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Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

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Journal of Optical Communications 2024年第2期45卷 347-354页

作者： Meenu Sharma, Anshu Malhotra, Rahul Department of Electronics and Communication Engineering CT Institute of Engineering Research and Technology Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology & Research Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology and Management Shahpur Near Lambra Punjab Jalandhar144020 India

In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Fog

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Classification of Autism Spectrum Disorder using CNN & Transfer Learning 2

Classification of Autism Spectrum Disorder using CNN & Trans...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Shyamali Chaudhury, Pamela Tripathy, Hrudaya Kumar Sillicon Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9798350370188

Autism spectrum disorder (ASD) is a neurodevelopment condition that impacts a person's ability to communicate and interact socially. Diagnosing autism spectrum disorder (ASD) early and correctly is critical for providing appropriate help to those who need it. Transferring information or learned representations from one task or domain to another is possible using a computerized learning technique known as transfer learning. In this study, we proposed a method for classifying ASD using an Enhanced Layerwise Sequential Convolutional Neural Network (ELS-CNN) with transfer learning. Transfer learning involves leveraging pre-trained models on large-scale datasets used for the Kaggle platform and fine-tuning them for specific tasks. A dataset that tested these models was collected and consisted of 2,928 face images. These images are preprocessed using Adaptive Median Filtering to remove noise from an image and Histogram Equalization to improve image contrast. Otsu's Binary Threshold can be used for image segmentation. Extracting the features can be Principal Component Analysis (PCA) for image compression. We suggested the ELS-CNN algorithm, which has demonstrated good performance in a number of image identification applications. The output of the deep learning neural network models was assessed using common assessment measures like accuracy, precision, recall, and fl-score. The Enhanced Layerwise Sequential CNN algorithm achieved the highest accuracy. © 2024 IEEE.

关键词： Principal component analysis

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An Experimental Analysis of Machine Learning Models for Diabetes Classification 7th

An Experimental Analysis of Machine Learning Models for Di...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Ghosh, Subhayu Acharya, Riyan Jana, Nanda Dulal Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819764648

Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection and effective management of diabetes are crucial to prevent severe complications and improve the quality of life for affected individuals. Machine Learning (ML) techniques have shown great promise in aiding the early detection of various diseases on patient data, including diabetes. ML algorithms can analyze vast datasets, identify patterns, and make accurate predictions, helping medical professionals to diagnose diabetes at its early stages. In our work, we employed several ML models for diabetes classification using different datasets. These models include K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient-Boosted Model (GBM), eXtreme Gradient Boosting (XG-Boost), Adaptive Boosting (Ada-Boost), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). We performed a comparative analysis of their performance on three distinct datasets using evaluation metrics like accuracy, precision, F1-score, sensitivity, specificity and Cohen’s Kappa Value. Our findings revealed that the RF algorithm is optimal for symptoms-based and primary lab report-based diabetes detection, while XG-Boost excels in classifying different types of diabetes from a multi-class dataset. Moreover, we investigated diverse symptoms and their impact on diabetes outcomes, offering insights into preventive measures and early stage monitoring for this disease classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Protecting Recommendations: Combating BIAS and Manipulation in Recommendation 8

Protecting Recommendations: Combating BIAS and Manipulation ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Infant, P.Alwin Kumar, P.Mohan Jainish, G.R. Hindustan Institute of Technology & Sciencce Department of Computer Science and Engineering Chennai India Loyola Icam College of Engineering & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Recommender systems assist consumers in navigating the deluge of information by helping them find services and goods. The effectiveness of recommender systems has been thoroughly examined in research currently available from a number of angles. In the research community, robustness against malicious attack is becoming a more important concern. Collaborative Filtering (CF) recommender systems have been established to accomplish this objective. However, due to the fact that they are collaborative recommender systems are open to attack. To weaken the system, attackers can introduce fictitious and biased profiles. An examination of the random attack, Bandwagon attack, Mixed Attack and Noise Injection on recommender systems is presented in this work. Numerous algorithms are susceptible to profile injection attacks. Some of the earlier approaches suffer from only looking at one aspect of user rating values while assessing the rating matrix, ignoring alternative viewpoints. First, a rating matrix in this research that takes into account both genuine and fictitious user ratings is created. Next, using user rating time as a basis, the rating matrix is examined. Next, build a model to distinguish between real and fraudulent users using a variety of classifiers, including Principal Component Analysis (PCA), K-Nearest Neighbour (KNN), and Bayesian Networks (BN). Ultimately, a number of tests are conducted, and a summary of the findings comparing different approaches' abilities to detect small-scale attacks and detect overall attacks is presented. © 2024 IEEE.

关键词： Collaborative filtering

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DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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VoteChain: Promising a Secure and Transparent Election using Blockchain and Biometrics 4

VoteChain: Promising a Secure and Transparent Election using...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vivekanandan, G. Prabahar Godwin James, T. Divya, B. Tejas, Madhav V. Naveen Karthick, T.K. Yuvanesh, P. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India

ISBN: (纸本)9798331508845

Democratic voting is one of the significant aspects of governance that has various complications which are as follows;Complication arising from transparency Complication arising from low voter turnout Complication arising from vulnerability to the manipulation of the elections Complication arising from low confidence in the election process Complication arising from forgery of voter id cards Complication arising from delays in the announcement of the results Complication arising from security breach. All these issues erode the credibility of elections and public faith. To them, blockchain has a perfect solution in that it is decentralized, secure, transparent, and cannot be tampered with technology. To realize our e-voting system, we built an evoting application as a smart contract on the Ethereum platform using digital wallets and solidity. A given voter is assigned tokens (gas) which are utilized every time they cast their vote in order to eliminate vote forgeries and other undesirable incidents. This blockchain-based system does not require physical polling stations;also, voting is scalable and secure with flexible consensus algorithms, cryptographic hashing, and measures in response to a 51% attack. It also incorporates fingerprint scanning, to further discourage fraud and make sure that the process is completely secure and transparent. Since these records are stored in a blockchain, the ability of a single unscrupulous agency trying to manipulate the records is very hard given that records are validated by the nodes in the network, making the voting process more secure and credible. © 2024 IEEE.

关键词： Blockchain

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An Adaptive IDS Scheduling Framework for Host Based Mimicry Attack Detection in IoT Networks 18

An Adaptive IDS Scheduling Framework for Host Based Mimicry ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sanyal, Surja Khatua, Manas Chattopadhyay, Pratik Indian Institute of Technology Guwahati Department of Computer Science and Engineering Guwahati India Varanasi Department of Computer Science and Engineering Varanasi India

ISBN: (纸本)9798350391725

With the expanding applicability of the Internet of Things (IoT), novel IoT network security challenges also appear more frequently. Host-based Mimicry Attacks (HMA) are one of them that is difficult to detect by traditional Intrusion Detection Systems (IDS) since they mimic legitimate network and host behaviors to evade detection. Additionally, IDSs consume host device resources when their implementations, partially or fully, are executed in the device, and thus it is needed to be scheduled properly so as to save device resources when possible. Current IDS solutions capable of detecting HMAs mostly use randombased scheduling or do not use scheduling at all and so are always executed. Random-based scheduling solutions have low HMA detection accuracy, while solutions without scheduling do not save device resources. This work proposes an adaptive IDS scheduling framework that suspends IDS execution based on device trust and resumes IDS execution when the device is under attack. Extensive experimentation using the Cooja simulator and Python script shows that the proposed framework achieves a device behavior classification accuracy of at least 97.15% when under attack while saving at least 81.98% of CPU energy when not under attack for a network of 40 devices, as compared to those when executing the IDS without any scheduling scheme. © 2024 IEEE.

关键词： Network intrusion

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Sentiment and hashtag-aware attentive deep neural network for multimodal post popularity prediction

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Neural Computing and Applications 2024年第4期37卷 2799-2824页

作者： Bansal, Shubhi Kumar, Mohit Raghaw, Chandravardhan Singh Kumar, Nagendra Department of Computer Science and Engineering Indian Institute of Technology Indore Khandwa Road Simrol Madhya Pradesh Indore453552 India

Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of expression, leading to a notable surge in such multimodal content on social media platforms. Nonetheless, accurately forecasting the popularity of these posts presents a considerable challenge. Prevailing methodologies primarily center on the content itself, thereby overlooking the wealth of information encapsulated within alternative modalities such as visual demographics, sentiments conveyed through hashtags and adequately modeling the intricate relationships among hashtags, texts, and accompanying images. This oversight limits the ability to capture emotional connection and audience relevance, significantly influencing post popularity. To address these limitations, we propose a seNtiment and hAshtag-aware attentive deep neuRal netwoRk for multimodAl posT pOpularity pRediction, herein referred to as NARRATOR that extracts visual demographics from faces appearing in images and discerns sentiment from hashtag usage, providing a more comprehensive understanding of factors influencing post popularity. Moreover, we introduce a hashtag-guided attention mechanism that leverages hashtags as navigational cues, guiding the model’s focus toward the most pertinent features of textual and visual modalities, thus aligning with target audience interests and broader social media context. Experimental results demonstrate that NARRATOR outperforms existing methods by a significant margin on two real-world datasets. Furthermore, ablation studies underscore the efficacy of integrating visual demographics, sentiment analysis of hashtags, and hashtag-guided attention mechanisms in enhancing the performance of post popularity prediction, thereby facilitating increased audience relevance, emotional engagement, and aesthetic appeal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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On the Maximum Distance Sublattice Problem and Closest Vector Problem 26

On the Maximum Distance Sublattice Problem and Closest Vecto...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Kumar, Rajendra Mehta, Shashank K Rajasree, Mahesh Sreekumar Indian Institute of Technology Delhi Department of Computer Science & Engineering New Delhi India Indian Institute of Technology Kanpur Department of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798331532833

In this paper, we introduce the Maximum Distance Sublattice Problem (MDSP). We observed that the problem of solving an instance of the Closest Vector Problem (CVP) in a lattice L is the same as solving an instance of MDSP in the dual lattice of L. We give an alternate reduction between the CVP and MDSP. This alternate reduction does not use the concept of dual lattice. © 2024 IEEE.

关键词： CVP geometry GSO Karp reduction lattice

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Detection and Classification of Grape Leaf Diseases using EfficientNet Model

Detection and Classification of Grape Leaf Diseases using Ef...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Subramanya, S.G. Parkavi, A. Ramaiah Institute of Technology affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350388602

Viticulture or grape cultivation is a significant horticulture method all over the globe. Often, grapevines suffer from many viral, fungal, and bacterial infections that affect production quantity and quality. Farmers worldwide use traditional methods to detect and treat these diseases. However, they are time-consuming, labor intensive, and require significant expertise. The EfficientNetB0 model is implemented for automating the diagnosis of grape leaf diseases. The transfer learning (TL) approach is leveraged to train and fine-tune the EfficientNetB0 model using Kaggle grapevine disease dataset. The leaf image dataset represents four classes - black rot, esca, leaf blight, and healthy samples. The model's performance is evaluated based on metrics like accuracy and loss. The result shows that EfficientNetB0 gave accuracies of 92.45% and 96.73% on training and validation datasets, respectively. The model achieves better results in disease classification while maintaining computational efficiency. © 2024 IEEE.

关键词： Transfer learning

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