Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user’s choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scen

Carotid intima media thickness evaluation by ultrasound comparison amongst healthy, diabetic and hypertensive Pakistani patients

Objective: To compare carotid Intima media thickness and atherosclerosis burden amongst healthy, diabetic and hypertensive Pakistani patients.Methods: A cross-sectional study was carried out at the Department of radiology and family medicine, Aga Khan University Hospital Karachi from April 2014 to July 2015. Bilateral carotid ultrasound was done in 133 healthy adults, 65 hypertensive, 31 type-2 diabetic and 37 hypertensive with type-2 diabetes patients. Normal adults were matched for age and gender. Mean intimal media thickness was measured for common and internal carotid arteries. Presence or absence of atherosclerotic plaque was also identified. Height, weight, ethnicity, socioeconomic status and other risk factors were also assessed. Ultrasound findings were compared between healthy and diseased patients through statistical tests.Results: A total of 266 patients participated (Controls=133, Hypertensive=65, Diabetic=31, and Diabetes with Hypertension=37). There was no significant difference in the baseline characteristics between the four patients\u27 groups for age (p\u3e0.05) and gender (p\u3e0.05). The mean carotid intima media thickenss of right common carotid artery was significantly higher in patients with diabetes along with hypertension as compared to the control group (p=0.03). For (RICA) Right Internal Carotid Artery, (LCCA) Left Common Carotid Artery and (LICA) Left Internal Carotid Artery, there was a significantly higher thickness among patients with hypertension as compared to the control group with p=0.011, p=0.002, and p=0.039 respectively.Conclusion: Increased CIMT is most likely associated with underlying chronic diseases. Ultrasound is a non-invasive, easily available and useful modality for early detection and prevention of vascular atherosclerosis

Potential of Indigenous Plants for Skin Healing and Care

The outer protective layer of body is skin which not only guards it from external fluctuations and effects but also performs its thermoregulation. Its functioning may get affected due to several factors like dermal wounds, injuries, aging and many other disorders. These dermal ailments can be cured with the help of indigenous flora to get economical pharamcognosal benefits with no side effects which is a serious concern of synthetic drugs now days. Furthermore, research efforts are necessary for their proper dose optimization and administration to achieve low cost and side effects free pharamcognosal skin cure and care gains

An efficient hybrid approach for optimization using simulated annealing and grasshopper algorithm for IoT applications

Abstract The multi-objective grasshopper optimization algorithm (MOGOA) is a relatively new algorithm inspired by the collective behavior of grasshoppers, which aims to solve multi-objective optimization problems in IoT applications. In order to enhance its performance and improve global convergence speed, the algorithm integrates simulated annealing (SA). Simulated annealing is a metaheuristic algorithm that is commonly used to improve the search capability of optimization algorithms. In the case of MOGOA, simulated annealing is integrated by employing symmetric perturbation to control the movement of grasshoppers. This helps in effectively balancing exploration and exploitation, leading to better convergence and improved performance. The paper proposes two hybrid algorithms based on MOGOA, which utilize simulated annealing for solving multi-objective optimization problems. One of these hybrid algorithms combines chaotic maps with simulated annealing and MOGOA. The purpose of incorporating simulated annealing and chaotic maps is to address the issue of slow convergence and enhance exploitation by searching high-quality regions identified by MOGOA. Experimental evaluations were conducted on thirteen different benchmark functions to assess the performance of the proposed algorithms. The results demonstrated that the introduction of simulated annealing significantly improved the convergence of MOGOA. Specifically, the IDG (Inverse Distance Generational distance) values for benchmark functions ZDT1, ZDT2, and ZDT3 were smaller than the IDG values obtained by using MOGOA alone, indicating better performance in terms of convergence. Overall, the proposed algorithms exhibit promise in solving multi-objective optimization problems

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user’s choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scene

Deep Learning-Based Feature Engineering to Detect Anterior and Inferior Myocardial Infarction Using UWB Radar Data

Cardiovascular disease is the main cause of death worldwide. The World Health Organization (WHO) reports that 17.9 million individuals die yearly due to complications from heart disease and other heart-related ailments. ECG monitoring and early detection are critical to decreasing myocardial infarction (MI) mortality. Thus, a non-invasive method to accurately classify different types of MI would be extremely beneficial. Our proposed study aims to detect and classify Anterior and Inferior MI infarction with advanced deep and machine learning techniques. A newly created UWB radar signal-based image dataset is used to conduct our study experiments. A novel Convolutional spatial Feature Engineering (CSFE) technique is proposed to extract the spatial features from the image dataset. The spatial features consist of both spatial and temporal information which allows machine learning models to leverage both the spatial and temporal relationships present in the data. Study results show that using the proposed CSFE technique, the advanced machine learning techniques achieved high-performance accuracy scores. The K-Neighbors Classifier (KNC) outperformed with a high-performance accuracy score of 98% for detecting Anterior and Inferior patients. The applied methods are fully hyperparametric tuned, and performance is validated using the k-fold cross-validation method

Influence of Bacterial Contamination and Antibiotic Sensitivity on Cryopreserved Sperm Quality of Indian Red Jungle Fowl

Aims: Bacterial contamination may occur in feces during collection and processing of semen. Bacteria not only compete for nutrients with spermatozoa but also produce toxic metabolites and endotoxins and affect sperm quality. The aim of the present study was to investigate the effect of antibiotic supplementation on the sperm quality of Indian red jungle fowl, estimation and isolation of bacterial species and their antibiotic sensitivity. Materials and Methods: Semen was collected and initially evaluated, diluted, and divided into six experimental extenders containing gentamicin (2.5 μg/mL), kanamycin (31.2 μg/mL), neomycin (62.5 mg/mL), penicillin (200 U/mL), and streptomycin (250 μg/mL), and a control having no antibiotics were cryopreserved and semen quality was evaluated at post-dilution, post-cooling, post-equilibration, and post-thawing stages (Experiment 1). A total aerobic bacterial count was carried out after culturing bacteria (Experiment 2) and subcultured for antibiotic sensitivity (Experiment 3). Results: It was shown that penicillin-containing extender improved semen quality (sperm motility, plasma membrane integrity, viability, and acrosomal integrity) compared with the control and other extenders having antibiotics. The bacteria isolated from semen were Escherichia coli, Staphylococcus spp., and Bacillus spp. Antibiotic sensitivity results revealed that E. coli shows high sensitivity toward neomycin, kanamycin, and penicillin. Staphylococcus spp. shows high sensitivity toward streptomycin, neomycin, and penicillin. Bacillus spp. shows high sensitivity toward kanamycin and penicillin. Conclusions: It was concluded that antibiotics added to semen extender did not cause any toxicity and maintained semen quality as that of untreated control samples, and penicillin was identified as most effective antibiotic. It is recommended that penicillin can be added to the semen extender for control of bacterial contamination without affecting the semen quality of Indian red jungle fowl.Peer reviewe

Analysis of receptor binding domain for possible mutations in S gene region of SARS-CoV-2

Humanity has historically been affected by remarkable epidemics and pandemics, including the plague, cholera, influenza, SARS-CoV, and MERS-CoV. A novel coronavirus pandemic known as SARS-CoV-2 is rapidly sweeping the globe. Over the period, the genome of the novel coronavirus has been mutated as it passes through its primary host. The world is reporting multiple point mutations. So the objective of this study was to observe modifications in the partial region of SARS-CoV-2 for the surveillance. This cross sectional study was carried out to detect the modifications in the SARS-CoV-2. For this purpose initial screening of COVID-19 was done to collect the strain of SARS-CoV-2 using RT-PCR. Then, primers were created in order to amplify the area using the S gene of the SARS-CoV-2. The amplified product was sent for the sequencing and bioinformatic tools were used to observed the mutations and data was compared with the Wild strain of the Virus. During the analysis, one of the most important point mutation was D614G caused by the amino acid substitution of aspartic acid with the glycine. Addition to this point mutation, other important mutations have also been observed

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach