Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalab...
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Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational *** this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a ***,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural *** approach offers high accuracy,easy scalability to new buildings,and computational *** results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art ***,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.
Social media platforms (SMPs) are frequently utilised as a readily accessible and comprehensive medium for expressing personal opinions nowdays. The use of euphemism, a linguistic strategy in which the underlying feel...
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Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships i...
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Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the pass...
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Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex
Parkinson’s disease is one of the most prevalent and harmful neurodegenerative conditions (PD). Even today, PD diagnosis and monitoring remain pricy and inconvenient processes. With the unprecedented progress of arti...
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Human activity recognition is a crucial domain in computerscience and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, ...
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Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein...
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Blockchain is a distributed database that multiple parties can maintain and share. This new technology is expected to greatly impact the healthcare industry. It can help address various issues related to patient care....
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With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of t...
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With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues. Due to its flexible and efficient fine-grained access control feature, Ciphertext-Policy Attribute-Based Encryption(CP-ABE) is suitable for data sharing in IoV. However, there are many flaws in most existing CP-ABE schemes, such as attribute privacy leakage and key misuse. This paper proposes a Traceable and Revocable CP-ABE-based data Sharing with Partially hidden policy for IoV(TRE-DSP). A partially hidden access structure is adopted to hide sensitive user attribute values, and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure. In addition, key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse. Since the main computation task is outsourced to the cloud, the burden of the user side is relatively low. Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.
In recent years, artificial intelligence has undergone robust development, leading to the emergence of numerous autonomous AI applications. However, a crucial challenge lies in optimizing computational efficiency and ...
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