The progress in Natural Language Processing (NLP) using Large Language Models (LLMs) has greatly improved medical sentiment analysis of patient feedback extraction from health-related question and answer. However, using LLMs to analyze such data often requires significant training data and computational resources, resulting in considerable increases in training costs and durations, which is one of the primary issues in applying LLMs to real-world healthcare scenarios. To tackle these challenges, a novel optimization algorithm named KAdam-EnGPT4LLM, based on Kalman filters and Adaptive Moment Estimation, is proposed to enhance training efficiency and reduce training costs of LLMs for analyzing patient feedback sentiment. Furthermore, the optimization algorithm KAdam-EnGPT4LLM is employed in training the LLM model GPT4ALL for medical sentiment analysis, resulting in the development of GPT4ALL-MediSentAly-KAdam, which leds to faster convergence and more stable training specifically for medical questions and answer in the context of healthcare. The results show that our GPT4ALL-MediSentAly-KAdam with the optimization algorithm KAdam-EnGPT4LLM achieved better performance that include the best Accuracy, Recall, F1-score, and Runtime for both datasets, outperforming traditional fine-tuned LLMs such as the classic GPT4ALL, Ada, Babbage, Curie, and Duvinci.

Breast cancer is the most prevalent cancer among women and poses a significant global health challenge due to its association with uncontrolled cell proliferation. Artificial intelligence (AI) integration into medical practice has shown promise in boosting diagnosis accuracy and treatment protocol optimisation, thus contributing to improved survival rates globally. This paper presents a comprehensive analysis utilizing the Wisconsin Breast Cancer dataset, comprising data from 569 patients and 30 attributes. We propose BreastCancerNet, a hybrid AI architecture that leverages dual deep neural networks (DNNs) coupled with an attention mechanism to enhance breast cancer diagnosis. The proposed framework integrates two distinct DNNs (DNN-I and DNN-II) to extract diverse feature representations from the dataset, which are then concatenated for comprehensive analysis. An attention mechanism is employed to prioritize critical features, thereby improving the model's focus on essential characteristics of the input data. The final classification is performed using a support vector machine (SVM), achieving an impressive accuracy rate of 99.42% in differentiating between malignant and benign cases. Furthermore, we introduce a user-centric web application that facilitates real-time breast cancer detection by allowing users to input new attributes. This intuitive web interface fosters interactive engagement with the predictive algorithm, potentially enhancing breast cancer screening and treatment outcomes.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent, unpredictable attacks, affects individuals of all ages. IoT-based seizure monitoring can greatly enhance seizure patients' quality of life. IoT device acquires patient data and transmits it to a computer program so that doctors can examine it. Currently, doctors invest significant manual effort in inspecting Electroencephalograph (EEG) signals to identify seizure activity. However, EEG-based seizure detection algorithms face challenges in real-world scenarios due to non-stationary EEG data and variable seizure patterns among patients and recording sessions. Therefore, a sophisticated computer-based approach is necessary to analyze complex EEG records. In this work, the authors proposed a hybrid approach by combining traditional convolution neural (CN) and recurrent neural networks (RNN) along with an attention mechanism for the automatic recognition of epileptic seizures through EEG signal analysis. This attention mechanism focuses on significant subsets of EEG data for class recognition, resulting in improved model performance. The proposed methods are evaluated using a publicly available UCI epileptic seizure recognition dataset, which consists of five classes: four normal conditions and one abnormal seizure condition. Experimental results demonstrate that the suggested approach achieves an overall accuracy of 97.05% for the five-class EEG recognition data, with an accuracy of 99.52% for binary classification distinguishing seizure cases from normal instances. Furthermore, the proposed intelligent seizure recognition model is compatible with an IoMT (Internet of Medical Things) cloud-based smart healthcare framework.

Internet of Medical Things (IoMT) has surfaced as the emerging era of the Internet of Things (IoT), drawing the attention of researchers given its broad operations in Smart Healthcare Systems (SHS) [1]. Since it is extremely risky for an individual to communicate with doctors in the hospital for every minor issue in the present pandemic scenario, we may check our day-to-day health records using IoMT devices and take precautionary measures on our own. In order to improve the delicacy, thickness, and outturn of electronic outfits, IoMT is essential to the healthcare sectors [2], [3].

An estimated 1.3 million individuals every year pass away in crashes involving cars. A comprehensive vehicular communication system is necessary given the worrisome increase in daily accidents brought on by the spike in the number of vehicles using our roadways. The creation of a fresh communication paradigm with an accident prevention focus was motivated by this need. The research presented here offers an extensive system that uses cutting-edge technology to coordinate dynamic interactions between nearby vehicles. A sophisticated inter-vehicle communication network aided by radio frequency (RF) technology forms the basis of this breakthrough. By enabling vehicles to exchange vital warnings and data, this network fosters a cooperative environment for accident prevention. Each vehicle also has a Visible-Light Communications (VLC) module, which was carefully engineered to identify accidents and transmit realtime accident information to all connected vehicles.

The Internet of Things (IoT) has been instrumental in bringing about several advancements and innovations in the domain of healthcare. Healthcare professionals are essentially life savers when it comes to handling emergency cases, such as accidents, heart attacks, etc. Only the patient’s vital parameters generally characterize emergency cases, and the doctors must wait for additional details for a wholesome diagnosis. As a result, the treatment processes and procedures sometimes get hastened and, in turn, put the patients’ lives at risk. It would always be helpful for doctors to be equipped with the medical requirements in advance for deciding the right course of action, thereby increasing the scope and chances of recovery. In this work, multimodel IoT (MMIoT) devices are deployed to monitor and collect health data from different body parts simultaneously. The healthcare data comprises signals and imagery captured from the MMIoT devices. Both the U-Net model and LSTM model are used to analyze the data automatically. The data processing is carried out by the server connected to the MMIoT network. All the medical IoT devices experimented with in this work are interconnected using a potential 5G network for optimal data transmission. The output obtained from the U-Net and the LSTM are channelized through a dense layer to classify the health anomalies accurately. It would not only facilitate but also educate medical professionals to handle unseen and typical cases in the future confidently. It can improve the overall quality of treatment and save lives with the best available resources.

Abstract The open communication medium of the Internet of Things (IoT) is more vulnerable to security attacks. As the IoT environment consists of distributed power limited units, the routing protocol used for distributed routing should be light‐weighted compared to other centralized networks. In this situation, complex security algorithms and routing mechanisms affect the generic data communications in IoT platforms. To handle this problem, this proposed system develops a cooperative and feedback‐based trustable energy‐efficient routing protocol (CFTEERP). This protocol calculates local trust value (LTV) and global trust value (GTV) of each node using node attributes and K‐means‐based feedback evaluation procedures. The K‐means clustering algorithm leaves out the distorted node routing metrics and misbehaving node metrics for all channels. This proposed CFTEERP uses the nearest secure node costs to increase the network lifetime without selecting the nearest nodes for routing the data. In this work, secure routing is initiated using multipath routing strategy that analyses LTV, GTV, next trustable node, average throughput, energy consumption, average packet delivery ratio (PDR) and traffic various metrics of entire IoT communication. The technical aspects of proposed system are implemented to solve different existing techniques' limitations. In the comparative experiment, the proposed method provides 90% of PDR and a minimal energy consumption rate of 25% lesser than the existing systems against different malicious attacks.

Summary X‐ray images are widely used to identify fractures in human bones. Radiographic diagnosis models take more time and manual procedures for establishing physical analysis of bone fractures. Furthermore, the lack of clinical resources and the medical support systems lead to inaccurate bone data extractions. In this case, the need for detail is required to understand the scientific issues in x‐ray and magnetic resonance imaging (MRI) based bone fracture diagnosis solutions. Particularly, the current bone fracture detection models are emerging with computerized frameworks and health informatics consoles. In this regard, this article lists five phases. First, data preparation and data collection tasks are initiated. Second, bone fracture diagnosis models are comparatively analyzed to find optimal observations. Consequently, the third phase of the article examines the treatments against bone fracture observations with traditional and deep learning (DL) techniques. The fourth phase consists of relative diagnosis solutions and the treatment benefits on various types of technical frameworks. Finally, this work concludes the comparison with recent DL‐based bone fracture identification models.

Abstract The production rate of crops is significantly declining due to natural disasters, animal interventions and plant diseases. Internet of things (IoT) and wireless sensor networks are widely applied in crop field monitoring systems to observe the quality of each plant and the field. This work proposes IoT based crop field protection system (ICFPS) that monitors and protects the crop fields from animal intrusions. This proposed system uses ultrasonic sensors, hyperspectral cameras, voice recorded buzzers and other agriculture sensors to protect the entire crop field. This system uses numerous sensor nodes and cameras for gathering field objects (images and environmental objects). The proposed ICFPS creates deep learning techniques such as recurrent convolutional neural networks (RCNN) and recurrent generative adversarial neural networks (RGAN) for feature extraction, disease detection and field data monitoring practices. This proposed work develops a smart city‐based agriculture system using cognitive learning approaches. This proposed system analyses crop field data and provide automatic alerts regarding animal interferences and crop diseases. Moreover, the cognitive smart crop field system observes various field conditions which support for good production rate. In this system, sensors and camera‐enabled agriculture drones are coordinated with each other to collect the field data regularly. At the same time, the proposed work trains the RCNN and RGAN units using effective crop field datasets to attain realistic decisions within minimal time intervals. The experiment details and results show the proposed ICFPS works with 8%–10% of more classification accuracy than existing systems.

Extracting discriminative speaker-specific representations from speech signals and transforming them into fixed length vectors are key steps in speaker identification and verification systems. In this study, we propose a latent discriminative representation learning method for speaker recognition. We mean that the learned representations in this study are not only discriminative but also relevant. Specifically, we introduce an additional speaker embedded lookup table to explore the relevance between different utterances from the same speaker. Moreover, a reconstruction constraint intended to learn a linear mapping matrix is introduced to make representation discriminative. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods based on the Apollo dataset used in the Fearless Steps Challenge in INTERSPEECH2019 and the TIMIT dataset.