Pub Date : 2023-01-04DOI: 10.37082/ijirmps.v11.i1.230318
Shaik Tousif -, Abdul Saboor -, Syed Saffwan Ahmed -, Sumayya Begum -
The development in computer vision has aided drivers in the form of automatic self-driving cars etc. The accidents are caused by driver's exhaustion and drowsiness about 20%. Its carriages a dangerous issue for which numerous methods were proposed. However, they are not appropriate for real-time implementation. The major encounters confronted by these approaches are forcefulness to handle dissimilarity in human face and lightning conditions. Our intention is to implement a smart operating system that can lower the rate of road accidents considerably. This method enables us to find driver's face features like eye closure percentage, eye-mouth aspect ratios, blink rate, yawning, head movement, etc. In this classification, the driver is uninterruptedly observed by using a webcam. The car driver’s facial features along with the eye movements are observed using a cascade classifier. Eye images are pull out and fed to Custom designed Convolutional Neural Network for categorizing whether both left and right eye are closed. Based on the sorting, the eye closure score is considered. Upon finding that the driver is being detected drowsy that a high alarm will be raised.
{"title":"Detecting Driver Sleepiness using Convolutional Neural Networks","authors":"Shaik Tousif -, Abdul Saboor -, Syed Saffwan Ahmed -, Sumayya Begum -","doi":"10.37082/ijirmps.v11.i1.230318","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230318","url":null,"abstract":"The development in computer vision has aided drivers in the form of automatic self-driving cars etc. The accidents are caused by driver's exhaustion and drowsiness about 20%. Its carriages a dangerous issue for which numerous methods were proposed. However, they are not appropriate for real-time implementation. The major encounters confronted by these approaches are forcefulness to handle dissimilarity in human face and lightning conditions. Our intention is to implement a smart operating system that can lower the rate of road accidents considerably. This method enables us to find driver's face features like eye closure percentage, eye-mouth aspect ratios, blink rate, yawning, head movement, etc. In this classification, the driver is uninterruptedly observed by using a webcam. The car driver’s facial features along with the eye movements are observed using a cascade classifier. Eye images are pull out and fed to Custom designed Convolutional Neural Network for categorizing whether both left and right eye are closed. Based on the sorting, the eye closure score is considered. Upon finding that the driver is being detected drowsy that a high alarm will be raised.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135500967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-04DOI: 10.37082/ijirmps.v11.i1.230316
Rasha Anjum -, Amatun Noor Sadaf -, Maheen Sami -, Kamel Alikhan Siddiqui -
Non-verbal communication involves the usage of Sign Language. The sign language is used by people with hearing / speech disabilities to express their thoughts and feelings. But normally, people find it difficult to understand the hand gestures of the specially challenged people as they do not know the meaning of the sign language gestures. Usually, a translator is needed when a speech / hearing impaired person wants to communicate with an ordinary person and vice versa. In order to enable the specially challenged people to effectively communicate with the people around them, a system that translates the Indian Sign Language (ISL) hand gestures of numbers (1-9), English alphabets (A-Z) and a few English words to understandable text and vice versa has been proposed in this paper. This is done using image processing techniques and Machine Learning algorithms. Different neural network classifiers are developed, tested and validated for their performance in gesture recognition and the most efficient classifier is identified.
{"title":"An Efficient Approach for Interpretation of Indian Sign Language using Machine Learning","authors":"Rasha Anjum -, Amatun Noor Sadaf -, Maheen Sami -, Kamel Alikhan Siddiqui -","doi":"10.37082/ijirmps.v11.i1.230316","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230316","url":null,"abstract":"Non-verbal communication involves the usage of Sign Language. The sign language is used by people with hearing / speech disabilities to express their thoughts and feelings. But normally, people find it difficult to understand the hand gestures of the specially challenged people as they do not know the meaning of the sign language gestures. Usually, a translator is needed when a speech / hearing impaired person wants to communicate with an ordinary person and vice versa. In order to enable the specially challenged people to effectively communicate with the people around them, a system that translates the Indian Sign Language (ISL) hand gestures of numbers (1-9), English alphabets (A-Z) and a few English words to understandable text and vice versa has been proposed in this paper. This is done using image processing techniques and Machine Learning algorithms. Different neural network classifiers are developed, tested and validated for their performance in gesture recognition and the most efficient classifier is identified.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135500968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-04DOI: 10.37082/ijirmps.v11.i1.230317
Mohammed Obaid -, Md. Salman Areeb -, Mir Wajahat Ali Khan -, Batchu Nagalakshmi -, Kadime Deepthi -
Online media cooperation particularly the word getting out around the organization is an incredible wellspring of data these days. From one's point of view, its insignificant effort, direct access, and speedy scattering of data that lead individuals to watch out and global news from web sites. Twitter being a champion among the most notable progressing news sources moreover winds up a champion among the most prevailing news emanating mediums. It is known to cause broad damage by spreading pieces of tattle beforehand. Therefore, motorizing fake news acknowledgment is rudimentary to keep up healthy online media and casual association. We proposes a model for perceiving manufactured news messages from twitter posts, by making sense of how to envision exactness examinations, considering automating fashioned news distinguishing proof in Twitter datasets. Subsequently, we played out a correlation between five notable Machine Learning calculations, similar to Support Vector Machine, Naïve Bayes Method, Logistic Regression and Recurrent Neural Network models, independently to exhibit the effectiveness of the grouping execution on the dataset. Our exploratory outcome indicated that SVM and Naïve Bayes classifier beats different calculation.
{"title":"Detecting Fake News Using Machine Learning","authors":"Mohammed Obaid -, Md. Salman Areeb -, Mir Wajahat Ali Khan -, Batchu Nagalakshmi -, Kadime Deepthi -","doi":"10.37082/ijirmps.v11.i1.230317","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230317","url":null,"abstract":"Online media cooperation particularly the word getting out around the organization is an incredible wellspring of data these days. From one's point of view, its insignificant effort, direct access, and speedy scattering of data that lead individuals to watch out and global news from web sites. Twitter being a champion among the most notable progressing news sources moreover winds up a champion among the most prevailing news emanating mediums. It is known to cause broad damage by spreading pieces of tattle beforehand. Therefore, motorizing fake news acknowledgment is rudimentary to keep up healthy online media and casual association. We proposes a model for perceiving manufactured news messages from twitter posts, by making sense of how to envision exactness examinations, considering automating fashioned news distinguishing proof in Twitter datasets. Subsequently, we played out a correlation between five notable Machine Learning calculations, similar to Support Vector Machine, Naïve Bayes Method, Logistic Regression and Recurrent Neural Network models, independently to exhibit the effectiveness of the grouping execution on the dataset. Our exploratory outcome indicated that SVM and Naïve Bayes classifier beats different calculation.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135500972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a brand-new feature called Exam (Infinity Exam) that supports paper-based exams and speeds up the entire process while maintaining all of their beneficial qualities and minimizing their drawbacks, notably in higher education. The method is very different from those employed in the earlier 10+ years, which were implemented in a way that prevented them from replicating and supplanting the conventional paper-based examination format. The article's core relies on the image processing flow, which is the most crucial component of the software. Multiple Choice Questions (MCQ) have been a more common method of testing someone's knowledge over time. The use of multiple choice questions in exams is becoming more widespread in the education sector (including in schools and colleges). It is employed even when conducting interviews. The current scenario involves either manually correcting the test or using OMR technology. Having OMR at all times in real time is rather challenging, and manually correcting it takes a lot of effort and could result in a mistake. We address this issue by applying a digital image processing technique in our proposed system to correct the response using multiple-choice questions written in Python. Here, we are processing data using Open-Source Computer Vision Library (OpenCV).
{"title":"A Novel Approach for Exam E-assessment Utilizing Image Processing","authors":"Mohd. Samee Khan -, Mudasir Patel -, Syed Idris Hussaini -, Neha Hasan -","doi":"10.37082/ijirmps.v11.i1.230313","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230313","url":null,"abstract":"There is a brand-new feature called Exam (Infinity Exam) that supports paper-based exams and speeds up the entire process while maintaining all of their beneficial qualities and minimizing their drawbacks, notably in higher education. The method is very different from those employed in the earlier 10+ years, which were implemented in a way that prevented them from replicating and supplanting the conventional paper-based examination format. The article's core relies on the image processing flow, which is the most crucial component of the software. Multiple Choice Questions (MCQ) have been a more common method of testing someone's knowledge over time. The use of multiple choice questions in exams is becoming more widespread in the education sector (including in schools and colleges). It is employed even when conducting interviews. The current scenario involves either manually correcting the test or using OMR technology. Having OMR at all times in real time is rather challenging, and manually correcting it takes a lot of effort and could result in a mistake. We address this issue by applying a digital image processing technique in our proposed system to correct the response using multiple-choice questions written in Python. Here, we are processing data using Open-Source Computer Vision Library (OpenCV).","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135500969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-04DOI: 10.37082/ijirmps.v11.i1.230315
Syed Shoeb Ahmed -, Mohammed Sohaib Zahoor -, Syed Shadab -, M. Shilpa -
Understanding the effects of a pandemic on the public sentiment is an important challenge in the study of social dynamics during a global pandemic. This paper puts forward a case study that throws light on the psychological impact of the COVID-19 pandemic on the people living in the Indian subcontinent. The study is based on a pipeline that involves pre-processing, sentiment analysis, topic modelling, natural language processing and statistical analysis of Twitter data extracted in the form of tweets. The results demonstrate the effectiveness of this pipeline in understanding the temporal impact of the different lockdowns implemented in the span of the pandemic on the public sentiment, which can be useful for healthcare workers, authorities, and researchers.
{"title":"Leveraging Natural Language Processing Algorithms to Understand the Impact of the COVID","authors":"Syed Shoeb Ahmed -, Mohammed Sohaib Zahoor -, Syed Shadab -, M. Shilpa -","doi":"10.37082/ijirmps.v11.i1.230315","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230315","url":null,"abstract":"Understanding the effects of a pandemic on the public sentiment is an important challenge in the study of social dynamics during a global pandemic. This paper puts forward a case study that throws light on the psychological impact of the COVID-19 pandemic on the people living in the Indian subcontinent. The study is based on a pipeline that involves pre-processing, sentiment analysis, topic modelling, natural language processing and statistical analysis of Twitter data extracted in the form of tweets. The results demonstrate the effectiveness of this pipeline in understanding the temporal impact of the different lockdowns implemented in the span of the pandemic on the public sentiment, which can be useful for healthcare workers, authorities, and researchers.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135500971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-04DOI: 10.37082/ijirmps.v11.i1.230314
Abdul Rahman Bin Salam -, Ibaad Mohammed Hameeduddin -, Mohammed Faizan Hussain -, Hajira Sabuhi -
Artificial intelligence and machine learning are increasingly being applied in medicine, particularly in biomedical imaging and diagnostic procedures. Machine learning algorithms are being used to process chest X-ray images, enhancing consistency and accuracy in reporting. The research focuses on using deep learning algorithms based on convolutional neural networks to build a processing model for detecting pneumonia-related changes in chest X-rays and classifying them into two groups based on detection results. This approach aims to improve decision-making and accuracy in medical imaging.
{"title":"Pneumonia Detection System Using Deep Learning","authors":"Abdul Rahman Bin Salam -, Ibaad Mohammed Hameeduddin -, Mohammed Faizan Hussain -, Hajira Sabuhi -","doi":"10.37082/ijirmps.v11.i1.230314","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230314","url":null,"abstract":"Artificial intelligence and machine learning are increasingly being applied in medicine, particularly in biomedical imaging and diagnostic procedures. Machine learning algorithms are being used to process chest X-ray images, enhancing consistency and accuracy in reporting. The research focuses on using deep learning algorithms based on convolutional neural networks to build a processing model for detecting pneumonia-related changes in chest X-rays and classifying them into two groups based on detection results. This approach aims to improve decision-making and accuracy in medical imaging.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"334 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135500970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the key sectors of the national economy is the auto industry. Cars are becoming more and more common as a form of private transportation. When a buyer wants to purchase the ideal vehicle, particularly a car, an evaluation is necessary. Because it is an expensive vehicle, there are a lot of conditions and elements to consider before buying a new one, including price, headlamp, cylinder volume, and spare parts. Therefore, it is crucial for the consumer to choose a purchase that can meet all of the criteria before making any other decisions. In our research, we therefore suggest various well-known methods to improve accuracy for a car purchase. These algorithms were used on our dataset, which consists of 50 data. With a prediction accuracy of 86.7%, Support Vector Machine (SVM) produces the best result of the bunch. In this study, we also present comparison findings for all data samples using various methods for precision, recall, and F1 score.
{"title":"Prediction of Car Purchase based on User Demands using Supervised Machine Learning","authors":"Mohd. Samee Uddin -, Rabab Fatima Hussain -, Asfiya Samreen -, Saleha Butool -","doi":"10.37082/ijirmps.v11.i1.230312","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230312","url":null,"abstract":"One of the key sectors of the national economy is the auto industry. Cars are becoming more and more common as a form of private transportation. When a buyer wants to purchase the ideal vehicle, particularly a car, an evaluation is necessary. Because it is an expensive vehicle, there are a lot of conditions and elements to consider before buying a new one, including price, headlamp, cylinder volume, and spare parts. Therefore, it is crucial for the consumer to choose a purchase that can meet all of the criteria before making any other decisions. In our research, we therefore suggest various well-known methods to improve accuracy for a car purchase. These algorithms were used on our dataset, which consists of 50 data. With a prediction accuracy of 86.7%, Support Vector Machine (SVM) produces the best result of the bunch. In this study, we also present comparison findings for all data samples using various methods for precision, recall, and F1 score.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.37082/ijirmps.v11.i1.230311
Mohammed Aslam Khan -, Mohd. Yaseen Ahmed -, Syed Safadar Hussain -, Khutaija Abid -
The success of machine learning algorithms in medical imaging has boosted the demand for models that have been artificially trained to function more rapidly and effectively in the medical profession. In this paper, a method for identifying bone fractures using machine learning algorithms is presented, which can help to lighten the workload of orthopedics. Instead of spending hours in radiology departments, the substantial application of machine learning in this era of huge medical data will make it possible to obtain information from the available X-ray images. The imaging techniques described in this study can quickly determine whether a bone fracture has occurred in a human body after an X-ray has been obtained.
{"title":"Machine Learning for the Identification of Bone Deformities","authors":"Mohammed Aslam Khan -, Mohd. Yaseen Ahmed -, Syed Safadar Hussain -, Khutaija Abid -","doi":"10.37082/ijirmps.v11.i1.230311","DOIUrl":"https://doi.org/10.37082/ijirmps.v11.i1.230311","url":null,"abstract":"The success of machine learning algorithms in medical imaging has boosted the demand for models that have been artificially trained to function more rapidly and effectively in the medical profession. In this paper, a method for identifying bone fractures using machine learning algorithms is presented, which can help to lighten the workload of orthopedics. Instead of spending hours in radiology departments, the substantial application of machine learning in this era of huge medical data will make it possible to obtain information from the available X-ray images. The imaging techniques described in this study can quickly determine whether a bone fracture has occurred in a human body after an X-ray has been obtained.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"50 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135799631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-25DOI: 10.37082/ijirmps.2022.v10i01.003
Chirontan Bhuyan, Anurag Gogoi
Exoskeleton can be defined as "wearable, external mechanical structure" whose objective is to reinforce or restore the physical performance of the person. The exoskeletons are placed on the user’s body and can be classified into two categories: • Passive: This kind of exoskeleton does not use any type of electrical power source. On the other hand, these are constituted with mechanisms as springs, dampers or high-pressure springs. It can be used for weight re-distribution or energy capture to support the users with the posture or motion. • Active: unlike passive exoskeletons, active exoskeletons do use some type of actuator which in- creases human power. This actuator can be an electric motor, pneumatic muscles or hydraulic power.
{"title":"Designing of Capio-active, Lower Extremity and Brain Energized Full Body Exoskeleton with IoT Edge for Assisting Paralyzed Patients","authors":"Chirontan Bhuyan, Anurag Gogoi","doi":"10.37082/ijirmps.2022.v10i01.003","DOIUrl":"https://doi.org/10.37082/ijirmps.2022.v10i01.003","url":null,"abstract":"Exoskeleton can be defined as \"wearable, external mechanical structure\" whose objective is to reinforce or restore the physical performance of the person. The exoskeletons are placed on the user’s body and can be classified into two categories:\u0000\u0000• Passive: This kind of exoskeleton does not use any type of electrical power source. On the other hand, these are constituted with mechanisms as springs, dampers or high-pressure springs. It can be used for weight re-distribution or energy capture to support the users with the posture or motion.\u0000\u0000• Active: unlike passive exoskeletons, active exoskeletons do use some type of actuator which in- creases human power. This actuator can be an electric motor, pneumatic muscles or hydraulic power.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132610269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-12DOI: 10.37082/ijirmps.2022.v10i01.001
Laxman Thapa, R. Bhandari
Nepal is a Himalayan country that has surplus potential in hydropower generation. It lies among the largely populated countries such as India, China, Bangladesh and Pakistan. Nepal can be a country to fulfill its demand. However, for a few decades, Nepal has been suffering from domestic power shortages. This review study holds attention to intrinsic developmental barriers that stem from the domestic power supply, internal governance systems, and indigenous societal sensitivity. The barrier behind the unavailability of Nepal to export electric power is its insufficient production which is dragged by: energy treading policies, technical, environmental, economical, and financial factors, political and regulatory barriers, social and cultural barriers.
{"title":"A Review of Domestic Socio-economic Barriers on Hydroelectricity Trading in Nepal","authors":"Laxman Thapa, R. Bhandari","doi":"10.37082/ijirmps.2022.v10i01.001","DOIUrl":"https://doi.org/10.37082/ijirmps.2022.v10i01.001","url":null,"abstract":"Nepal is a Himalayan country that has surplus potential in hydropower generation. It lies among the largely populated countries such as India, China, Bangladesh and Pakistan. Nepal can be a country to fulfill its demand. However, for a few decades, Nepal has been suffering from domestic power shortages. This review study holds attention to intrinsic developmental barriers that stem from the domestic power supply, internal governance systems, and indigenous societal sensitivity. The barrier behind the unavailability of Nepal to export electric power is its insufficient production which is dragged by: energy treading policies, technical, environmental, economical, and financial factors, political and regulatory barriers, social and cultural barriers.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125740526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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