Pub Date : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099337
Aliya Anwar, Liu Dunnan, K. Jamil, Sohaib Mustafa, Fazal Hussain Awan, Usama Khalil Ansari
This study explores the role of green supply chain management (GSCM) in the sustainable performance of energy sector organizations in Pakistan. A survey method was used to collect data from the management of energy sector organizations. Results show that GSCM is a crucial element for the sustainable performance of energy organizations. This study provides some valuable insights to the higher management of energy sector organizations to adopt GSCM for sustainable performance.
{"title":"Sustainable Performance of Energy sector Organizations through Green Supply Chain Management","authors":"Aliya Anwar, Liu Dunnan, K. Jamil, Sohaib Mustafa, Fazal Hussain Awan, Usama Khalil Ansari","doi":"10.1109/iCoMET57998.2023.10099337","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099337","url":null,"abstract":"This study explores the role of green supply chain management (GSCM) in the sustainable performance of energy sector organizations in Pakistan. A survey method was used to collect data from the management of energy sector organizations. Results show that GSCM is a crucial element for the sustainable performance of energy organizations. This study provides some valuable insights to the higher management of energy sector organizations to adopt GSCM for sustainable performance.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114502110","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099207
M. A. Abbas, Muzaffar Hussain, S. Muhammad
Hydro-power project must take into consideration the design and performance analysis of hydraulic turbines to ensure the cost-effective and efficient operation of these facilities. Since the previous few decades, numerical hydrodynamic analysis of water turbines has brought about remarkable developments in the techniques. Data for this study is attained from CFD simulation. Both experimental research and computational fluid dynamics (CFD) have been utilized in specific situations to estimate the efficiency of water turbines by Pico scale. It is made to generate electricity for residential units. Which is a great alternative that can benefit from tiny avalanches and streams to increase electricity clearly in the use of hydro turbine systems. With the aid of the ANSYS CFX software and simulations performed on a modal designed in SolidWorks, the procedure of data collection was undertaken via simulations. The manufacturer's manual proposed updated values of solver configuration. Solver configuration put in which is recommended by the manufacturer's manuals. The boundary conditions were leveled in response with the current ongoing powerhouse data in District Kharmang of Gilgit Baltistan region. The obtained results of velocity and pressure is compared with the results published previously. It is concluded that when the rpm was 90, a simulation could only be run at 0.4 W with an angular velocity of 9.4 rad/s and a torque of 0.04 Nm. Additionally, the turbine was also run at rpm=60, producing 0.3W at 0.05 Nm of torque and an angular velocity of 6.28 rad/s. These findings are much be beneficial in the application mechanical engineering and chemical engineering.
{"title":"Design and Simulation for hydro turbine through Computational Fluid Dynamics","authors":"M. A. Abbas, Muzaffar Hussain, S. Muhammad","doi":"10.1109/iCoMET57998.2023.10099207","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099207","url":null,"abstract":"Hydro-power project must take into consideration the design and performance analysis of hydraulic turbines to ensure the cost-effective and efficient operation of these facilities. Since the previous few decades, numerical hydrodynamic analysis of water turbines has brought about remarkable developments in the techniques. Data for this study is attained from CFD simulation. Both experimental research and computational fluid dynamics (CFD) have been utilized in specific situations to estimate the efficiency of water turbines by Pico scale. It is made to generate electricity for residential units. Which is a great alternative that can benefit from tiny avalanches and streams to increase electricity clearly in the use of hydro turbine systems. With the aid of the ANSYS CFX software and simulations performed on a modal designed in SolidWorks, the procedure of data collection was undertaken via simulations. The manufacturer's manual proposed updated values of solver configuration. Solver configuration put in which is recommended by the manufacturer's manuals. The boundary conditions were leveled in response with the current ongoing powerhouse data in District Kharmang of Gilgit Baltistan region. The obtained results of velocity and pressure is compared with the results published previously. It is concluded that when the rpm was 90, a simulation could only be run at 0.4 W with an angular velocity of 9.4 rad/s and a torque of 0.04 Nm. Additionally, the turbine was also run at rpm=60, producing 0.3W at 0.05 Nm of torque and an angular velocity of 6.28 rad/s. These findings are much be beneficial in the application mechanical engineering and chemical engineering.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121793129","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099175
Shah Noor, S. Bazai, Muhammad Imran Ghafoor, Shahabzade Marjan, Saira Akram, Fatima Ali
In numerous research areas, anomaly identification is a major problem. Identifying and properly classifying data as anomalous is a challenging task that is resolved in various manners over the years. Different approaches like traditional, supervised, unsupervised, and semi-supervised are used for anomaly detection. In the literature, various machine learning-based anomaly detection algorithms exist. It is challenging to choose one anomaly detection algorithm from the several available algorithms because each algorithm puts forward its good detection performance. In recent years, generative adversarial networks have shown remarkable results for anomaly classification. This paper aims to represent a systematic literature review of generative adversarial network-based approaches for anomaly detection and highlights their pros.
{"title":"Generative Adversarial Networks for Anomaly Detection: A Systematic Literature Review","authors":"Shah Noor, S. Bazai, Muhammad Imran Ghafoor, Shahabzade Marjan, Saira Akram, Fatima Ali","doi":"10.1109/iCoMET57998.2023.10099175","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099175","url":null,"abstract":"In numerous research areas, anomaly identification is a major problem. Identifying and properly classifying data as anomalous is a challenging task that is resolved in various manners over the years. Different approaches like traditional, supervised, unsupervised, and semi-supervised are used for anomaly detection. In the literature, various machine learning-based anomaly detection algorithms exist. It is challenging to choose one anomaly detection algorithm from the several available algorithms because each algorithm puts forward its good detection performance. In recent years, generative adversarial networks have shown remarkable results for anomaly classification. This paper aims to represent a systematic literature review of generative adversarial network-based approaches for anomaly detection and highlights their pros.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124799068","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099333
M. Yamin, A. Imran, Basel Katt
With time, most people spend their lives in a digital world, hooked up to different services provided by various internet companies. This is great for companies or organizations to display ads based on their user behavior. However, this has an adverse effect on user productivity by spending a lot of time on social media for momentary pleasures. Many organizations have taken additional steps to address this problem, like Apple introduced a new feature in IOS 14, Focus to disable application notifications so people can focus on their work. Similarly, Google added a feature in YouTube to show how much time is spent on YouTube for personal reflection. However, other organizations aren't as socially responsible compared to the above, and they're enticing users to spend more time in the digital world. To address this problem, the same technology empowering ads personalization can be utilized to provide digital learning personalization based on individuals' behavior metrics. This can enable the users of such technology to spend their time based on their own self-improvement goals as part of life-long learning. Therefore in this work, we propose a framework of a conversational digital twin agent that can assist in personalized learning.
{"title":"Towards a Digital Twin for Lifelong Learning","authors":"M. Yamin, A. Imran, Basel Katt","doi":"10.1109/iCoMET57998.2023.10099333","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099333","url":null,"abstract":"With time, most people spend their lives in a digital world, hooked up to different services provided by various internet companies. This is great for companies or organizations to display ads based on their user behavior. However, this has an adverse effect on user productivity by spending a lot of time on social media for momentary pleasures. Many organizations have taken additional steps to address this problem, like Apple introduced a new feature in IOS 14, Focus to disable application notifications so people can focus on their work. Similarly, Google added a feature in YouTube to show how much time is spent on YouTube for personal reflection. However, other organizations aren't as socially responsible compared to the above, and they're enticing users to spend more time in the digital world. To address this problem, the same technology empowering ads personalization can be utilized to provide digital learning personalization based on individuals' behavior metrics. This can enable the users of such technology to spend their time based on their own self-improvement goals as part of life-long learning. Therefore in this work, we propose a framework of a conversational digital twin agent that can assist in personalized learning.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125309480","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099351
Ubaidullah Alias Kashif, Zulfiqar Ali Memon, Sajid Ahmed Ghanghro, Wajid Ahmed Channa, A. Soomro
Cloud computing platforms constructed on Trusted Computing Technology are commonly referred to as trusted cloud computing. The core component of this technology is known as Trusted Platform Module (TPM). Its specifications are given by the consortium known as Trusted Computing Group (TCG). Though TPM is an immovable module and is fixed inside any computer, yet its credentials can be shared with other devices. This paper presents the accessibility model for Virtual Machine (VM) of a distributed trust protocol which is based on TPM, through which consumer's credentials are shared to other trusted devices. These credentials essentially describe the integrity of VM.
{"title":"Centralized Accessibility of VM for Distributed Trusted Cloud Computing","authors":"Ubaidullah Alias Kashif, Zulfiqar Ali Memon, Sajid Ahmed Ghanghro, Wajid Ahmed Channa, A. Soomro","doi":"10.1109/iCoMET57998.2023.10099351","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099351","url":null,"abstract":"Cloud computing platforms constructed on Trusted Computing Technology are commonly referred to as trusted cloud computing. The core component of this technology is known as Trusted Platform Module (TPM). Its specifications are given by the consortium known as Trusted Computing Group (TCG). Though TPM is an immovable module and is fixed inside any computer, yet its credentials can be shared with other devices. This paper presents the accessibility model for Virtual Machine (VM) of a distributed trust protocol which is based on TPM, through which consumer's credentials are shared to other trusted devices. These credentials essentially describe the integrity of VM.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129068337","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099078
Ghalib Nadeem, Yawar Rehman, Abdul Khaliq, Huma Khalid, Muhammad Irfan Anis
COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification.
{"title":"Artificial Intelligence based prediction system for General Medicine","authors":"Ghalib Nadeem, Yawar Rehman, Abdul Khaliq, Huma Khalid, Muhammad Irfan Anis","doi":"10.1109/iCoMET57998.2023.10099078","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099078","url":null,"abstract":"COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130336557","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099361
Maaz Ahmad, Muhammad Yousaf Ali Khan, A. Nawaz, E. Mustafa, Nazar Hussain Baloch
Due to technological advancement, modern life style, rural electrification and rapid urbanization, Electricity demand is also increasing in Pakistan from last decade. The country is facing demand and supply deficit in order to meet its electricity demand. Though the country has installed generation capacity of 43775 MW, but still the country could not meet demand of 29000 MW during peak summer season of 2022. Due to non-expansion of transmission network, non-up gradation of distribution network and mismanagement of energy mix, the public is facing load shedding of 8 to 12 hours in urban and rural areas. In 1998 Unbundling of WAPDA, formation of DISCOs and NTDC was aimed to end the monopoly in the power sector and to introduce the competition in order to provide better services to the end users which could not be achieved in true spirit. Net metering, installation of smart meters and AMI infrastructure is also introducing innovation in distribution networks of Pakistan, which could help to meet the energy crisis and reduce the demand & supply deficit. Net metering system has motivated the users to install rooftop solar system and supply excessive generated power to grid. NEPRA has approved a CTBCM model recently in 2022, which would lead to open Electricity market in power sector in near future. This model will not only help to bring the competition among the electricity suppliers but will also allow bulk power consumers to directly purchase electricity from generators. The research work elaborates how the installation of smart meters, net metering and introducing AMI infrastructure could help the DISCOs to counter the challenges like electricity theft problems, load management, load forecasting and improving their performance.
{"title":"Future Prospective of Smart Meters, Net Metering and Electricity Market for Power Distribution companies in Pakistan","authors":"Maaz Ahmad, Muhammad Yousaf Ali Khan, A. Nawaz, E. Mustafa, Nazar Hussain Baloch","doi":"10.1109/iCoMET57998.2023.10099361","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099361","url":null,"abstract":"Due to technological advancement, modern life style, rural electrification and rapid urbanization, Electricity demand is also increasing in Pakistan from last decade. The country is facing demand and supply deficit in order to meet its electricity demand. Though the country has installed generation capacity of 43775 MW, but still the country could not meet demand of 29000 MW during peak summer season of 2022. Due to non-expansion of transmission network, non-up gradation of distribution network and mismanagement of energy mix, the public is facing load shedding of 8 to 12 hours in urban and rural areas. In 1998 Unbundling of WAPDA, formation of DISCOs and NTDC was aimed to end the monopoly in the power sector and to introduce the competition in order to provide better services to the end users which could not be achieved in true spirit. Net metering, installation of smart meters and AMI infrastructure is also introducing innovation in distribution networks of Pakistan, which could help to meet the energy crisis and reduce the demand & supply deficit. Net metering system has motivated the users to install rooftop solar system and supply excessive generated power to grid. NEPRA has approved a CTBCM model recently in 2022, which would lead to open Electricity market in power sector in near future. This model will not only help to bring the competition among the electricity suppliers but will also allow bulk power consumers to directly purchase electricity from generators. The research work elaborates how the installation of smart meters, net metering and introducing AMI infrastructure could help the DISCOs to counter the challenges like electricity theft problems, load management, load forecasting and improving their performance.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132013402","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099326
Abdul Khaliq, Mansoor Ahmed Tahir, Ghalib Nadeem, Syed Hasan Adil, Junaid Jamshid, Jamil Ahmed Memon
In software-defined networking, The network control plane's physical separation from the forwarding plane from where several devices are being controlled from the control plane. Therefore, software-defined networking is an emergent dynamic architect, quite manageable, cost-effective, and readily adaptable which makes it immensely ideal for high bandwidth hence, dynamic for today's application. Whereas, with this architecture the network controlling and forwarding functions can be separated, allowing the underlying infrastructure to be abstracted for applications and network services and making network control easily programmable. Hence, the fundamental element for building SDN solutions is the OpenFlow® protocol. In this paper, our crucial objective is to provide (in particular) uninterrupted delivery of services using available resources using all networked devices, links, and the web. The servers are on. An average improvement of 23-53 % was observed using the round-robin forwarding strategy.
{"title":"Performance comparison of Webservers load balancing using HAProxy in SDN","authors":"Abdul Khaliq, Mansoor Ahmed Tahir, Ghalib Nadeem, Syed Hasan Adil, Junaid Jamshid, Jamil Ahmed Memon","doi":"10.1109/iCoMET57998.2023.10099326","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099326","url":null,"abstract":"In software-defined networking, The network control plane's physical separation from the forwarding plane from where several devices are being controlled from the control plane. Therefore, software-defined networking is an emergent dynamic architect, quite manageable, cost-effective, and readily adaptable which makes it immensely ideal for high bandwidth hence, dynamic for today's application. Whereas, with this architecture the network controlling and forwarding functions can be separated, allowing the underlying infrastructure to be abstracted for applications and network services and making network control easily programmable. Hence, the fundamental element for building SDN solutions is the OpenFlow® protocol. In this paper, our crucial objective is to provide (in particular) uninterrupted delivery of services using available resources using all networked devices, links, and the web. The servers are on. An average improvement of 23-53 % was observed using the round-robin forwarding strategy.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115759010","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099192
Muhammad Sohaib, Hamza Shaukat, T. Tauqeer, Arslan Shahid, Usman Younis, Rehan Hafiz
Induction motors comprise more than 90 percent of the industrial load. With time, Induction motors are prone to losses. To save energy consumption, predictive maintenance of motors must be carried out at regular intervals. The industrial monitoring and automation lab has developed state-of-the-art motor test bench facility which is completely automated using LabView - a widely used industrial software. The manual methods of motor testing are not only hectic but also unreliable. A systematic approach has been adopted to measure and analyze various parameters of the induction motor, which will help us to identify key performance factors. This work is towards the development of a high performance motor test bench facility for industrial load. In its current state it can measure up to 15 hp induction motors and perform the tests such as No-Load Test, Full-Load Test, Locked-Rotor Test, Temperature Rise Test, DC Winding Test etc.
{"title":"LabView based Automated Motor Test Bench for Induction Motors","authors":"Muhammad Sohaib, Hamza Shaukat, T. Tauqeer, Arslan Shahid, Usman Younis, Rehan Hafiz","doi":"10.1109/iCoMET57998.2023.10099192","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099192","url":null,"abstract":"Induction motors comprise more than 90 percent of the industrial load. With time, Induction motors are prone to losses. To save energy consumption, predictive maintenance of motors must be carried out at regular intervals. The industrial monitoring and automation lab has developed state-of-the-art motor test bench facility which is completely automated using LabView - a widely used industrial software. The manual methods of motor testing are not only hectic but also unreliable. A systematic approach has been adopted to measure and analyze various parameters of the induction motor, which will help us to identify key performance factors. This work is towards the development of a high performance motor test bench facility for industrial load. In its current state it can measure up to 15 hp induction motors and perform the tests such as No-Load Test, Full-Load Test, Locked-Rotor Test, Temperature Rise Test, DC Winding Test etc.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411871","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099357
Gul Zaman Khan, Ibrar Ali Shah, Farhatullah, Muhammad Ikram Ullah, Inam Ullah, Muhammad Ihtesham, Hazrat Junaid, Spogmay Yousafzai, Fouzia Sardar
Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.
{"title":"An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease","authors":"Gul Zaman Khan, Ibrar Ali Shah, Farhatullah, Muhammad Ikram Ullah, Inam Ullah, Muhammad Ihtesham, Hazrat Junaid, Spogmay Yousafzai, Fouzia Sardar","doi":"10.1109/iCoMET57998.2023.10099357","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099357","url":null,"abstract":"Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126282245","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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