Developing MLP based prediction system for anticancer drug response using hybrid features of genomics and cheminformatics

Traditional cancer treatment methods have become less effective due to the increasing diversity of cancer types. To address this, precision medicine has gained support within the medical community. This approach tailors treatment to individual patients based on their specific disease characteristics. However, a major challenge lies in accurately predicting how a patient will respond to a specialized drug. Numerous machine learning-based predictive systems have been developed to address this challenge. These systems utilize genomic signatures and the chemical structure of drugs to predict drug activity. In this paper, we introduce a Multi-Layer Perceptron (MLP) based system for predicting the response of anticancer drugs. Our system utilizes hybrid features derived from both genetic expression and the chemical structure of drugs. It is developed using the well-known GDSC dataset (Genomics of Drug Sensitivity in Cancer). Our system achieved a lower Root Mean Square Error (RMSE) value of 0.889, in contrast to the RMSE value of 0.983 obtained by the current state-of-the-art (SOTA) system, SwNet. This indicates superior predictive accuracy. The findings suggest that our proposed research holds promise for the development of targeted drugs for anticancer treatments

Characterization based machine learning modeling for the prediction of the rheological properties of water‑based drilling mud

The successful drilling operation depends upon the achievement of target drilling attributes within the environmental and economic constraints but this is not possible only on the basis of laboratory testing due to the limitation of time and resources. The chemistry of the mud decides its rheological potential and selection of the techniques required for recycling operations. Conductivity, pH, and photometer testing were performed for the physio-chemical characterization of the grass to be used as an environmental friendly drilling mud additive. In this study, different particle sizes (75, 150, and 300 µm) of grass powder were mixed in mud density of 8.5, 8.6, and 8.7 ppg in the measurement of gel strength and viscosity of drilling mud. The grass additive was added in different weight conditions considering no additive, 0.25, 0.5, and 1 g to assess the contribution of grass on the gel strength and viscosity of the drilling mud. The machine learning techniques (Multivariate Linear Regression Analysis, Artificial Neural Network, Support Vector Machine Regression, k-Nearest Neighbor, Decision Stump, Random Forest, and Random Tree approaches) were applied to the generated rheological data. The results of the study show that grass can be used for the improvement of the gel strength and viscosity of the drilling mud. The highest improvement of the viscosity was seen when grass powder of 150 µm was added in the 8.7 ppg drilling mud in 0.25, 0.5, and 1 g weights. The gel strength of the drilling mud was improved when the grass additive was added to the drilling mud 8.7 ppg. Random forest and Artificial Neural Network had the same results of 0.72 regression coefficient (R2) for the estimation of viscosity of the drilling mud. The random tree was found as the most effective technique for the modeling of gel strength at 10 min (GS_10min) of the drilling mud. The predictions of Artificial Neural Network had 0.92 R2 against the measured gel strength at 10 s (GS_10sec) of the drilling mud. On average, Artificial Neural Network predicted the rheological properties of the mud with the highest accuracy as compared to other machine learning approaches. The work may serve as a key source to estimate the net effect of grass additives for the improvement of the gel strength and viscosity of the drilling mud without the performance of any large number of laboratory tests.publishedVersio

Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient

Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be 21,482,withanadditional29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient’s conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson’s over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson’s progression

Critical analysis of resource sharing and optimization in fog clustering

Fog computing aims to process data closer to its source to reduce latency and enhance application performance. However, the integration of an additional fog layer introduces complexities in resource management and service guarantees.This paper critically analyzes resource sharing and optimization in fog clustering, focusing on adaptive middleware designed to manage resources within and across fog clusters. We present a novel middleware architecture that facilitates efficient load balancing, task scheduling, and inter-cluster communication. The middleware’s performance is evaluated through simulations using OMNeT++, highlighting improvements in response time and resource utilization compared to traditional cloud processing

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals. It can be linked to several life-threatening diseases. Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss. State-of-the-art technologies have the potential for long-term benefits in post-surgery living. In this work, an Internet of Things (IoT) framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight. The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients. It also attempts to automate the data analysis and represent the facts about a patient. The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system. The proposed IoT framework also benefits from machine learning based activity classification systems, with relatively high accuracy, which allow the communicated data to be translated into meaningful information

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Tracheal reconstruction for comlex acute tracheal stenosis

Tracheal stenosis refers to a reduction in the size of the tracheal lumen and can be due to a myriad of reasons, but the most common remains trauma. In iatrogenic situations, internal trauma is the most likely culprit, resulting from prolonged intubation. Our case reviews a patient who developed severe tracheal stenosis (90% reduction in lumen size) within a month of a threeday- long intubation, and presented to the emergency room with dyspnea, orthopnea, and stridor. Tracheal reconstruction with resection of the stenosed segment and end-to-end anastomosis was done. The patient returned a month later with re-stenosis, and underwent tracheal dilatation. Subsequently, he was discharged with a tracheostomy with no problems thereafter

Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance