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14th International Conference on Information, Intelligence, Systems and applications, IISA 2023

作者： Chroni, Maria Nikolopoulos, Stavros D. Polenakis, Iosif Vouronikos, Vasileios University of Ioannina Dept. of Computer Science & Engineering Ioannina Greece

ISBN: (纸本)9798350318067

In this work we present watermarking technique for the preservation of authenticity property of forensic images. The proposed technique has its basis on the utilization of watermarks by the embedding of Self-Inverting Permutations by their 2D representation in the frequency domain of forensic images. We propose the embedding of multiple integers through a code-sequence structure, where the robustness and the imperceptibility of the proposed technique has been attested and particular settings on their application e.g. the lower their imperceptibility the higher the fragility of a watermark is, cooperate on the development of the watermarking technique that can be utilized for the originality of the image. The effectiveness of proposed technique is validated throughout an experimental evaluation deploying various attacks, while the achieved results prove the potentials of the proposed model in the preservation of forensic Image authenticity. © 2023 IEEE.

关键词： Embeddings

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Machine Learning Based Prediction of Psychiatric Disorder 4

Machine Learning Based Prediction of Psychiatric Disorder

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sharma, Mayank Goswami, Puneet SRM University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350306118

The foundation of the education systems all around the world were shaken up when Covid-19 started spreading its roots worldwide. India of course was no exception to it. While the education sector in India was already undergoing a transformation with the NEP 2020 policy, all the schools and colleges had to be shut down urgently due to the rapid spread of Covid-19. Almost overnight students and teachers had to switch to online learning. At the onset of lockdown around 250 million students were affected due to closure of educational institutes in India. While there was a massive decline in learning outcomes, the physical and mental well-being of the students was also severely impacted. Students lost touch with being socially physical and alternated to being present on social media. However humans are social animals. With deprivation, came a sense of loneliness and that's when physical and mental wellness issues started taking over leading to psychiatric disorders. Our generation are the leaders of the next and ensuring the wellbeing of young minds is a must. Thus there is a need to study and find out just how much Covid-19 is impacting our students and their lives using Artificial Intelligence cum Machine Learning and furthermore act upon it to find solutions to help them. In this paper, we have approached the above problem using the Binary Classification Predictive Model with different logical neural networks. © 2023 IEEE.

关键词： Students

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Empirical Validation of Fuzzy Logic Based Predictive Load Scheduling in Mimic Home Energy Management System

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Journal of Internet Technology 2023年第7期24卷 1429-1436页

作者： Jegadeesan, Nirmala Balasubramanian, G. Hemavathi, N. Chen, Yu-Chi School of Electrical and Electronics Engineering SASTRA Deemed University India Dept. Computer Science and Information Engineering National Taipei University of Technology Taiwan

Load scheduling plays a vital role in the home energy management systems. The main objective of this load scheduling is to balance the power demand and supply power without degrading the performance of the loads and consumers tolerance. Though many research works concentrate on load scheduling, very few works concentrated on real time scenario. On the other hand, research work concentrated on optimal load scheduling through fuzzy logic requires incorporation of fuzzy based system in either simulated or real-time home energy management system. Hence, the proposal aims to schedule the loads in a simulated home energy management system through fuzzy logic controller using MATLAB Simscape with required subsystems. The proposed simulated environment considers four different resistive loads. Intelligent scheduling is aimed to achieve efficient load scheduling. A fuzzy controller has three inputs namely integrated source, state of charge of battery, and power demand whereas probability of scheduling is considered as output. The efficiency of the proposed fuzzy-based load scheduling scheme is evaluated under various load conditions for different sub-systems with varying input power. The results exhibit the efficacy of the proposed scheme. © 2023 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Fuzzy logic

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Blockchain Unleashed: Empowering Information-centric Network Computing in Cloud, Fog, and Edge Services 14

Blockchain Unleashed: Empowering Information-centric Network...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Zambre, Pranav Panchal, Mitali Chouhan, Ankit Parul University Dept. of Computer Science and Engineering Vadodara India

ISBN: (纸本)9798350335095

Blockchain technology has been gaining a lot of attention due to its potential to improve data security, privacy, and decentralization. In this paper, we explore the use of blockchain in cloud, fog, and edge computing services. We analyze the unique characteristics of these services and how blockchain technology can enhance them. One of the benefits of blockchain in cloud computing is that it allows for secure and transparent data sharing among multiple parties. Blockchain can ensure the authenticity and integrity of data with smart contracts, which can automate processes and eliminate the need for intermediaries. This can result in cost savings and increased efficiency. In fog computing, blockchain can be used to enable secure and real-time data processing at the network edge. This can improve latency and reduce bandwidth consumption by processing data closer to the source. Additionally, blockchain-based fog computing can provide better data privacy and security for connected devices. Edge computing can benefit from blockchain by providing a trustless environment for data sharing and storage. Edge devices can act as nodes in a blockchain network, allowing for decentralized control and increased security. This can enable new use cases such as peer-to-peer transactions and autonomous decision making. Overall, blockchain has the potential to empower information-centric network computing in cloud, fog, and edge services by providing increased security, privacy, and efficiency. However, there are still challenges that need to be addressed such as scalability, interoperability, and governance. © 2023 IEEE.

关键词： Smart contract

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Active Learning Using Multifeature Kernel Sparse Representation Classifier for Hyperspectral Image Classification 3

Active Learning Using Multifeature Kernel Sparse Representat...

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3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

作者： Bortiew, Amos Patra, Swarnajyoti Tezpur University Dept. of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350325591

Active learning is a popular technique that mitigates the need for large training samples for hyperspectral image classification. The success of this technique is dependent on the query function designed to select informative samples for labelling. In the literature, most of the query functions are defined by exploiting spectral information. In this research, a novel query function for active learning is defined that considers both the spectral and spatial information of the hyperspectral image using multiple feature sets. The multiple feature sets provided complementary information for selecting those informative samples which are not only informative in spectral perspective but also from spatial perspective. Experiments conducted using two real hyperspectral datasets show that the proposed technique outperformed many state-of-the-art techniques. © 2023 IEEE.

关键词： Image classification

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Performance Evaluation of Machine Learning Algorithms for Lung Nodule Detection using Multi-Modal Imaging - A Hybrid Approach 2

Performance Evaluation of Machine Learning Algorithms for Lu...

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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Puneeth, R.P. Amogha Mayya, K.S. Shetty, Agnesh Rao, Akash Sooda, Aditya A. Dept. of Computer Science and Engineering Karnataka Nitte574110 India

ISBN: (纸本)9798350347579

Lung cancer is a major contributor to global mortality rates and identification is critical to improve patient outcomes. In recent years, machine learning algorithms have demonstrated promising results in identifying lung nodules from medical images. The most compelling area of research for scientists is the early detection of lung cancer. This study is a method for lung nodule detection using CT images. The study incorporates a hybrid model that combines multiple machine learning algorithms including CNN, SVM, DTC, ANN, and KNN to improve the accuracy of nodule detection. The hybrid model demonstrated high accuracy in identifying various types of lung nodules, including Adenocell carcinoma, squamous cell carcinoma, and large cell carcinoma. Specifically, the model achieved an accuracy rate of over 90% in detecting and differentiating normal lung tissue and Adenocell carcinomas. Accuracy graphs and priority setting were utilized to assess the model's capability in accurately predicting the presence of lung cancer. Additionally, the efficiency of the hybrid model was compared with other machine learning algorithms, including SVM, Random Forest, and Decision Trees. A large dataset of CT scans was collected for training and evaluation purposes. The results demonstrated the advantages of the suggested hybrid model in terms of accuracy and efficiency. This study highlights the importance of early lung nodule identification using CT scans and demonstrates the effectiveness of the hybrid model in accurately identifying different types of lung nodules. © 2023 IEEE.

关键词： computerized tomography

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Reviewing the Landscape of Deep Learning Approaches for Breast Cancer Detection 4

Reviewing the Landscape of Deep Learning Approaches for Brea...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Maiti, Souvik Juneja, Sonam Goyal, Reema Chaudhry, Navneet Chandigarh University Dept. of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350306118

Rapid and precise identification of breast cancer is crucial in order to improve patient outcomes for this prevalent and life-Threatening disease. The emergence of AI and deep learning techniques has opened new avenues for enhancing breast cancer screening and diagnosis. CNNs and ensemble models, among other deep learning architectures, have demonstrated promising results in the analysis of medical pictures and the extraction of useful characteristics for precise diagnosis. Additionally, the use of transfer learning and pretrained CNN models has demonstrated superior performance in classifying breast tumor images. Studies investigating the usage of one-dimensional data conversion into pictures and utilizing stacked ensemble models for increased classification accuracy show that the use of deep learning extends beyond image-based research. These techniques offer potential solutions for addressing the challenges in breast cancer detection, such as distinguishing between benign and malignant tumors and predicting prognosis. By automating the diagnosis process, deep learning algorithms can assist medical professionals in making informed decisions and providing personalized treatment plans. Enhancing the accuracy and reliability of breast cancer prediction models necessitates further research into the incorporation of larger datasets, processing multi-modal data, and advancements in image generation methods. Overall, AI and deep learning hold great promise in revolutionizing breast cancer screening and diagnosis, ultimately leading to improved survival rates and better patient care. © 2023 IEEE.

关键词： Support vector machines

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Textual Authenticity in the AI Era: Evaluating BERT and RoBERTa with Logistic Regression and Neural Networks for Text Classification 16

Textual Authenticity in the AI Era: Evaluating BERT and RoBE...

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16th International Symposium on Electronics and Telecommunications, ISETC 2024

作者： Hazim, Layth Rafea Ata, Oguz Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350390865

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models' robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications. © 2024 IEEE.

关键词： Logistic regression

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A review on investigating the role of block-chain in cyber security

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Materials Today: Proceedings 2022年 56卷 3312-3316页

作者： Aggarwal, Bharat Kumar Gupta, Ankur Goyal, Deepak Gupta, Pankaj Bansal, Bijender Barak, Dheer Dhwaj Dept. of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

In the issue of cyber security, block-chain, which is best known for crypto currencies, is providing an interesting answer. Block-chain has excellent safeguards against information manipulation. It supports securing access to Internet of Things (IOT) devices. Furthermore, it enables hacked IOT network instruments to be closed and unplugged. Block-chain serves as a distributed ledger system. It is used in conjunction with IOT to enable machine-to-machine interactions. It makes use of a database that contains a collection of transactions. These transactions are double-checked and recorded in a central ledger. On each node, these ledgers have been deployed. The idea of block chain is discussed in conjunction with the Internet of Things in this research study. Several studies in the fields of block-chain and IOT are considered in this paper. The necessity for block-chain and its development have been discussed in terms of its applicability difficulties. In addition, the significance of block chain in IOT-based research for online transaction security is examined. © 2022

关键词： Internet of things

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Enhancement of Accuracy for Iris Presentation Attack

Enhancement of Accuracy for Iris Presentation Attack

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

作者： Priyanka, V. Shyam, Gopal K. Presidency University Dept. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300826

Biometrics is the assessment and statistical examination of an individual's distinctive physical and behavioural traits. Identification, access control, and identifying those who are being monitored are the major areas where technology is focused. In security systems, biometric-based recognition has been substituting traditional recognition techniques. Iris recognition (IR) is more utilized in today's biometric technologies, which are applied to various devices for various security concerns. The effectiveness of IR systems has significantly improved during the past ten years. This is due to recent developments in deep convolutional neural networks (CNNs) and computer vision, as well as the availability of enormous training datasets. A presentation attack is a kind of attack where an imposter creates artificial or fake out of genuine. This paper proposed an effective approach to increase the accuracy in detecting iris presentation attacks and design the state-of-the-art of existing CNN techniques from 2015 to 2023. The proposed method is a Dual Channel Convolutional Neural Network Presentation Attack Detector(DC-CNNPAD) for increasing the accuracy of detecting real iris. The experiment was performed on the sample LivDet-2017 dataset to identify the effectiveness of the model in order to detect the artifact. The results obtained from the detection model on the sample dataset are extremely good. © 2023 IEEE.

关键词： Biometrics

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