Pub Date : 2023-04-29DOI: 10.5121/csit.2023.130707
Around 15 million bikes are discarded annually, which poses an environmental risk [1]. The rubber from bike tires takes a long time to decompose, and toxic chemicals are released into the soil during this process [2]. Additionally, the popularity of e-bikes is increasing, and the lithium batteries they use harm the environment during extraction. To address this problem, a bike donation app is proposed, which reduces the number of bikes produced, minimizes waste, and benefits those in need [3]. By operating online, the cost of running the operation is minimal, and the project can reach and help anyone with internet access. However, the app's success relies on a user base, which may be a significant challenge. Furthermore, the app's design may need improvement to attract users. Blind spots in the program may include inaccurate bike donation recommendations and a lack of proper verification for donated bikes' safety and condition. An A/B test shows that personalized recommendations through the app increased the conversion rate for successful bike donations. The verification process for donated bikes was effective in ensuring the bikes' safety and quality. By developing a mobile app that provides personalized recommendations and addresses bike waste, the project contributes to sustainable transportation and reduces environmental harm [4].
{"title":"A Community-Based Mobile Application to Reduce Waste from Un-used Bikes Using Social Media","authors":"","doi":"10.5121/csit.2023.130707","DOIUrl":"https://doi.org/10.5121/csit.2023.130707","url":null,"abstract":"Around 15 million bikes are discarded annually, which poses an environmental risk [1]. The rubber from bike tires takes a long time to decompose, and toxic chemicals are released into the soil during this process [2]. Additionally, the popularity of e-bikes is increasing, and the lithium batteries they use harm the environment during extraction. To address this problem, a bike donation app is proposed, which reduces the number of bikes produced, minimizes waste, and benefits those in need [3]. By operating online, the cost of running the operation is minimal, and the project can reach and help anyone with internet access. However, the app's success relies on a user base, which may be a significant challenge. Furthermore, the app's design may need improvement to attract users. Blind spots in the program may include inaccurate bike donation recommendations and a lack of proper verification for donated bikes' safety and condition. An A/B test shows that personalized recommendations through the app increased the conversion rate for successful bike donations. The verification process for donated bikes was effective in ensuring the bikes' safety and quality. By developing a mobile app that provides personalized recommendations and addresses bike waste, the project contributes to sustainable transportation and reduces environmental harm [4].","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"85 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74270473","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 : 2023-04-29DOI: 10.5121/csit.2023.130711
Yajing Liu, Ruijie Cai, Xiaokang Yin, Shengli Liu
Vulnerability exploitation is the key to obtaining the control authority of the system, posing a significant threat to network security. Therefore, it is necessary to discover exploitation from traffic. The current methods usually only target a single stage with an incomplete causal relationship and depend on the payload content, causing attacker easily avoids detection by encrypting traffic and other means. To solve the above problems, we propose a traffic traceback method of vulnerability exploitation based on session relation. First, we construct the session relationship model using the session correlation of different stages during the exploit. Second, we build a session diagram based on historical traffic. Finally, we traverse the session diagram to find the traffic conforming to the session relationship model. Compared with Blatta, a method detecting early exploit traffic with RNN, the detection rate of our method is increased by 50%, independent of traffic encryption methods.
{"title":"A Novel Exploit Traffic Traceback Method based on Session Relationship","authors":"Yajing Liu, Ruijie Cai, Xiaokang Yin, Shengli Liu","doi":"10.5121/csit.2023.130711","DOIUrl":"https://doi.org/10.5121/csit.2023.130711","url":null,"abstract":"Vulnerability exploitation is the key to obtaining the control authority of the system, posing a significant threat to network security. Therefore, it is necessary to discover exploitation from traffic. The current methods usually only target a single stage with an incomplete causal relationship and depend on the payload content, causing attacker easily avoids detection by encrypting traffic and other means. To solve the above problems, we propose a traffic traceback method of vulnerability exploitation based on session relation. First, we construct the session relationship model using the session correlation of different stages during the exploit. Second, we build a session diagram based on historical traffic. Finally, we traverse the session diagram to find the traffic conforming to the session relationship model. Compared with Blatta, a method detecting early exploit traffic with RNN, the detection rate of our method is increased by 50%, independent of traffic encryption methods.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"27 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81699759","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}
B. Kuboye, Adedamola Israel Adedipe, S. Oloja, O. Obolo
Over time, higher demand for data speed and quality of service by an increasing number of mobile network subscribers has been the major challenge in the telecommunication industry. This challenge is the result of an increasing population of human race and the continuous advancement in mobile communication industry, which has led to network traffic congestion. In an effort to solve this problem, the telecommunication companies released the Fourth Generation Long Term Evolution (4G LTE) network and afterwards the Fifth Generation Long Term Evolution (5G LTE) network that laid claims to have addressed the problem. However, machine learning techniques, which are very effective in prediction, have proven to be capable of great importance in the extraction and processing of information from the subscriber’s perceptions about the network. The objective of this work is to use machine learning models to predict the existence of traffic congestion in LTE networks as users perceived it. The dataset used for this study was gathered from some students over a period of two months using Google form and thereafter, analysed using the Anaconda machine learning platform. This work compares the results obtained from the four machine learning techniques employed that are k-Nearest Neighbour, Support Vector Machine, Decision Tree and Logistic Regression. The performance evaluation of the ML techniques was done using standard metrics to ascertain the real existence of congestion. The result shows that k-Nearest Neighbour outperforms all other techniques in predicting the existence of traffic congestion. This study therefore has shown that the majority of LTE network users experience traffic congestion.
{"title":"Users’ Evaluation of Traffic Congestion in LTE Networks using Machine Learning Techniques","authors":"B. Kuboye, Adedamola Israel Adedipe, S. Oloja, O. Obolo","doi":"10.30564/aia.v5i1.5452","DOIUrl":"https://doi.org/10.30564/aia.v5i1.5452","url":null,"abstract":"Over time, higher demand for data speed and quality of service by an increasing number of mobile network subscribers has been the major challenge in the telecommunication industry. This challenge is the result of an increasing population of human race and the continuous advancement in mobile communication industry, which has led to network traffic congestion. In an effort to solve this problem, the telecommunication companies released the Fourth Generation Long Term Evolution (4G LTE) network and afterwards the Fifth Generation Long Term Evolution (5G LTE) network that laid claims to have addressed the problem. However, machine learning techniques, which are very effective in prediction, have proven to be capable of great importance in the extraction and processing of information from the subscriber’s perceptions about the network. The objective of this work is to use machine learning models to predict the existence of traffic congestion in LTE networks as users perceived it. The dataset used for this study was gathered from some students over a period of two months using Google form and thereafter, analysed using the Anaconda machine learning platform. This work compares the results obtained from the four machine learning techniques employed that are k-Nearest Neighbour, Support Vector Machine, Decision Tree and Logistic Regression. The performance evaluation of the ML techniques was done using standard metrics to ascertain the real existence of congestion. The result shows that k-Nearest Neighbour outperforms all other techniques in predicting the existence of traffic congestion. This study therefore has shown that the majority of LTE network users experience traffic congestion.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"180 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78562856","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}
It is essential to continually assess and find new ways to recruit and retain participants for research studies. Cryptocurrency is growing in popularity and may be a novel way to incentivize research participants. 100 participants, 50 of whom already had a cryptocurrency wallet and 50 of whom did not have a cryptocurrency wallet, were recruited through Facebook ads and completed a survey that asked about their experience with cryptocurrency and non-fungible tokens (NFTs) and potential interest in use of it for compensating research participants. The majority of respondents (79%) had some experience with cryptocurrency and 85% said they were comfortable trading cryptocurrency. Many participants had exchanged cryptocurrency within the past month (62%) and over their lifetime (70%). Respondents, however, were less familiar with NFTs, with only half having some experience with them. 18% of those without a cryptocurrency wallet and 42% of those with a cryptocurrency wallet chose to be compensated by cryptocurrency and NFT. Results suggest that, although cash and gift card incentives are preferred, there is an interest in cryptocurrency and NFTs. More studies will need to be done on a larger sample size and some of the challenges discussed (like cryptocurrency volatility) need to be addressed.
{"title":"Attitudes About Cryptocurrency Incentives for Research Participation","authors":"D. Ugarte, S. Young","doi":"10.30564/aia.v5i1.5395","DOIUrl":"https://doi.org/10.30564/aia.v5i1.5395","url":null,"abstract":"It is essential to continually assess and find new ways to recruit and retain participants for research studies. Cryptocurrency is growing in popularity and may be a novel way to incentivize research participants. 100 participants, 50 of whom already had a cryptocurrency wallet and 50 of whom did not have a cryptocurrency wallet, were recruited through Facebook ads and completed a survey that asked about their experience with cryptocurrency and non-fungible tokens (NFTs) and potential interest in use of it for compensating research participants. The majority of respondents (79%) had some experience with cryptocurrency and 85% said they were comfortable trading cryptocurrency. Many participants had exchanged cryptocurrency within the past month (62%) and over their lifetime (70%). Respondents, however, were less familiar with NFTs, with only half having some experience with them. 18% of those without a cryptocurrency wallet and 42% of those with a cryptocurrency wallet chose to be compensated by cryptocurrency and NFT. Results suggest that, although cash and gift card incentives are preferred, there is an interest in cryptocurrency and NFTs. More studies will need to be done on a larger sample size and some of the challenges discussed (like cryptocurrency volatility) need to be addressed.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"11 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86507193","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}
The opioid crisis has impacted the lives of millions of Americans. Digital technology has been applied in both research and clinical practice to mitigate this public health emergency. Blockchain technology has been implemented in healthcare and other industries outside of cryptocurrency, with few studies exploring its utility in dealing with the opioid crisis. This paper explores a novel application of blockchain technology and its features to increase uptake of medications for opioid use disorder.
{"title":"A novel application of blockchain technology and its features in an effort to increase uptake of medications for Opioid Use Disorder","authors":"Garett Renee, Zeyad Kelani, Young Sean D.","doi":"10.30564/aia.v4i2.5398","DOIUrl":"https://doi.org/10.30564/aia.v4i2.5398","url":null,"abstract":"The opioid crisis has impacted the lives of millions of Americans. Digital technology has been applied in both research and clinical practice to mitigate this public health emergency. Blockchain technology has been implemented in healthcare and other industries outside of cryptocurrency, with few studies exploring its utility in dealing with the opioid crisis. This paper explores a novel application of blockchain technology and its features to increase uptake of medications for opioid use disorder. ","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"14 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75154766","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}
Mohamed Emish, Hari Kishore Chaparala, Zeyad Kelani, Sean D. Young
Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights. While wearable device data helps to monitor, detect, and predict diseases and health conditions, some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns. Moreover, wearable devices have been recently available as commercial products; thus large, diverse, and representative datasets are not available to most researchers. In this article, we propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers (e.g., researchers) to make wearable device data more available to healthcare researchers. To secure the data transactions in a privacy-preserving manner, we use a decentralized approach using Blockchain and Non-Fungible Tokens (NFTs). To ensure data originality and integrity with secure validation, our marketplace uses Trusted Execution Environments (TEE) in wearable devices to verify the correctness of health data. The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share. To ensure user participation, we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs. We also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits. If widely adopted, we believe that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.
{"title":"On Monetizing Personal Wearable Devices Data: A Blockchain-based Marketplace for Data Crowdsourcing and Federated Machine Learning in Healthcare","authors":"Mohamed Emish, Hari Kishore Chaparala, Zeyad Kelani, Sean D. Young","doi":"10.30564/aia.v4i2.5316","DOIUrl":"https://doi.org/10.30564/aia.v4i2.5316","url":null,"abstract":"Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights. While wearable device data helps to monitor, detect, and predict diseases and health conditions, some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns. Moreover, wearable devices have been recently available as commercial products; thus large, diverse, and representative datasets are not available to most researchers. In this article, we propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers (e.g., researchers) to make wearable device data more available to healthcare researchers. To secure the data transactions in a privacy-preserving manner, we use a decentralized approach using Blockchain and Non-Fungible Tokens (NFTs). To ensure data originality and integrity with secure validation, our marketplace uses Trusted Execution Environments (TEE) in wearable devices to verify the correctness of health data. The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share. To ensure user participation, we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs. We also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits. If widely adopted, we believe that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives. ","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"61 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88758679","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}
Mohamed Oulad-Kaddour, Hamid Haddadou, C. Conde, D. Palacios-Alonso, E. Cabello
Gender classification is an important biometric task. It has been widely studied in the literature. Face modality is the most studied aspect of human-gender classification. Moreover, the task has also been investigated in terms of different face components such as irises, ears, and the periocular region. In this paper, we aim to investigate gender classification based on the oral region. In the proposed approach, we adopt a convolutional neural network. For experimentation, we extracted the region of interest using the RetinaFace algorithm from the FFHQ faces dataset. We achieved acceptable results, surpassing those that use the mouth as a modality or facial sub-region in geometric approaches. The obtained results also proclaim the importance of the oral region as a facial part lost in the Covid-19 context when people wear facial mask. We suppose that the adaptation of existing facial data analysis solutions from the whole face is indispensable to keep-up their robustness.
{"title":"Real-world human gender classification from oral region using convolutional neural netwrok","authors":"Mohamed Oulad-Kaddour, Hamid Haddadou, C. Conde, D. Palacios-Alonso, E. Cabello","doi":"10.14201/adcaij.27797","DOIUrl":"https://doi.org/10.14201/adcaij.27797","url":null,"abstract":"Gender classification is an important biometric task. It has been widely studied in the literature. Face modality is the most studied aspect of human-gender classification. Moreover, the task has also been investigated in terms of different face components such as irises, ears, and the periocular region. In this paper, we aim to investigate gender classification based on the oral region. In the proposed approach, we adopt a convolutional neural network. For experimentation, we extracted the region of interest using the RetinaFace algorithm from the FFHQ faces dataset. We achieved acceptable results, surpassing those that use the mouth as a modality or facial sub-region in geometric approaches. The obtained results also proclaim the importance of the oral region as a facial part lost in the Covid-19 context when people wear facial mask. We suppose that the adaptation of existing facial data analysis solutions from the whole face is indispensable to keep-up their robustness.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"21 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83206367","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}
Sentiment Classification is a key area of natural language processing research that is frequently utilized in several industries. The goal of sentiment analysis is to figure out if a product or service received a negative or positive response. Sentiment analysis is widely utilized in several commercial fields to enhance the quality of services (QoS) for goods or services by gaining a better knowledge of consumer feedback. Deep learning provides cutting-edge achievements in a variety of complex fields. The goal of the study is to propose an improved approach for evaluating and categorising sentiments into different groups. This study proposes a novel hybridised model that combines the benefits of deep learning technologies Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with the word embedding technique. The performance of three distinct word embedding approaches is compared in order to choose the optimal embedding for the proposed model's implementation. In addition, attention-based BiLSTM is used in a multi-convolutional approach. Standard measures were used to verify the validity of the suggested model's performance. The results show that the proposed model has a significantly enhanced accuracy of 96.56%, which is significantly better than existing models.
{"title":"An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique","authors":"R. Ranjan, Daniel A. K.","doi":"10.14201/adcaij.27902","DOIUrl":"https://doi.org/10.14201/adcaij.27902","url":null,"abstract":"Sentiment Classification is a key area of natural language processing research that is frequently utilized in several industries. The goal of sentiment analysis is to figure out if a product or service received a negative or positive response. Sentiment analysis is widely utilized in several commercial fields to enhance the quality of services (QoS) for goods or services by gaining a better knowledge of consumer feedback. Deep learning provides cutting-edge achievements in a variety of complex fields. The goal of the study is to propose an improved approach for evaluating and categorising sentiments into different groups. This study proposes a novel hybridised model that combines the benefits of deep learning technologies Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with the word embedding technique. The performance of three distinct word embedding approaches is compared in order to choose the optimal embedding for the proposed model's implementation. In addition, attention-based BiLSTM is used in a multi-convolutional approach. Standard measures were used to verify the validity of the suggested model's performance. The results show that the proposed model has a significantly enhanced accuracy of 96.56%, which is significantly better than existing models.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"60 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83739652","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}
Metin Argan, Halime Dinç, S. Kaya, Mehpare Tokay Argan
Nowadays, information technology is not only widely used in all walks of life but also fully applied in the marketing and advertisement sector. In particular, Artificial Intelligence (AI) has received growing attention worldwide because of its impact on advertising. However, it remains unclear how social media users react to AI advertisements. The purpose of this study is to examine the behavior of social media users towards AI-based advertisements. This study used a qualitative method, including a semi-structured interview. A total of 23 semi-structured interviews were conducted with social media users aged 18 and over, using a purposive sampling method. The interviews lasted between 27.05–50.39 minutes on average (Mean: 37.48 SD: 6.25) between August and October 2021. We categorized the findings of the current qualitative research into three main process themes: I) reception; II) diving; and III) break-point. While 'reception' covers positive and negative sub-themes, 'diving' includes three themes: comparison, timesaving, and leaping. The final theme, 'break-point', represents the decision-making stage and includes negative or positive opinions. This study provides content producers, social media practitioners, marketing managers, advertising industry, AI researchers, and academics with many insights into AI advertising.
{"title":"Artificial Intelligence (AI) in Advertising","authors":"Metin Argan, Halime Dinç, S. Kaya, Mehpare Tokay Argan","doi":"10.14201/adcaij.28331","DOIUrl":"https://doi.org/10.14201/adcaij.28331","url":null,"abstract":"Nowadays, information technology is not only widely used in all walks of life but also fully applied in the marketing and advertisement sector. In particular, Artificial Intelligence (AI) has received growing attention worldwide because of its impact on advertising. However, it remains unclear how social media users react to AI advertisements. The purpose of this study is to examine the behavior of social media users towards AI-based advertisements. This study used a qualitative method, including a semi-structured interview. A total of 23 semi-structured interviews were conducted with social media users aged 18 and over, using a purposive sampling method. The interviews lasted between 27.05–50.39 minutes on average (Mean: 37.48 SD: 6.25) between August and October 2021. We categorized the findings of the current qualitative research into three main process themes: I) reception; II) diving; and III) break-point. While 'reception' covers positive and negative sub-themes, 'diving' includes three themes: comparison, timesaving, and leaping. The final theme, 'break-point', represents the decision-making stage and includes negative or positive opinions. This study provides content producers, social media practitioners, marketing managers, advertising industry, AI researchers, and academics with many insights into AI advertising.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"125 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84955316","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}
With enormous evolution in Microelectronics, Wireless Sensor Networks (WSNs) have played a vital role in every aspect of daily life. Technological advancement has led to new ways of thinking and of developing infrastructure for sensing, monitoring, and computational tasks. The sensor network constitutes multiple sensor nodes for monitoring, tracking, and surveillance of remote objects in the network area. Battery replacement and recharging are almost impossible; therefore, the aim is to develop an efficient routing protocol for the sensor network. The Fuzzy Based Cluster Head Selection (FBCHS) protocol is proposed, which partitions the network into several regions based on node energy levels. The proposed protocol uses an artificial intelligence technique to select the Cluster Head (CH) based on maximum node Residual Energy (RE) and minimum distance. The transmission of data to the Base Station (BS) is accomplished via static clustering and the hybrid routing technique. The simulation results of the FBCHS protocol are com- pared to the SEP protocol and show improvement in the stability period and improved overall performance of the network.
{"title":"FBCHS: Fuzzy Based Cluster Head Selection Protocol to Enhance Network Lifetime of WSN","authors":"Vipul Narayan, Daniel A. K.","doi":"10.14201/adcaij.27885","DOIUrl":"https://doi.org/10.14201/adcaij.27885","url":null,"abstract":"With enormous evolution in Microelectronics, Wireless Sensor Networks (WSNs) have played a vital role in every aspect of daily life. Technological advancement has led to new ways of thinking and of developing infrastructure for sensing, monitoring, and computational tasks. The sensor network constitutes multiple sensor nodes for monitoring, tracking, and surveillance of remote objects in the network area. Battery replacement and recharging are almost impossible; therefore, the aim is to develop an efficient routing protocol for the sensor network. The Fuzzy Based Cluster Head Selection (FBCHS) protocol is proposed, which partitions the network into several regions based on node energy levels. The proposed protocol uses an artificial intelligence technique to select the Cluster Head (CH) based on maximum node Residual Energy (RE) and minimum distance. The transmission of data to the Base Station (BS) is accomplished via static clustering and the hybrid routing technique. The simulation results of the FBCHS protocol are com- pared to the SEP protocol and show improvement in the stability period and improved overall performance of the network.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"233 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87035179","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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