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Secure the ownership of WoT devices using secure ownership transfer framework

International Journal of Information technology (Singapore) 2023年第4期15卷 2161-2171页

作者： Singh, Kaptan Tomar, Deepak Singh Department of Computer Science and Engineering Truba Institute of Engineering and Information Technology Bhopal 462038 India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal India

The primary concern behind ownership in Web of Things (WoT) is to provide rights to own devices to operate and access. When the ownership of Internet of Things (IoT) devices changes, there is a likelihood of threats that compromise the security and privacy of sensitive data. Intrusion to the user’s private storage in a cloud is also possible. Therefore, there is a need to spread awareness regarding ownership transfer. It is essential to handle the ownership appropriately, else data and accessibility get compromised, affecting both new and previous users. This paper proposed a secure ownership transfer framework for the Web of Things. This paper intends to present ownership transfer schemes securely, and in this work, a Secured Third Party is introduced, which acts as an intermediate. They help to perform secure Ownership transfer between two owners. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Authorization Internet of things Ownership transfer Security STP Web of things WoT devices

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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

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Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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Deep learning based predictive analysis of energy consumption for smart homes

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Multimedia Tools and Applications 2025年第12期84卷 10665-10686页

作者： Malik, Sangeeta Malik, Sitender Singh, Ishmeet Gupta, Harsh Vardhan Prakash, Sidhant Jain, Rachna Acharya, Biswaranjanjan Hu, Yu-Chen Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India IT Department Maharaja Surajmal Institute of Technology New Delhi India IT Department Bhagwan Parshuram Institute of Technology New Delhi India Department of Computer Engineering-AI & BD Marwadi University Gujarat Rajkot India Department of Computer Science TungHai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

Predicting energy consumption has become crucial to creating a sustainable and intelligent environment. With the aid of forecasts of future demand, the distribution and production of energy can be optimized to meet the requirements of a vastly growing population. However, because of the varied types of energy consumption patterns, predicting the demand for any household can be difficult. It has recently gained popularity with social Internet of Things-based smart homes, smart grid planning, and artificial intelligence-based smart energy-saving solutions. Although there are methods for estimating energy consumption, most of these systems are based on one-step forecasting and have a limited forecasting period. Several prediction models were implemented in this paper to address the problem mentioned above and achieve high accuracy, including the baseline model, the Auto-Regressive Integrated Moving Average (ARIMA) model, the Seasonal Auto-Regressive Integrated Moving Average (SARIMAX with eXogenous factors) model, the Long Short-Term Memory (LSTM) Univariate model, and the LSTM Multivariate model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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A cross entropy and whale optimization algorithm based image segmentation for aerial images

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International Journal of Information technology (Singapore) 2024年 1-15页

作者： Ahmed, Saifuddin Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Cachar Silchar 788010 India

There are several image segmentation techniques for image processing such as, thresholding, region based, edge based, watershed etc. However, the multilevel thresholding based image segmentation is the most widely used approach. This approach mostly suffers high computational complexity with the increasing number of threshold levels. The researchers have been using optimization techniques to resolve this issue. This paper presented a multilevel thresholding technique for segmenting aerial images using Whale Optimization Algorithm (WOA). It uses Minimum Cross Entropy (MCE) as an objective function for selecting a set of optimum threshold values. The presented method is evaluated by using a set of standard aerial images. The evaluated performance of the proposed technique is compared with Bacterial Foraging Optimization (BFO), Moth-flame Flame optimization (MFO) and Particle Swarm Optimization (PSO). The overall performance and the quality of segmented images are evaluated based on the following parameters, Average Mean Structural Similarity Index (MSSIM), Average Feature Similarity Index (FSIM), Average Peak Signal to Noise Ratio (PSNR), average mean objective functions values and average CPU rum time values. In terms of quality of the segmented images based on MSSIM, FSIM and PSNR values the presented method performs better than other methods and we found the average CPU run time also to a considerable range. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cross entropy Image segmentation Multilevel thresholding Optimization technique Whale optimization algorithm

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ICE-VDOP: an integrated clustering and ensemble machine learning methods for an enhanced vector-borne disease outbreak prediction using climatic variables

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International Journal of Information technology (Singapore) 2024年第4期16卷 2077-2088页

作者： Jayabalan, Divya Elango, Sivasankar Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli 620015 India

Dengue is prone to cause frequent disease outbreaks. An early outbreak prediction based on the climatic variables is essential to control the disease’s spread and avoid possible outbreaks. Existing non-seasonal outbreak classification uses a partition-based clustering method for outlier removal;however, a standard outlier detection with improved prediction performance is needed to detect outbreaks. We develop ICE-VDOP model, an Integrated Clustering and Ensemble machine-learning model for Vector-borne Disease Outbreak Prediction to classify outbreaks based on non-seasonal climatic characteristics for Bangkok and Bangladesh datasets. The work begins with identifying the clustering approach that generates a refined dataset ensuring the data points’ inherent structure is preserved and does not alter the association of climatic variables and Dengue Incidence. The partitioning-based clustering methods modify the direction of association between climatic variables and Dengue Incidence after outlier removal as it removes the essential data points. The comparative results shows that BIRCH (balanced iterative reducing and clustering hierarchy), a hierarchical clustering technique for outlier removal captures finer non-seasonal climate variables (temperature, rainfall and relative humidity) and gradient boosting (GB) achieves a prediction accuracy of 97% for Bangkok and 98% for the Bangladesh datasets. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Binary classification BIRCH Dengue Gradient boosting Outlier detection Vector-borne disease outbreak prediction

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Hardware Security of Image Processing Cores Against IP Piracy Using PSO-Based HLS-Driven Multi-Stage Encryption Fused with Fingerprint Signature

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SN computer science 2024年第7期5卷 941页

作者： Sengupta, Anirban Anshul, Aditya Department of Computer Science and Engineering Indian Institute of Technology (IIT) Indore 453552 India

The increasing usage of image processing applications in modern technological environments is driven by their ability to enhance visual quality in diverse applications, from social media to medical imaging. The design of these cores as dedicated application-specific hardware intellectual property (IP) must ensure optimized performance and security. It is crucial to optimize these designs while enhancing their security to create a robust, secure design flow that can provide security against hardware security threats like IP piracy and fraudulent ownership claim. The proposed methodology introduces a low-cost security framework for application-specific hardware systems using an HLS-aided fusion watermarking framework and particle swarm optimization-driven design space exploration (PSO-DSE) process. The proposed approach demonstrates the generation of a multi-stage encryption-based signature along with the IP seller’s fingerprint biometric-based signature, which is further fused to generate the final watermark signature. It allows the embedding of a greater number of secret watermark constraints offering lower probability of coincidence and higher tamper tolerance without incurring design overhead, compared to prior works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fingerprint biometric Hardware security HLS Image processing applications IP piracy

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Differently processed modality and appropriate model selection lead to richer representation of the multimodal input

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International Journal of Information technology (Singapore) 2024年第7期16卷 4505-4516页

作者： Panda, Saroj Kumar Diwan, Tausif Kakde, Omprakash G. Department of Computer Science and Engineering Indian Institute of Information Technology Tata Consultancy Services

We aim to effectively solve and improvise the Meta Meme Challenge for the binary classification of hateful memes detection on a multimodal dataset launched by Meta. This problem has its challenges in terms of individual modality processing and its impact on the final classification of hateful memes. We focus on feature-level fusion methodologies in proposing the solutions for hateful memes detection in comparison with the decision-level fusion as feature-level fusion generates richer features’ representation for further processing. Appropriate model selection in multimodal data processing plays an important role in the downstream tasks. Moreover, inherent negativity associated with the visual modality may not be detected completely through the visual processing models, necessitating the differently processed visual data through some other techniques. Specifically, we propose two feature-level fusion-based methodologies for the aforesaid classification problem, employing VisualBERT for the effective representation of textual and visual modality. Additionally, we employ image captioning generating the textual captions from the visual modality of the multimodal input which is further fused with the actual text associated with the input through the Tensor Fusion Networks. Our proposed model considerably outperforms the state of the arts on accuracy and AuROC performance metrics. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep learning Hateful memes Multimodality Text tensor fusion VisualBERT

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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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Lightweight Convolutional Neural Network Model for Cassava Leaf Diseases Classification

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SN computer science 2024年第3期5卷 284页

作者： Tewari, Anand Shanker Computer Science and Engineering Department National Institute of Technology Patna Patna 800005 India

The world population has increased many folds in the last few years and crossed the figure of 7 billion. Africa has the highest population growth rate. Food and water are the first and foremost necessities for the survival of living beings. Cassava is among the staple foods of Africa and other countries. Its roots and leaves fulfill the daily caloric demands of millions of people. In the last few years, the production of cassava has decreased due to the spread of disease in cassava leaves. The manual identification of these diseases needs large number of people with sufficient level of skills in this field, so an automated technique is required that can help farmers by detecting cassava leaf diseases within a small time frame. The paper proposed a lightweight convolutional neural network (CNN) model to find four types of cassava leaf diseases i.e., cassava mosaic disease, cassava green mottle, cassava bacterial blight, and cassava brown streak leaf disease. It uses depthwise separable convolution operation to reduce the number of parameters that makes it suitable for mobile devices. The proposed model has used both channel attention and spatial attention to highlight disease part of the leaf and suppress background scenes. The modified attention mechanism of the proposed work helps in further improving the accuracy of the model. The experimental results exhibit that the proposed model outperforms other state-of-the-art models like VGG16, ResNet 50, EfficientNet, MobileNet V1, and MobileNetV2. The proposed model uses 1.1 million fewer parameters than MobileNet V2 that makes it suitable for low computing power devices. It uses the natural scene images for the classification that enhances its applicability in real-world scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Attention Convolutional neural network Deep learning Leaf disease classification

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Improved bounding box regression loss for weapon detection systems using deep learning

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International Journal of Information technology (Singapore) 2024年 1-17页

作者： Sumi, Lucy Dey, Shouvik Department of Computer Science and Engineering National Institute of Technology Nagaland Chumukedima 797103 India

Rising number of crime rate using firearms (such as open firing, robbery, suicides, mass shootings, homicides, threatening at gun point, etc.), has underscored the growing importance of timely detection of weapons. Bounding box regression, an essential element in object detection, plays a crucial role in accurately identifying and localizing these firearms. This paper introduces a Manhattan-Complete IoU (MCIoU) loss for bounding boxes, which demonstrates significantly faster convergence during training compared to other IoU losses. By incorporating MCIoU into one of the advanced object detection framework, (You Only Look Once) YOLOv7, the proposed work demonstrates consistent improvement on their performance across popular weapon detection benchmarks datasets such as Granda, Synthetic, Cam157, Internet Movie Firearms Database (IMFDB), Gun Movie and Monash. The most encouraging outcomes were obtained on a Gun movie dataset with a precision and recall value of 98.2% and 96.3% respectively, which is an appreciable improvement compared to the baseline YOLOv7 model. Experiments show that the best precision achieved was at 98.2% and mAP@.50:.95 of 42.5% over other existing IoUs. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： CCTV surveillance Deep learning Intersection over union (IoU) Loss function Weapon detection system You Only Look Once v7 (YOLOv7)

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