KIET Journal of Computing and Information Sciences
This paper discusses a new evolution in the healthcare sector through a device by investigating t... more This paper discusses a new evolution in the healthcare sector through a device by investigating the principle application of Artificial Neural Networks (ANN) for the selection of an optimal benefactor-donor match in organ transplantation. The device aims to correlate ABO blood type, age and bone density of healthy subjects. Firstly, linker phase integrates a light intensity(lux)meter and an RGB Color Sensor module to perform an experimental observation of agglutination of RBC's which is measured through a halogen illumination source that measures the light intensity which is displayed on a screen through the icroprocessor interface. Secondly, we aim to study the possibility of calcium quantification via near-infrared spectroscopy to estimate bone density which involves the use of an emitting source and a photodiode as a detector/receiver. At last the device involves designing an Artificial Neural Network (ANN) model through the Neural Network Toolbox of MATLAB software to get th...
Surface electromyography (EMG) has emerged as a promising clisnical decision support system, enab... more Surface electromyography (EMG) has emerged as a promising clisnical decision support system, enabling the extraction of muscles' electrical activity through non-invasive devices placed on the body. This study focuses on the application of machine learning (ML) techniques to preprocess and analyze EMG signals for the detection of muscle abnormalities. Notably, state-of-the-art ML algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Random Forests (RF), and Naive Bayes (NB), have been harnessed by researchers in the biomedical sciences to achieve accurate surface EMG signal detection. Within this paper, we present a meticulously conducted systematic review, employing the PRISMA method to select relevant research papers. Various databases were thoroughly searched, and multiple pertinent studies were identified for detailed examination, weighing their respective merits and drawbacks. Our survey comprehensively elucidates t...
Communication between normal people and deaf people is the most difficult part of daily life worl... more Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from...
Human beings rely heavily on social communication as one of the major aspects of communication. L... more Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used. According to the World Federation of the Deaf, there are about 70 million deaf people present around the globe and about 300 sign languages being used. Hence, the structural form of the hand gestures involving visual motions and signs is used as a communication system to help the deaf and speech-impaired community for daily interaction. The aim is to collect a dataset of Urdu sign language (USL) and test it through a machine learning classifier. The overview of the proposed system is divided into four main stages i.e., data collection, data acquisition, training model ad testing model. The USL dataset which is comprised of 1,560 images was created by photographing various hand positio...
Background and Objective Humans communicate with one another using language systems such as writt... more Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. Materials and methods All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar...
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
The COVID-19 pandemic has triggered instabilities in various aspects of daily life. This includes... more The COVID-19 pandemic has triggered instabilities in various aspects of daily life. This includes economic, social, financial, and health crisis. In addition, the COVID-19 pandemic with the evolution of different virus strains such as delta and omicron has led to frequent global lockdowns. These lockdowns have caused disruption of trade activities that in turn have led to the shortage of medical supplies, especially personal protective equipment’s (PPE’s). Health-care workers (HCW’s) have been at the forefront of the fight against this pandemic and are responsible for saving millions of lives worldwide. However, the PPE’s available to HCW’s in the form of face shields and face masks only provide face and eye protection without encapsulating the ability to continuously monitor vital COVID-19 parameters including body temperature, heart rate, and SpO2. Hence, in this study, we propose the design and utilization of a PPE in the form of smart face shield. The device has been integrated ...
Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-... more Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and pri...
TENCON 2018 - 2018 IEEE Region 10 Conference, 2018
In contemporary world, technology has made its way into human lives and made it significantly eas... more In contemporary world, technology has made its way into human lives and made it significantly easier. Life seems impossible without human-computer interaction and each day brings innovative ways to improve our lives further. People born with physical impairments are subject to low self-esteem and special treatment. Such people need to be sensitively handled and groomed to be an independent member of the society. The project deals with those people who are by-birth hearing impaired. They have not heard any kind of voice, so they may not be able to speak as well but may have the capability to do so. So, to facilitate them and make them blend in the society a new device is made using haptics. According to a study, due to adaptive nature of brain, the hearing-impaired person can learn to listen by feeling the vibrations [1]. Work has been done in this respect to feel the music i.e. audio to vibe conversion [2]. It’s taken a step forward to the area of speech therapy. A training system i...
Diagnosis on the basis of a computerized acoustic examination may play an incredibly important ro... more Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package.
Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfun... more Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfunctioning. In medical science, novel machine learning algorithms are applied to construct a system to identify disorders that occur invoice. This study aims to extract the features from the audio signals of four chosen diseases from the SVD dataset, such as laryngitis, cyst, non-fluency syndrome, and dysphonia, and then compare the four results of machine learning algorithms, i.e., SVM, Naïve Byes, decision tree and ensemble classifier. In this project, we have used a comparative approach along with the new combination of features to detect voice pathologies which are laryngitis, cyst, non-fluency syndrome, and dysphonia from the SVD dataset. The combination of specific 13 MFCC (mel-frequency cepstral coefficients) features along with pitch, zero crossing rate (ZCR), spectral flux, spectral entropy, spectral centroid, spectral roll-off, and short term energy for more accurate detection of voice pathologies. It is proven that the combination of features extracted gives the best product on the audio, which split into 10 ms. Four machine learning classifiers, SVM, Naïve Bayes, decision tree and ensemble classifier for the inter classifier comparison, give 93.18, 99.45,100 and 51%, respectively. Out of these accuracies, both Naïve Bayes and the decision tree show the most promising results with a higher detection rate. Naïve Bayes and decision tree gives the highest reported outcomes on the selected set of features in the proposed methodology. The SVM has also been concluded to be the commonly used voice condition identification algorithm.
KIET Journal of Computing and Information Sciences
This paper discusses a new evolution in the healthcare sector through a device by investigating t... more This paper discusses a new evolution in the healthcare sector through a device by investigating the principle application of Artificial Neural Networks (ANN) for the selection of an optimal benefactor-donor match in organ transplantation. The device aims to correlate ABO blood type, age and bone density of healthy subjects. Firstly, linker phase integrates a light intensity(lux)meter and an RGB Color Sensor module to perform an experimental observation of agglutination of RBC's which is measured through a halogen illumination source that measures the light intensity which is displayed on a screen through the icroprocessor interface. Secondly, we aim to study the possibility of calcium quantification via near-infrared spectroscopy to estimate bone density which involves the use of an emitting source and a photodiode as a detector/receiver. At last the device involves designing an Artificial Neural Network (ANN) model through the Neural Network Toolbox of MATLAB software to get th...
Surface electromyography (EMG) has emerged as a promising clisnical decision support system, enab... more Surface electromyography (EMG) has emerged as a promising clisnical decision support system, enabling the extraction of muscles' electrical activity through non-invasive devices placed on the body. This study focuses on the application of machine learning (ML) techniques to preprocess and analyze EMG signals for the detection of muscle abnormalities. Notably, state-of-the-art ML algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Random Forests (RF), and Naive Bayes (NB), have been harnessed by researchers in the biomedical sciences to achieve accurate surface EMG signal detection. Within this paper, we present a meticulously conducted systematic review, employing the PRISMA method to select relevant research papers. Various databases were thoroughly searched, and multiple pertinent studies were identified for detailed examination, weighing their respective merits and drawbacks. Our survey comprehensively elucidates t...
Communication between normal people and deaf people is the most difficult part of daily life worl... more Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from...
Human beings rely heavily on social communication as one of the major aspects of communication. L... more Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used. According to the World Federation of the Deaf, there are about 70 million deaf people present around the globe and about 300 sign languages being used. Hence, the structural form of the hand gestures involving visual motions and signs is used as a communication system to help the deaf and speech-impaired community for daily interaction. The aim is to collect a dataset of Urdu sign language (USL) and test it through a machine learning classifier. The overview of the proposed system is divided into four main stages i.e., data collection, data acquisition, training model ad testing model. The USL dataset which is comprised of 1,560 images was created by photographing various hand positio...
Background and Objective Humans communicate with one another using language systems such as writt... more Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. Materials and methods All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar...
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
The COVID-19 pandemic has triggered instabilities in various aspects of daily life. This includes... more The COVID-19 pandemic has triggered instabilities in various aspects of daily life. This includes economic, social, financial, and health crisis. In addition, the COVID-19 pandemic with the evolution of different virus strains such as delta and omicron has led to frequent global lockdowns. These lockdowns have caused disruption of trade activities that in turn have led to the shortage of medical supplies, especially personal protective equipment’s (PPE’s). Health-care workers (HCW’s) have been at the forefront of the fight against this pandemic and are responsible for saving millions of lives worldwide. However, the PPE’s available to HCW’s in the form of face shields and face masks only provide face and eye protection without encapsulating the ability to continuously monitor vital COVID-19 parameters including body temperature, heart rate, and SpO2. Hence, in this study, we propose the design and utilization of a PPE in the form of smart face shield. The device has been integrated ...
Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-... more Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and pri...
TENCON 2018 - 2018 IEEE Region 10 Conference, 2018
In contemporary world, technology has made its way into human lives and made it significantly eas... more In contemporary world, technology has made its way into human lives and made it significantly easier. Life seems impossible without human-computer interaction and each day brings innovative ways to improve our lives further. People born with physical impairments are subject to low self-esteem and special treatment. Such people need to be sensitively handled and groomed to be an independent member of the society. The project deals with those people who are by-birth hearing impaired. They have not heard any kind of voice, so they may not be able to speak as well but may have the capability to do so. So, to facilitate them and make them blend in the society a new device is made using haptics. According to a study, due to adaptive nature of brain, the hearing-impaired person can learn to listen by feeling the vibrations [1]. Work has been done in this respect to feel the music i.e. audio to vibe conversion [2]. It’s taken a step forward to the area of speech therapy. A training system i...
Diagnosis on the basis of a computerized acoustic examination may play an incredibly important ro... more Diagnosis on the basis of a computerized acoustic examination may play an incredibly important role in early diagnosis and in monitoring and even improving effective pathological speech diagnostics. Various acoustic metrics test the health of the voice. The precision of these parameters also has to do with algorithms for the detection of speech noise. The idea is to detect the disease pathology from the voice. First, we apply the feature extraction on the SVD dataset. After the feature extraction, the system input goes into the 27 neuronal layer neural networks that are convolutional and recurrent neural network. We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. A 10-fold cross-validation is used to evaluate the performance of the classifier. On a Linux workstation with one NVidia Titan X GPU, program code was written in Python using the TensorFlow package.
Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfun... more Voice pathologies are irregular vibrations produced due to vocal folds and various factors malfunctioning. In medical science, novel machine learning algorithms are applied to construct a system to identify disorders that occur invoice. This study aims to extract the features from the audio signals of four chosen diseases from the SVD dataset, such as laryngitis, cyst, non-fluency syndrome, and dysphonia, and then compare the four results of machine learning algorithms, i.e., SVM, Naïve Byes, decision tree and ensemble classifier. In this project, we have used a comparative approach along with the new combination of features to detect voice pathologies which are laryngitis, cyst, non-fluency syndrome, and dysphonia from the SVD dataset. The combination of specific 13 MFCC (mel-frequency cepstral coefficients) features along with pitch, zero crossing rate (ZCR), spectral flux, spectral entropy, spectral centroid, spectral roll-off, and short term energy for more accurate detection of voice pathologies. It is proven that the combination of features extracted gives the best product on the audio, which split into 10 ms. Four machine learning classifiers, SVM, Naïve Bayes, decision tree and ensemble classifier for the inter classifier comparison, give 93.18, 99.45,100 and 51%, respectively. Out of these accuracies, both Naïve Bayes and the decision tree show the most promising results with a higher detection rate. Naïve Bayes and decision tree gives the highest reported outcomes on the selected set of features in the proposed methodology. The SVM has also been concluded to be the commonly used voice condition identification algorithm.
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Papers by HIRA ZAHID