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A secure and privacy preserving model for healthcare applications based on blockchain-layered architecture

International Journal of computers and Applications 2024年第12期46卷 1206-1218页

作者： Sanober, Adla Anwar, Shamama Department of Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India

Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. This paper explores the application of blockchain technology-based layered architecture integrated with Interplanetary File System (IPFS) storage to provide a secure and robust sharing platform for healthcare system. A multilayer security is provided in the proposed system including a 128-bit Random Deoxyribonucleic Acid (DNA) encoding sequence with a modified version of the 128-bit Advanced Encryption Standard (AES) key expansion symmetric key technique is being used before storing the records in the IPFS storage, Rivest–Shamir–Adleman (RSA) 2048 digital envelope is used for secure transmission of symmetric keys, RSA 1024 is used for digital signature, and Secure Hash Algorithm 256-bit (SHA-256) is used for hashing the record files. Comparative analysis with existing state-of-the-art in blockchain-based E-healthcare system is discussed. The performance of various assessment metrics is evaluated, including encryption and decryption time for EHR records, mining time, record retrieval time, smart contract execution time and network latency. Additionally, a reputation-based filtering mechanism specifically designed in the proposed model to detect and mitigate the various attacks. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electronic health record

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Detecting a suitable path method for assisted localization techniques in transmission of data over wireless sensor networks

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International Journal of Information technology (Singapore) 2025年第4期17卷 2235-2242页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Tamilnadu KattanKulathur 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Tamilnadu KattanKulathur 603203 India

In many Wireless Sensor Networks (WSNs) applications, the relevant sensor node’s location information is essential in determining where the event or situation occurs. Therefore, localization is one of the critical challenges in WSNs. Mobile Anchor Node Assisted Localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL is that the path planning of the Mobile Anchor Node (MAN) should be done in a way that the localization error in the network will be minimal and that all unknown nodes in the network are covered. In this paper, a new path planning approach called Nested Hexagons Curves (NH-exCurves) is proposed for MANAL. NH-exCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model is compared to six different path planning schemes using Accuracy Priority Trilateration (APT) under different evaluation criteria. In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Secondly, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) are observed. The simulation results present the superiority of the proposed work over existing path planning models used in this study. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Accuracy priority trilateration (APT) Localization Mobile anchor node assisted localization (MANAL) Static path planning Wireless sensor networks (WSNs)

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Farm-Level Smart Crop Recommendation Framework Using Machine Learning

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Annals of Data science 2025年第1期12卷 117-140页

作者： Bhola, Amit Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Agriculture is the primary source of food, fuel, and raw materials and is vital to any country’s economy. Farmers, the backbone of agriculture, primarily rely on instinct to determine what crops to plant in any given season. They are comfortable following customary farming practices and standards and are oblivious to the fact that crop yield is highly dependent on current environmental and soil conditions. Crop recommendations involve multifaceted factors such as weather, soil quality, crop production, market demand, and prices, making it crucial for farmers to make well-informed decisions. An improper or imprudent crop recommendation can affect them, their families, and the entire agricultural sector. Modern technologies like artificial intelligence, machine learning, and data science have emerged as efficient solutions to combat issues like declining crop production and lower profits. This research proposes a Smart Crop Recommendation framework that leverages machine learning to empower farmers to make informed decisions about optimal crop selection. The framework consists of two phases: crop filtration and yield prediction. Crops are filtered in the first phase using an artificial neural network based on local input parameters. The second phase estimates yield for filtered crops, considering the season, farm area, and location data. The final recommendation provides farmers with crops aimed at maximizing profit. The remarkable 99.10% accuracy of the framework is demonstrated through experimentation using artificial neural networks and the 0.99 R2 error metric for the random forest. The uniqueness of this framework lies in its distinctive focus on the farm level and its consideration of the challenges and various agricultural features that change over time. The experimental results affirm the effectiveness of the framework, and its lightweight nature enhances its practicality, making it an efficient real-time recommendation solution. © The Author(s), under exclusi

关键词： Crops

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A novel binary quantizer for variational autoencoder-based image compressor

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International Journal of computers and Applications 2024年第8期46卷 604-620页

作者： Thulasidharan, Pillai Praveen Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

Neural network-based encoder and decoder are one of the emerging techniques for image compression. To improve the compression rate, these models use a special module called the quantizer that improves the entropy of the generated representation. Here, we propose a novel binary quantizer and an auxiliary support module known as the remapper that co-ordinate for better compression and decompression of an image. The proposed quantizer generates the compressed binary representation whereas the remapper aids in minimizing the reconstruction error during decompression. The combined optimization of the model consisting of the encoder, quantizer, remapper, and the decoder as a single entity is not feasible due to the non-differentiable nature of the quantizer. To mitigate this, we propose a step-by-step training process wherein the encoder and decoder are trained first, then keeping the encoder parameters fixed, the remapper is trained along with the decoder to obtain a fully optimized model. The training is performed with different types of images and the results are compared to understand the best training procedures. Experimental results reveal that the proposed model can compress a (Formula presented.) image to a mere 256 bits, thereby achieving a compression ratio of 6144:1;with excellent perceptual reconstruction during decompression. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image compression

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Query reformulation system based on WordNet and word vectors clusters

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9119-9137页

作者： Jumde, Amol Keskar, Ravindra Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

With tremendous evolution in the internet world, the internet has become a household thing. Internet users use search engines or personal assistants to request information from the internet. Search results are greatly dependent on the entered keyword. Casual users may enter a vague query due to lack of knowledge of the domain-specific words. We propose a query reformulation system that determines the context of the query, decides on keywords to be replaced and outputs a better-modified query. We propose strategies for keyword replacements and metrics for query betterment checks. We have found that if we project keywords into the vector space of word projection using word embedding techniques and if the keyword replacement is correct, clusters of a new set of keywords become more cohesive. This assumption forms the basis of our proposed work. To prove the effectiveness of the proposed system, we applied it to the ad-hoc retrieval tasks over two benchmark corpora viz TREC-CDS 2014 and OHSUMED corpus. We indexed Whoosh search engine on these corpora and evaluated based on the given queries provided along with the corpus. Experimental results show that the proposed techniques achieved 9 to 11% improvement in precision and recall scores. Using Google’s popularity index, we also prove that the reformulated queries are not only more accurate but also more popular. The proposed system also applies to Conversational AI chatbots like ChatGPT, where users must rephrase their queries to obtain better results. © 2024 – IOS Press. All rights reserved.

关键词： Embeddings

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Dimensionality reduction using neural networks for lattice-based cryptographic keys

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International Journal of computers and Applications 2024年第10期46卷 889-910页

作者： Wahlang, Roisan Chandrasekaran, K. Computer Science and Engineering Department National Institute of Technology Karnataka Surathkal India

Post Quantum Cryptography has received an increasing amount of active research. This has been made prominent by the ever-growing field of quantum computing which poses a formidable threat to the modern cybersecurity landscape. Recently, post quantum schemes have been standardized for adoption into existing security services and protocols. In comparison to contemporary cryptographic schemes, these approaches are lagging behind in terms of performance with regards to functional speed and package sizes. A study into the various applications of neural networks used in classical and post quantum cryptographic schemes has been explored to demonstrate their different possible applications within the various fields of cybersecurity. The main contribution of this work is to investigate the feasibility of dimensionality reduction using the autoencoder neural network on lattice-based keys generated by the Kyber key encapsulation mechanism scheme. Moreover, this work also presents a comparative analysis of the different implemented autoencoder models to showcase their relative performance. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Dimensionality reduction

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Generating Long Financial Report Using Conditional Variational Autoencoders With Knowledge Distillation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1669-1680页

作者： Wang, Ziao Ren, Yunpeng Zhang, Xiaofeng Wang, Yiyuan Harbin Institute of Technology Department of Computer Science Engineering Shenzhen518055 China

Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a conditional variational autoencoders (CVAEs)-based approach that distills external knowledge from a set of news-report data. Specifically, we design an encoder-decoder architecture to learn the latent variable distribution from this set of news-report data to provide background knowledge. Next, a teacher-student network is employed to distill knowledge to refine the output of the decoder component. To evaluate the model performance, extensive experiments have been performed on two public datasets using evaluation criteria like BLEU, ROUGE, METEOR, and human evaluation. Our promising experimental results demonstrate that our proposed approach outperforms existing state-of-the-art approaches. © 2020 IEEE.

关键词： Natural language processing systems

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Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

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Discover Internet of Things 2024年第1期4卷 16页

作者： Walling, Supongmen Lodh, Sibesh Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy. © The Author(s) 2024.

关键词： Network intrusion

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A Stratified Seed Selection Algorithm for K-Means Clustering on Big Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1334-1344页

作者： Bajpai, Namita Paik, Jiaul H. Sarkar, Sudeshna Indian Institute of Technology Department of Artificial Intelligence Kharagpur721302 India Indian Institute of Technology Department of Computer Science Kharagpur721302 India

In k-means clustering, the selection of initial seeds significantly influences the quality of the resulting clusters. Moreover, clustering large-sized data introduces an additional challenge for seed selection. We propose a novel and scalable seed selection approach by jointly modeling the quality and diversity of the potential seeds through a principled probabilistic stochastic point process. To this end, we also propose a novel seed quality estimation approach on large data. Our approach quantifies the quality of a seed by measuring the divergence between the distribution of similarity between the closest neighbors and that of the randomly chosen neighbors from exhaustive stratified batches of samples. Unlike many existing scalable approaches, we do not rely on a small sample of the original data;instead, we use the entire data, thereby minimizing the chance of leaving out information about a potentially high-quality seed. The extensive evaluation on a set of benchmark data shows that it outperforms a number of strong, well-known, and recent algorithms measured by three standard metrics. © 2020 IEEE.

关键词： k-nearest neighbors

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Detecting Anomalies for corn crop disease using Isolation forest 1

Detecting Anomalies for corn crop disease using Isolation fo...

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1st IEEE International Conference on networking and Communications, ICNWC 2023

作者： Sowmiya, K. Thenmozhi, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350336009

Today, the most challenging issue for researchers is to continue their research for crop disease using deep learning and image processing. One of the crucial detection methods used in one-class classification to find the outliers in the dataset is anomaly detection. The isolation forest approach is used in this work to discover anomalies using the corn dataset from Kaggleas a benchmark dataset. To find abnormalities in the provided dataset, this algorithm mimics a binary decision tree technique. This algorithm's classification accuracy is 89 percent. For the corn dataset, the experimental findings of the isolation forest show improved outcomes. Other anomaly identification methods will eventually combine anomaly scores and new datasets on leaf disease. © 2023 IEEE.

关键词： Image processing

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