secure communication. The Vigenere cipher is a classical method that uses a keyword to encrypt plaintext, while the Polybius cipher is another classical cipher that uses a grid to map letters to two-digit numbers to encrypt data. By combining these two ciphers, the proposed algorithm aims to enhance the security of the data by introducing a more complex and secure encryption method. The proposed method is implemented using Python programming language with web implementation. The web implementation of the algorithm uses Flask, a Python web framework. This allows users to encrypt and decrypt their data through a user-friendly interface, making it more accessible and practical for real-world use. Based on the obtained results demonstrate that the proposed method provides a higher level of security compared to using each cipher individually, without significantly impacting the encryption and decryption speed. Overall, the proposed double-layer encryption method can be a useful tool for secure communication in various applications. It contributes to the field of cryptography by introducing a novel approach that enhances data security without compromising usability. © 2024 IEEE.

关键词： Cryptography

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Isolation Forest based Fraud Detection for Cryptocurrency Transaction

Isolation Forest based Fraud Detection for Cryptocurrency Tr...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Balakrishnan, D. Mariappan, Umasree Seerla, Sharan Sagar Tej, Pyrapu Varun Vani, Yerroju Eswar Fazi, Shaik Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil India Sri Vidya College of Engineering and Technology Tamil Nadu Virudhunagar India

ISBN: (纸本)9798350315455

The growing popularity of cryptocurrencies has brought attention to the need for effective fraud detection mechanisms within this dynamic and rapidly evolving domain. This research explores the application of the Isolation Forest algorithm for detecting fraudulent transactions within cryptocurrency networks. The Isolation Forest algorithm is particularly well-suited for identifying anomalies in datasets characterized by imbalanced classes or rare instances, making it an ideal candidate for detecting infrequent fraudulent activities in cryptocurrency transactions. The proposed methodology involves preparing a dataset of cryptocurrency transactions, including features such as transaction amounts, timestamps, and wallet addresses. By labelling the data with known legitimate and fraudulent transactions, the Isolation Forest model is trained to recognize patterns that differentiate between the two classes. During training, the algorithm constructs a forest of isolation trees, and each transaction's anomaly score is determined based on its average path length through these trees. A critical step involves setting a suitable threshold above which a transaction is deemed anomalous. This threshold is determined through rigorous experimentation and validation to strike a balance between minimizing false positives and capturing actual fraud cases. The trained Isolation Forest model is deployed in a real-time system, capable of processing incoming cryptocurrency transactions and promptly flagging potential instances of fraud. Performance evaluation of the model is conducted using relevant metrics such as precision, recall, and F1-score, on a separate validation or test dataset from which it is proved that the proposed methodology attains 6% performance improvement than the existing methodology. © 2023 IEEE.

关键词： Cryptocurrency flagging Fraud detection Isolation Forest threshold Timestamps wallet address

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A Time Window Sequence-Based Evolutionary Algorithm for Solving Large-Scale Daily Task Planning Problems 13

A Time Window Sequence-Based Evolutionary Algorithm for Solv...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Peng Shao, Shuai Technology and Engineering Center for Space Utilization Chinese Academy of Science China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350308365

The daily task planning problem (DTPP) in the ground control of satellites has been a significant focus for space scientists. Although various optimization methods have been devoted to solving this problem, most of them can only deal with simple DTPPs with a few variables and naive objectives, which cannot obtain multiple optimal solutions that meet the complex requirements of real-world scenarios. To address this issue, this paper formulates DTPPs into a large-scale multi-objective optimization problem and solves it using an evolutionary algorithm embedded with heuristic strategies. The proposed algorithm can quickly approach the optimal solution of DTPPs and can be seamlessly embedded into various evolutionary algorithms. In order to evaluate the performance of the proposed algorithm, statistical experiments are carried out on the DTPP with nine real-world datasets. The experimental results demonstrate that the proposed algorithm has significant advantages over several representative evolutionary algorithms. © 2024 IEEE.

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Speak to your Database: Streamlining Human-to-Database Communication with NLP 1

Speak to your Database: Streamlining Human-to-Database Commu...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Sthuthi, B. Sivani, J.C. Sathyalakshmi, S. Kumar Yadav, Kamleshwar Global Academy of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350339727

The paper demonstrates the successful integration of recent enhancements in Natural Language Processing (NLP) with the goal of generating accurate and meaningful Structured Query Language (SQL) queries from human language. The focus is on the methods that we have implemented in the project. The methodology we propose in this paper relies entirely on NLP techniques. Our system accepts user queries in English, which are then transformed into SQL statements using our NLP-based approach, before being executed against the database to retrieve the desired results. The student database was used in our project, as it provides a real-world example of how our NLP-based approach can be applied to retrieve data from educational institutions. The paper also outlines the various challenges and limitations faced by our system when converting human language into SQL. The paper concludes by discussing the broader implications of our approach for the field of database querying, and how it could lead to the development of more intuitive and user-friendly interfaces for interacting with databases and outlines potential directions for future improvements. © 2023 IEEE.

关键词： Query languages

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