2771-1463ISSN (Online)
2771-1471ISSN (Print)
Cardiovascular disease has been the major cause of mortality worldwide for last several decades. Diagnosis of heart disease through traditional approaches is a complex, time consuming and error prone process. To address this issue, several techniques have been proposed to automate the process of diagnosing the heart disease accurately in timely manner. However these techniques report limited accuracy of diagnosing the disease. In this paper SAFARI algorithm is used to diagnose the heart disease. Safari uses rule based approach i.e. it extracts rules from a dataset and uses the extracted rules for diagnosis. The various attribute values are first discretised into specific ranges, each range corresponds to a symbol. This results in a symbol table. Safari extracts rules from this symbol table. The approach has been thoroughly tested on the heart disease dataset publicly available on UCI machine learning repository. The results obtained using this approach are compared with the results of various techniques reported by other authors, a significant improvement was observed.
Read MoreDoi: https://doi.org/10.54216/JCHCI.070201
Vol. 7 Issue. 2 PP. 08-16, (2024)
This research aims to detect human emotions using speech signals through the development and implementation of methodologies, namely the frequency domain synthesis. To achieve improved results, various machine learning and deep learning models were applied for implementation and their resulting model performance was analyzed. The research findings revealed that each model exhibited different accuracy rates for different emotions but weighted accuracy is best for deep learning based model. This study provides valuable insights into the feasibility and effectiveness of utilizing different methodologies and models for emotion detection through voice signals synthesis. The audio signals are synthesized for Mel-Frequency Cestrum Coefficients (MFCC), Chroma, and MEL characteristics, which are then used as features to train the various machine learning-based classifiers. Python libraries like Librosa, Sklearn, Pyaudio, Numpy, and sound files are used to analyze voice modulations and identify emotions.
Read MoreDoi: https://doi.org/10.54216/JCHCI.070202
Vol. 7 Issue. 2 PP. 17-26, (2024)
Emission of carbon footprints plays a major role in climate change and hence, the world is moving towards sustainable energy-based solutions. This paper investigates challenges in classroom environments, focusing on illuminance levels, indoor air quality, and temperature. The study introduces methodologies to enhance educational spaces, emphasizing advanced lighting for optimal illumination, addressing indoor air quality and efficient temperature regulation. Thereby aiming to create visually conducive environments, promoting concentration, and learning effectiveness. The research contributes to nurturing students' intellectual growth and well-being through sustainability.
Read MoreDoi: https://doi.org/10.54216/JCHCI.070203
Vol. 7 Issue. 2 PP. 27-36, (2024)
"Detecting Counterfeit currency with Image Processing" focuses on leveraging image processing techniques to identify counterfeit currency. Currency plays a crucial role in economic transactions, functioning as a means of trade, standard measure of value, and reservoir of wealth. Ensuring the integrity of currency is crucial for maintaining trust in financial systems, preventing economic disruptions, and protecting individuals and businesses from financial losses. The need for currency detection arises in the situation of counterfeit activities, which pose serious threats to the stability of economy. Counterfeit currency can lead to financial fraud, loss of confidence in monetary systems, and can negatively impact businesses and individuals. By employing efficient image processing algorithms, this paper aims to enhance the accuracy and efficiency of counterfeit currency detection, providing a robust tool for financial institutions, businesses, and law enforcement agencies to safeguard against economic threats.
Read MoreDoi: https://doi.org/10.54216/JCHCI.070204
Vol. 7 Issue. 2 PP. 37-49, (2024)
The development of smart health monitoring systems has emerged as a consequence of the integration of Internet of Things (IoT) and Machine Learning (ML) technologies within the healthcare sector. This transformation has significantly reshaped patient care methodologies, shifting from traditional approaches to electronic healthcare systems. Leveraging IoT technology fosters a contemporary medical device ecosystem, fostering seamless communication among healthcare professionals, patients, and medical devices. Through the deployment of IoT devices, encompassing sensors and transmitters, coupled with Machine Learning algorithms, various applications have arisen, spanning from remote patient monitoring to real-time health assessment during ambulance transit to medical facilities. This proposed framework aims to monitor essential physiological parameters including body temperature, blood pressure, heart rate, sweat analysis, glucose levels, ECG, EEG, and pulse oximetry, transmitting pertinent data for tailored processing and analysis. Implantable IoT devices serve as conduits for wireless communication, data storage, centralized computation, and portable processing, facilitating connectivity among sensors, GPS-enabled ambulances, medical practitioners, and patients. To mitigate potential health risks, sensors are equipped with Machine Learning capabilities to promptly assess illness severity and recommend appropriate interventions, potentially triggering automated alerts to healthcare providers. This seamless exchange of information via IoT and wireless networks enables rapid communication between doctors and patients, facilitating personalized medical recommendations, prescription management, and hospital selection based on individual health profiles.
Read MoreDoi: https://doi.org/10.54216/JCHCI.070205
Vol. 7 Issue. 2 PP. 50-59, (2024)