AI is quickly transforming the landscape of medical diagnostics, leading to remarkable gains in accuracy, speed, and availability. We perform a systematic review on the fundamental strategies, tools, applications, and challenges of AI enabled diagnostic in medicine with focus on medical diagnostics in IoT-based healthcare networks. The paper presents the use of machine learning algorithms, deep learning models, including CNN, and NLP to interpret clinical documentation. It also investigates the usage of smart computing infrastructures such as edge systems and the Internet of Medical Things (IoMT) that facilitates real-time, data-driven clinical decision-making consistently matching or exceeding human-level perception, notably in medical imaging, pathology and biosignal analysis. Nevertheless, there exist great challenges, such as data heterogeneity, lack of high-quality labeled datasets, model interpretability and ethics, such as algorithmic bias and patient privacy. In addition, questions on standards, clinical confidence, and regulation are often considered less important than technical performance but are central to the effective deployment of AI within health systems. This paper presents the comparison of AI-combated diagnostic approaches with the traditional ones, a recent literature review and some research gaps for future work. The study seeks to underpin the need to advance AI systems that can be understood, are clinically applicable and ethically justifiable. It promotes interdisciplinary cooperation and uniformed evaluation methodologies for the safe, efficacious and equitable utilization of AI for healthcare diagnostics.

Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate.

There has been, and will continue to be, discussion over how to monitor wireline communications with increased data rates. In order to meet bandwidth requirements while keeping complexity at an acceptable level, engineers have dealt with many data formats over the years. Manchester encoding is a popular choice among the current solutions for various applications. Another benefit in high-speed input-output lines when the power budget is limited is the Manchester coding transceiver, which can be realized in a reasonably simple fashion. For inexpensive digital data optical transmission, the Manchester data format can be utilised as a modulation mechanism. The performance of the related transceivers under different conditions can be estimated by doing a comprehensive analysis of the Manchester data format and comparing it with other line coding types. System developed and fine-tuned for performance and feasibility evaluation. We used Opti system 2014 to test the system.

Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), -nearest neighbour (KNN), and support vector machine (SVM).

Data transmission using protocols such as TCP (Transmission Control Protocol) between wireless and wired systems, is still include a concrete difficult. The congestion control appliance does not allow the flow of data to obtain a full bandwidth on present system acquaintances. To resolving this problematic, various protocols is hosted with a high performance. Consequently, this article improved congestion control algorithm and congestion window (cwnd) is suggested to increase TCP performance by growing the cycles number of the next cwnd to improving the number of sent packets. The suggested procedure uses a novel technique supported by the existing bandwidth of links to detecting the capacity of the network channel to develop a steady clocking mechanism in congestion avoidance phase. The effort provided in this article is based on Network Simulation (NS-2) to modeling the putative mechanism.

Artificial Intelligence (AI) is becoming the cornerstone of the future of healthcare diagnostics, that has to ability to change the healthcare diagnostic landscape in terms of diagnostic accuracy, speed, and availability. This systematic review investigates the basic methods, tools, applications, and challenges involved in the integration of AI in diagnostic medicine. It emphasizes the using of machine learning models, deep learning networks (e.g., CNNs), NLP for clinical documentation, and smart computing infrastructures, such as edge device and IoMT. They are making possible real-time, data-driven decision making that is already at human-expert-level performance or, in some cases, even better (in the analysis of medical images, pathology and biosignal, for example). Despite these breakthroughs, a number of significant challenges remain, such as data diversity and heterogeneity, lack of high-quality labeled data, model interpretability and ethical issues (e.g. algorithmic bias and patient privacy). In addition, there are strong compatibility, clinician reliance, regulation validation (these are the most commonly neglected part of technical development) factors, which will keep AI supported systems tightly engrafted to the existing health systems. This paper further attempts to compare AI based and conventional diagnostic methods, presents recent literature insights and scopes the research gaps in the ongoing research endeavors. The aim of the survey is, within the broader picture of the underpinning principles of the ‘fitness for purpose’ of predictive models, to capture these foundations fully in order to assist future developments that are driven by a desire for transparency, fairness, and clinical relevance. It emphasizes the necessity of interdisciplinary cooperation and of standard assessment methods to achieve a safe and effective application. As AI transforms diagnostics, the future of healthcare will rely on creating AI systems that are inclusive, interpretable and ethically grounded, and that can tackle global health problems and respond flexibly to the demands of clinical practice.