This research paper presents a proactive approach to congestion control in IoT networks using an encoder–decoder LSTM (ED-LSTM) model to predict packet loss ratios ahead of time. By forecasting ...

ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...

Background: Accurate forecasting of lung cancer incidence is crucial for early prevention, effective medical resource allocation, and evidence-based policymaking. Objective: This study proposes a ...

Beyond tumor-shed markers: AI driven tumor-educated polymorphonuclear granulocytes monitoring for multi-cancer early detection. Clinical outcomes of a prospective multicenter study evaluating a ...

ABSTRACT: Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types ...

Large Language Models (LLMs) have become indispensable tools for diverse natural language processing (NLP) tasks. Traditional LLMs operate at the token level, generating output one word or subword at ...

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Abstract: Automatic highlighting from texts is an abstractive summarization problem that is frequently focused on in natural language processing. In encoder-decoder architectures, developed for ...