Pub Date : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965237
Doorgesh Sookarah, Loovesh S. Ramwodin
In these contemporary times, social media is omnipresent and most people adhere to at least one of these digital platforms. Social entertainment generates an enormous amount of data and this is an unparalleled opportunity for data scientists and linguistic experts. These factors have renewed the interest in Natural Language Processing techniques and as such, there is a continuous increase in the number of publications that deal with the topic of Tweet classification using machine learning models. In this paper, experiments performed by the TweetEval team from the University of Cardiff have been studied and expanded upon. These tasks include emotion detection, offensive language identification and hate speech detection. The decision was made to focus on these specific classification tasks as they directly relate to unsought behaviours such as online harassment. This research endeavour involved building and testing a transformer-based language model which is capable of matching the performance of TweetEval. The aim of this study is therefore to identify common limitations to such models and how these can be circumvented to effectively combat phenomenon such as cyberbullying and online abuse using machine learning. From the results that were obtained, the developed BERT model performed comparatively well to other similar algorithms for all tasks as the obtained results were an F1-Score of 0.51, 0.76 and 0.80 for hate speech, emotion detection and offensive language respectively.
{"title":"Combatting online harassment by using transformer language models for the detection of emotions, hate speech and offensive language on social media","authors":"Doorgesh Sookarah, Loovesh S. Ramwodin","doi":"10.1109/ELECOM54934.2022.9965237","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965237","url":null,"abstract":"In these contemporary times, social media is omnipresent and most people adhere to at least one of these digital platforms. Social entertainment generates an enormous amount of data and this is an unparalleled opportunity for data scientists and linguistic experts. These factors have renewed the interest in Natural Language Processing techniques and as such, there is a continuous increase in the number of publications that deal with the topic of Tweet classification using machine learning models. In this paper, experiments performed by the TweetEval team from the University of Cardiff have been studied and expanded upon. These tasks include emotion detection, offensive language identification and hate speech detection. The decision was made to focus on these specific classification tasks as they directly relate to unsought behaviours such as online harassment. This research endeavour involved building and testing a transformer-based language model which is capable of matching the performance of TweetEval. The aim of this study is therefore to identify common limitations to such models and how these can be circumvented to effectively combat phenomenon such as cyberbullying and online abuse using machine learning. From the results that were obtained, the developed BERT model performed comparatively well to other similar algorithms for all tasks as the obtained results were an F1-Score of 0.51, 0.76 and 0.80 for hate speech, emotion detection and offensive language respectively.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131490047","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965253
Zdeněk Kohl, J. Vavra, Jiri Dolezal
In this paper, we discuss a Microcomputer Unit based dual-port emulator. The emulator can be embedded into a functional electrical circuit in order to take over the functionality of a selected component. The component parameters or even the component type can be modified on the run without the need for replacement. This saves the designer’s time and allows verification of the equipment functionality with components that are not available during the device design phase. We demonstrate the use of the emulator on typical examples and concentrate on emulation fidelity, usage simplicity, and overall device versatility.
{"title":"Dual Port Component Emulator and its usage for Electronic Equipment Functional Verification","authors":"Zdeněk Kohl, J. Vavra, Jiri Dolezal","doi":"10.1109/ELECOM54934.2022.9965253","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965253","url":null,"abstract":"In this paper, we discuss a Microcomputer Unit based dual-port emulator. The emulator can be embedded into a functional electrical circuit in order to take over the functionality of a selected component. The component parameters or even the component type can be modified on the run without the need for replacement. This saves the designer’s time and allows verification of the equipment functionality with components that are not available during the device design phase. We demonstrate the use of the emulator on typical examples and concentrate on emulation fidelity, usage simplicity, and overall device versatility.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124196803","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965236
Mariame Niang, P. Canalda, Massa Ndong, F. Spies, I. Dioum, I. Diop, Mohamed A. Abd El Ghany
Indoor localization has gained popularity in recent years. Various technologies have been proposed, but many of them do not give good accuracy without high-cost equipment. However, the Wi-Fi signal-based fingerprinting technique is widely employed for indoor locations because of its simplicity and low hardware requirements. Nevertheless, the Received Signal Strength Indicator (RSSI) values are affected by random fluctuations caused by fading and multi-path phenomena, resulting in decreased accuracy. In this paper, we propose indoor localization using Machine Learning (ML) algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Support-Vector Machine (SVM) combine with a Recursive Least Squares (RLS) filter to increase the accuracy. The first method involves the use of ML algorithms to build an indoor positioning model. The second approach is to apply the RLS filter to reduce the noise in the data as the environment changes. The performance of these methods is evaluated through extensive real-time indoor experiments. We found that the proposed approach is an improvement over the state-of-the-art and recently published work.
近年来，室内定位越来越受欢迎。已经提出了各种技术，但如果没有高成本的设备，其中许多技术无法提供良好的精度。然而，基于Wi-Fi信号的指纹识别技术由于其简单和低硬件要求而被广泛应用于室内位置。然而，接收信号强度指标(RSSI)值受到衰落和多径现象引起的随机波动的影响，导致精度下降。在本文中，我们提出使用机器学习(ML)算法，如随机森林(RF)，极端梯度增强(XGBoost)， k -近邻(KNN)和支持向量机(SVM)结合递归最小二乘(RLS)滤波器来提高室内定位精度。第一种方法是使用ML算法建立室内定位模型。第二种方法是应用RLS滤波器，随着环境的变化降低数据中的噪声。通过大量的实时室内实验对这些方法的性能进行了评估。我们发现，所提出的方法是对最先进的和最近发表的工作的改进。
{"title":"An adapted machine learning algorithm based-Fingerprints using RLS to improve indoor Wi-fi localization systems","authors":"Mariame Niang, P. Canalda, Massa Ndong, F. Spies, I. Dioum, I. Diop, Mohamed A. Abd El Ghany","doi":"10.1109/ELECOM54934.2022.9965236","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965236","url":null,"abstract":"Indoor localization has gained popularity in recent years. Various technologies have been proposed, but many of them do not give good accuracy without high-cost equipment. However, the Wi-Fi signal-based fingerprinting technique is widely employed for indoor locations because of its simplicity and low hardware requirements. Nevertheless, the Received Signal Strength Indicator (RSSI) values are affected by random fluctuations caused by fading and multi-path phenomena, resulting in decreased accuracy. In this paper, we propose indoor localization using Machine Learning (ML) algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Support-Vector Machine (SVM) combine with a Recursive Least Squares (RLS) filter to increase the accuracy. The first method involves the use of ML algorithms to build an indoor positioning model. The second approach is to apply the RLS filter to reduce the noise in the data as the environment changes. The performance of these methods is evaluated through extensive real-time indoor experiments. We found that the proposed approach is an improvement over the state-of-the-art and recently published work.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117116907","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965250
Jahib Nawfal, A. Mungur
Object detection plays a crucial role in the field of computer vision. It is viewed as a challenging task as it identifies instances of objects from a particular class in digital images or videos. However, since the invention of deep learning methods, the performance of object detection has significantly improved. They are now able to learn semantic, high-level, and deeper features to address existing issues found in traditional architectures. In this paper, an evaluation framework has been proposed to assess the performance of Tiny Yolov3 and MobileNet SSD v1 for detecting people. In addition, both Tiny Yolov3 and MobileNet SSD v1 consist of a lightweight architecture that eliminates the expensive computation to run the models in real time detection using a NON-GPU platform. A fair comparison was made between the pre-trained models by using the two available datasets which are COCO and PASCAL VOC. The model’s performance was evaluated in a classroom scenario, where people were detected and counted. A mobile application was built to view the detection results and its performance was assessed when used with deep learning models. To have a more expansive evaluation, different parameters such as platform, cameras, and conditions were considered. From those parameters, different test cases were formulated and tested to determine which models excel the most and where. Following the evaluation, this paper proposes an evaluation framework for MobileNet SSD v1 and Tiny Yolov3 and provides a domain recommendation for future applications.
{"title":"Performance Evaluation Between Tiny Yolov3 and MobileNet SSDv1 for Object Detection","authors":"Jahib Nawfal, A. Mungur","doi":"10.1109/ELECOM54934.2022.9965250","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965250","url":null,"abstract":"Object detection plays a crucial role in the field of computer vision. It is viewed as a challenging task as it identifies instances of objects from a particular class in digital images or videos. However, since the invention of deep learning methods, the performance of object detection has significantly improved. They are now able to learn semantic, high-level, and deeper features to address existing issues found in traditional architectures. In this paper, an evaluation framework has been proposed to assess the performance of Tiny Yolov3 and MobileNet SSD v1 for detecting people. In addition, both Tiny Yolov3 and MobileNet SSD v1 consist of a lightweight architecture that eliminates the expensive computation to run the models in real time detection using a NON-GPU platform. A fair comparison was made between the pre-trained models by using the two available datasets which are COCO and PASCAL VOC. The model’s performance was evaluated in a classroom scenario, where people were detected and counted. A mobile application was built to view the detection results and its performance was assessed when used with deep learning models. To have a more expansive evaluation, different parameters such as platform, cameras, and conditions were considered. From those parameters, different test cases were formulated and tested to determine which models excel the most and where. Following the evaluation, this paper proposes an evaluation framework for MobileNet SSD v1 and Tiny Yolov3 and provides a domain recommendation for future applications.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123795580","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965235
Aicha Jahangeer, V. Bassoo
A cell outage compensation algorithm based on Received Signal Strength Indicator (RSSI) for a three-tier hetrogeneous network (HetNet) is proposed in this paper. The algorithm is non-machine learning based to reduce the complexity of the compensation scheme, and to eliminate the need for training. Simulation results show that cell outage compensation is successfully achieved, provided that base stations (BSs) of sufficient capacity are deployed near users in outage. The RSSI values after compensation are also higher than those during the outage. Additionally, the proposed algorithm outperforms a k-means clustering scheme when allocating users in outage to neighbouring BSs.
{"title":"Non-Machine Learning Cell Outage Compensation for a Three Tier Heterogeneous Network","authors":"Aicha Jahangeer, V. Bassoo","doi":"10.1109/ELECOM54934.2022.9965235","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965235","url":null,"abstract":"A cell outage compensation algorithm based on Received Signal Strength Indicator (RSSI) for a three-tier hetrogeneous network (HetNet) is proposed in this paper. The algorithm is non-machine learning based to reduce the complexity of the compensation scheme, and to eliminate the need for training. Simulation results show that cell outage compensation is successfully achieved, provided that base stations (BSs) of sufficient capacity are deployed near users in outage. The RSSI values after compensation are also higher than those during the outage. Additionally, the proposed algorithm outperforms a k-means clustering scheme when allocating users in outage to neighbouring BSs.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131750623","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965229
Baboo Sivrajsingh Loderchand, R. A. Ah King, B. Rajkumarsingh
High concentrations of particulate matter (PM) in the atmosphere have been associated with the degradation of human health. Citizens have increasingly demanded for more participatory, timely, and diffused air quality monitoring actions. In this work, we present a low cost IoT based Real-Time Air Pollution Monitoring System of ${$}$50 only to analyse the air quality in Mauritius. The proposed real-time stations consist of an IoT module that monitors and signalizes the air quality. A website and mobile application is also provided to allow an end-user access all the recorded data. Two stations were implemented in 3 different localities in Mauritius and they were evaluated under different scenarios. The study shows the results of air monitoring at the different locations as well as the air quality regarding the natural indoor conditions. Finally, this paper shows how much has been achieved and how the system can be improved.
{"title":"Smart Real Time System For Air Pollution Monitoring","authors":"Baboo Sivrajsingh Loderchand, R. A. Ah King, B. Rajkumarsingh","doi":"10.1109/ELECOM54934.2022.9965229","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965229","url":null,"abstract":"High concentrations of particulate matter (PM) in the atmosphere have been associated with the degradation of human health. Citizens have increasingly demanded for more participatory, timely, and diffused air quality monitoring actions. In this work, we present a low cost IoT based Real-Time Air Pollution Monitoring System of ${$}$50 only to analyse the air quality in Mauritius. The proposed real-time stations consist of an IoT module that monitors and signalizes the air quality. A website and mobile application is also provided to allow an end-user access all the recorded data. Two stations were implemented in 3 different localities in Mauritius and they were evaluated under different scenarios. The study shows the results of air monitoring at the different locations as well as the air quality regarding the natural indoor conditions. Finally, this paper shows how much has been achieved and how the system can be improved.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132383948","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965247
Ackbarally Bassirr, A. Murdan
Water is an essential requirement for all living things. With the exponential growth of the human population, the need for water resource conservation is becoming increasingly important. Many water management systems have been proposed in the past. The pursuit of a smart water management system is gaining ground with the advent of the Internet of Things (IoT). This project aims at designing and implementing a smart water management system based on IoT for an apartment. The main requirements of the system are low cost, effectiveness and reliability. The project comprises of monitoring the different parameters with regards to water management and transmitting the data to a smartphone. Various parameters such as water level and flow rate are measured using appropriate sensors. The in-built Wi-Fi connection capability of the NodeMCU microcontroller is used to transmit the data to the Blynk application dashboard. The system is also designed to alert the user on his smartphone in case any fault such as low water level or water leakage is detected. A solenoid valve has also been implemented in the system to allow the user to control the flow of water in case of emergency. Furthermore, the system includes an LCD and buzzer so that the system can still be usable in case there is no WIFI connection.
{"title":"Smart Water Management System for an Apartment","authors":"Ackbarally Bassirr, A. Murdan","doi":"10.1109/ELECOM54934.2022.9965247","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965247","url":null,"abstract":"Water is an essential requirement for all living things. With the exponential growth of the human population, the need for water resource conservation is becoming increasingly important. Many water management systems have been proposed in the past. The pursuit of a smart water management system is gaining ground with the advent of the Internet of Things (IoT). This project aims at designing and implementing a smart water management system based on IoT for an apartment. The main requirements of the system are low cost, effectiveness and reliability. The project comprises of monitoring the different parameters with regards to water management and transmitting the data to a smartphone. Various parameters such as water level and flow rate are measured using appropriate sensors. The in-built Wi-Fi connection capability of the NodeMCU microcontroller is used to transmit the data to the Blynk application dashboard. The system is also designed to alert the user on his smartphone in case any fault such as low water level or water leakage is detected. A solenoid valve has also been implemented in the system to allow the user to control the flow of water in case of emergency. Furthermore, the system includes an LCD and buzzer so that the system can still be usable in case there is no WIFI connection.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"32 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123228236","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965244
Muhammad Hasan Motalib Soorefan, R. Ramjug-Ballgobin
Stability is a crucial part when discussing power systems. It is the ability of a system to return to an equilibrium state after being subjected to a certain load perturbation. In this paper, the focus is on frequency stability, also known as Load Frequency Control (LFC). For this instance, two different power systems, namely a single source two area system and a multi-source power system were used. The aim is to design and apply three different control algorithms namely Equilibrium optimisation (EO), Grey Wolf optimisation (GWO) and Whale optimisation (WOA) to the two mentioned power systems and determine the most suitable one for LFC. The results obtained showed that all three algorithms were successful in improving the uncompensated response. Further in-depth analysis which was related to the fitness function found that the Equilibrium optimisation was the best among the three techniques due to its superior explorative behaviour.
{"title":"Optimisation Techniques for Load Frequency Control","authors":"Muhammad Hasan Motalib Soorefan, R. Ramjug-Ballgobin","doi":"10.1109/ELECOM54934.2022.9965244","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965244","url":null,"abstract":"Stability is a crucial part when discussing power systems. It is the ability of a system to return to an equilibrium state after being subjected to a certain load perturbation. In this paper, the focus is on frequency stability, also known as Load Frequency Control (LFC). For this instance, two different power systems, namely a single source two area system and a multi-source power system were used. The aim is to design and apply three different control algorithms namely Equilibrium optimisation (EO), Grey Wolf optimisation (GWO) and Whale optimisation (WOA) to the two mentioned power systems and determine the most suitable one for LFC. The results obtained showed that all three algorithms were successful in improving the uncompensated response. Further in-depth analysis which was related to the fitness function found that the Equilibrium optimisation was the best among the three techniques due to its superior explorative behaviour.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"55 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128555969","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965246
Salem Alghamdi, B. Hilton, Yaser Alhasawi, Anthony Corso, June K. Hilton
Due to the massive competition, the business environment is getting dynamic and as well as complicated. To win this competition, a successful business needs to develop strategic decisions by exploring all the available information. To this end, competitive intelligence is one of the appropriate tools to reach this goal. In this paper, we proposed an artifact for strategic decision-making by telecommunication firms. A detailed comparison is conducted among the firms and geographical areas in the United States. The study includes Spectrum (Charter Communications), Verizon Communications Inc, Xfinity (Comcast Corporation), and Cox Communications. We conduct qualitative and quantitative approaches to evaluate the proposed artifact. The findings showed positive results towards using the artifact and it exhibits the potential to be effective with respect to business decision-making in the telecommunication industry. Compared to the benchmark data, the achieved results shows that the participants experienced in the proposed artifact had an excellent experience with respect to the stimulation’s novelty.
{"title":"Artifact for Strategic Decision-Making by Telecommunication Firms","authors":"Salem Alghamdi, B. Hilton, Yaser Alhasawi, Anthony Corso, June K. Hilton","doi":"10.1109/ELECOM54934.2022.9965246","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965246","url":null,"abstract":"Due to the massive competition, the business environment is getting dynamic and as well as complicated. To win this competition, a successful business needs to develop strategic decisions by exploring all the available information. To this end, competitive intelligence is one of the appropriate tools to reach this goal. In this paper, we proposed an artifact for strategic decision-making by telecommunication firms. A detailed comparison is conducted among the firms and geographical areas in the United States. The study includes Spectrum (Charter Communications), Verizon Communications Inc, Xfinity (Comcast Corporation), and Cox Communications. We conduct qualitative and quantitative approaches to evaluate the proposed artifact. The findings showed positive results towards using the artifact and it exhibits the potential to be effective with respect to business decision-making in the telecommunication industry. Compared to the benchmark data, the achieved results shows that the participants experienced in the proposed artifact had an excellent experience with respect to the stimulation’s novelty.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114926808","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 : 2022-11-22DOI: 10.1109/ELECOM54934.2022.9965224
Deepam Yasvant Ambelal, M. Shuma-Iwisi, A. Mohamed
The work carried out was to develop a model of the human upper body. The model developed took into account five displacement characteristics of the subject, and five optimizing parameters. The model was applied on 18 healthy, un-amputated participants for the scenario of grasping an insulated, flat, two-core power cable for a lamp to determine the effect EMI has on electromyography sensors due to capacitive coupling. The experiment evaluated four cases which covered the combinations of using each hand to grasp the cable and two rotational orientations of the cable in the grasp. The unique parameters for each participant model were fine-tuned to achieve the lowest average error given certain criteria. The resulting average error across all participants for modelling the four cases was 27.4 %. The lowest average error achieved for a single participant was 9.6 %.
{"title":"Development of Hand-to-Hand Human Body Electric Circuit Model with optimisation","authors":"Deepam Yasvant Ambelal, M. Shuma-Iwisi, A. Mohamed","doi":"10.1109/ELECOM54934.2022.9965224","DOIUrl":"https://doi.org/10.1109/ELECOM54934.2022.9965224","url":null,"abstract":"The work carried out was to develop a model of the human upper body. The model developed took into account five displacement characteristics of the subject, and five optimizing parameters. The model was applied on 18 healthy, un-amputated participants for the scenario of grasping an insulated, flat, two-core power cable for a lamp to determine the effect EMI has on electromyography sensors due to capacitive coupling. The experiment evaluated four cases which covered the combinations of using each hand to grasp the cable and two rotational orientations of the cable in the grasp. The unique parameters for each participant model were fine-tuned to achieve the lowest average error given certain criteria. The resulting average error across all participants for modelling the four cases was 27.4 %. The lowest average error achieved for a single participant was 9.6 %.","PeriodicalId":302869,"journal":{"name":"2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025657","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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