The problem of detecting image fragments characterized by high-frequency fluctuations in spatial intensity in the general case has not been previously considered in the literature. The article researches a sequence of known and new algorithms that allows detection and localization of such fragments. The geometric localization of the fragments is based on the Hough transform of the pixel array of the external contours of the connected components. The components connecting becomes possible due to the use of the oscillation function proposed by the authors. The oscillation function turns out to be an effective tool for highlighting intensity fluctuations zones in an image and is superior in reliability to alternative methods for detecting such zones, based, for example, on gradient methods. The article demonstrates examples of localization of the image fragments with different levels of background complexity.
{"title":"Localization of image fragments with high frequency intensity oscillation","authors":"A. Trubitsyn, Maksim Shadrin, Andrey Serezhin","doi":"10.32629/jai.v6i2.597","DOIUrl":"https://doi.org/10.32629/jai.v6i2.597","url":null,"abstract":"The problem of detecting image fragments characterized by high-frequency fluctuations in spatial intensity in the general case has not been previously considered in the literature. The article researches a sequence of known and new algorithms that allows detection and localization of such fragments. The geometric localization of the fragments is based on the Hough transform of the pixel array of the external contours of the connected components. The components connecting becomes possible due to the use of the oscillation function proposed by the authors. The oscillation function turns out to be an effective tool for highlighting intensity fluctuations zones in an image and is superior in reliability to alternative methods for detecting such zones, based, for example, on gradient methods. The article demonstrates examples of localization of the image fragments with different levels of background complexity.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43344319","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 building pieces for creating a secure cloud framework for data exchange with authenticated and authorized users are ECC and ABAC. So, the goal of this study is to improve access control and encryption-related methods in order to increase security. The elliptic curve is a key component of the comparative study of cloud encryption and access control techniques. This research project’s main goal is to provide a security architecture that combines authenticated access with attribute-based access control and better elliptic curve encryption. The second goal is to provide a better mapping strategy with reduced time and space complexity for elliptic curve encoding from plain text. To boost the performance of the standard ECC, a thorough algorithm focusing on designing an improved mapping mechanism for encoding plain text to elliptic curve points with excellent security has been included. The strength of the security should not be sacrificed in order to reduce security measures’ overhead costs. ABE is regarded as an effective way for protecting cloud data, according to study results. Because to the use of complex pairing processes, the same is difficult to use. As a result, a hybrid approach using ECC and ABAC performs better to handle the increasing processing capacity.
{"title":"A hybrid framework to enhance cloud security for storing and retrieving confidential data in clouds","authors":"N. Krishnaveni, C. Jayakumari","doi":"10.32629/jai.v6i2.566","DOIUrl":"https://doi.org/10.32629/jai.v6i2.566","url":null,"abstract":"The building pieces for creating a secure cloud framework for data exchange with authenticated and authorized users are ECC and ABAC. So, the goal of this study is to improve access control and encryption-related methods in order to increase security. The elliptic curve is a key component of the comparative study of cloud encryption and access control techniques. This research project’s main goal is to provide a security architecture that combines authenticated access with attribute-based access control and better elliptic curve encryption. The second goal is to provide a better mapping strategy with reduced time and space complexity for elliptic curve encoding from plain text. To boost the performance of the standard ECC, a thorough algorithm focusing on designing an improved mapping mechanism for encoding plain text to elliptic curve points with excellent security has been included. The strength of the security should not be sacrificed in order to reduce security measures’ overhead costs. ABE is regarded as an effective way for protecting cloud data, according to study results. Because to the use of complex pairing processes, the same is difficult to use. As a result, a hybrid approach using ECC and ABAC performs better to handle the increasing processing capacity.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44424391","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}
Rishabh Jain, Sunita Dhingra, Kamaldeep Joshi, A. Rana, Nitin Goyal
This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.
{"title":"Enhance traffic flow prediction with Real-Time Vehicle Data Integration","authors":"Rishabh Jain, Sunita Dhingra, Kamaldeep Joshi, A. Rana, Nitin Goyal","doi":"10.32629/jai.v6i2.574","DOIUrl":"https://doi.org/10.32629/jai.v6i2.574","url":null,"abstract":"This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45390646","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}
Traffic congestion is a major problem in urban areas, leading to increased travel time, air pollution, and fuel consumption. Road impedance function, which describes the relationship between traffic status and travel time, plays an important role in predicting travel time and managing traffic flow. Traditional methods for estimating road impedance function rely on manual calibration and may have limitations in reflecting the complexity of traffic patterns. To address these challenges, researchers have proposed various machine learning models for predicting travel time and road impedance function. In this paper, a hybrid particle swarm optimization—radial basis function neural network model is proposed for improving the accuracy of the road impedance function. The model takes into consideration various vehicle types and is validated using travel time data collected from a road section in Huai’an City, China. The effectiveness of the proposed model is compared with the traditional road impedance function calibrated by nonlinear regression. The experimental results indicate that the Mean Relative Error (MRE) of PSORBFNN is increased by 3.89% and 6.28% respectively when compared with DPNR training samples and validation samples. When compared with DPPSO training and validation samples, the MRE of PSORBFNN is increased by 2.87% and 3.3% respectively. These findings suggest that the proposed model could guide and assist traffic engineers and practitioners in predicting travel time on road sections with improved accuracy.
{"title":"A novel road traffic flow prediction model using hybrid Particle Swarm Optimization (PSO) and Radial Basis Function Neural Network (RBFNN)","authors":"Shanhua Zhang, H. An","doi":"10.32629/jai.v6i2.883","DOIUrl":"https://doi.org/10.32629/jai.v6i2.883","url":null,"abstract":"Traffic congestion is a major problem in urban areas, leading to increased travel time, air pollution, and fuel consumption. Road impedance function, which describes the relationship between traffic status and travel time, plays an important role in predicting travel time and managing traffic flow. Traditional methods for estimating road impedance function rely on manual calibration and may have limitations in reflecting the complexity of traffic patterns. To address these challenges, researchers have proposed various machine learning models for predicting travel time and road impedance function. In this paper, a hybrid particle swarm optimization—radial basis function neural network model is proposed for improving the accuracy of the road impedance function. The model takes into consideration various vehicle types and is validated using travel time data collected from a road section in Huai’an City, China. The effectiveness of the proposed model is compared with the traditional road impedance function calibrated by nonlinear regression. The experimental results indicate that the Mean Relative Error (MRE) of PSORBFNN is increased by 3.89% and 6.28% respectively when compared with DPNR training samples and validation samples. When compared with DPPSO training and validation samples, the MRE of PSORBFNN is increased by 2.87% and 3.3% respectively. These findings suggest that the proposed model could guide and assist traffic engineers and practitioners in predicting travel time on road sections with improved accuracy.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49167662","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}
Purpose: New priorities for research are emerging in nutrition and sports sciences. These include application of artificial intelligence (AI) and coactive life coaching (CoALC) in nutrition and fitness worlds. Building off such link, this review aims to explore the up-to-date scientific literature at the intersections of AI and CoALC trends, and nutrition and fitness. Design/methodology/approach: A narrative review based on systems thinking approach was used to explore and discuss how AI concepts can affect nutrition and fitness matters, and how life coaching has attempted to deal with healthy lifestyles matters and with considerations of unintended related-consequences and health ethics. Findings: Systems thinking and transdisciplinary approaches could provide more understandings on how to better evaluate the impacts of AI concepts and CoALC and how they are significantly changing nutrition and fitness paradigms of production and consumption. Food and sports systems must continue to build their capacities to understand, regulate, and adapt to these changes. Originality/value: This study suggests a novel argumentative scenario that could be creatively adapted to generate effective strategies and advice on a controversial topic such as nutrition and fitness that involves values, personal attitudes and social behaviors. Practical implications: This paper provides a forward view of the use and impact of AI and CoALC on our dietary patterns and fitness behaviours, and on interventions methods in nutrition and health science research.
{"title":"Are artificial intelligence and co-active life coaching the future designers of nutrition and fitness matters?","authors":"S. Hamadeh","doi":"10.32629/jai.v6i2.608","DOIUrl":"https://doi.org/10.32629/jai.v6i2.608","url":null,"abstract":"Purpose: New priorities for research are emerging in nutrition and sports sciences. These include application of artificial intelligence (AI) and coactive life coaching (CoALC) in nutrition and fitness worlds. Building off such link, this review aims to explore the up-to-date scientific literature at the intersections of AI and CoALC trends, and nutrition and fitness. Design/methodology/approach: A narrative review based on systems thinking approach was used to explore and discuss how AI concepts can affect nutrition and fitness matters, and how life coaching has attempted to deal with healthy lifestyles matters and with considerations of unintended related-consequences and health ethics. Findings: Systems thinking and transdisciplinary approaches could provide more understandings on how to better evaluate the impacts of AI concepts and CoALC and how they are significantly changing nutrition and fitness paradigms of production and consumption. Food and sports systems must continue to build their capacities to understand, regulate, and adapt to these changes. Originality/value: This study suggests a novel argumentative scenario that could be creatively adapted to generate effective strategies and advice on a controversial topic such as nutrition and fitness that involves values, personal attitudes and social behaviors. Practical implications: This paper provides a forward view of the use and impact of AI and CoALC on our dietary patterns and fitness behaviours, and on interventions methods in nutrition and health science research.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46882487","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}
Low back pain occurs because of the degeneration in Intervertebral Disc (IVD) namely: Disc Desiccation, Disc Bulge, and Disc Herniation, etc. To detect disc degeneration, a doctor often physically evaluates the Magnetic Resonance Imaging (MRI), which takes time and is dependent on the doctor’s expertise and training. Degeneration diagnosis that is automated can ease some of the doctor’s workload. On 378 IVDs for 63 patients, the proposed method is trained, tested, and assessed. According to the performance evaluation, the proposed Local Sub-Rhombus Binary Relationship (LS-RBRP) and Random Forrest (RF) classifier approach gives an overall accuracy of 90.2%. The proposed approach also produces a higher sensitivity, specificity, precision, and F-score of 80.8%, 90.3%, 90.4%, and 84.5%, respectively, when diagnosing the normal IVD, disc desiccation, and disc bulge in the lumbar MRI.
{"title":"Automated spinal MRI-based diagnostics of disc bulge and desiccating using LS-RBRP with RF","authors":"S. Shirly, R. Venkatesan, D. David, T. Jebaseeli","doi":"10.32629/jai.v6i2.938","DOIUrl":"https://doi.org/10.32629/jai.v6i2.938","url":null,"abstract":"Low back pain occurs because of the degeneration in Intervertebral Disc (IVD) namely: Disc Desiccation, Disc Bulge, and Disc Herniation, etc. To detect disc degeneration, a doctor often physically evaluates the Magnetic Resonance Imaging (MRI), which takes time and is dependent on the doctor’s expertise and training. Degeneration diagnosis that is automated can ease some of the doctor’s workload. On 378 IVDs for 63 patients, the proposed method is trained, tested, and assessed. According to the performance evaluation, the proposed Local Sub-Rhombus Binary Relationship (LS-RBRP) and Random Forrest (RF) classifier approach gives an overall accuracy of 90.2%. The proposed approach also produces a higher sensitivity, specificity, precision, and F-score of 80.8%, 90.3%, 90.4%, and 84.5%, respectively, when diagnosing the normal IVD, disc desiccation, and disc bulge in the lumbar MRI.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41588130","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}
Ram Krishna, R. Yaduvanshi, Harendra Singh, A. Rana, Nitin Goyal, Ravinder Kumar
In this paper, an equivalent combination of series and parallel R-L-C high-pass filter circuit is derived for a nano (quantum) antenna for the Internet of thing (IoT) based sensors for speedy data or organ image displaying in medical line surgeries. The proposed method utilized the sample frequency behavior of characteristics mode to develop a fundamental building block that superimposes to create the complete response. The resonance frequency, input impedance, and quality factor have been evaluated along with basic and higher-order resonating modes. The relation between quality factor, bandwidth, resonance frequency, and selectivity for higher order, increases the quantum circuits in terms of increased order of a filter, quality factor, and odd and even harmonics factors. Therefore, the basic circuits derivation factor of frequency coefficients are expanded in terms of polynomials and then they are expressed as a simple rational function from which the basic circuit parameters are calculated. In this circuit input impedance of each circuit’s element is complex. The real part of input impedance depends on frequency, depending on the frequency positive or negative value of the resistor, and the imaginary part of impedance modelling an inductor or capacitor due to the value of frequency. At cutoff frequency 511 THz, z11 and VSWR parameters are 34 Ω and 1.11, respectively. The proposed quantum DRA is tested at 5 THz, 10 THz, and 500 THz by calculating the electrical parameters like R, L, C and model performance is quite good as compared to existing ones. The dynamic impedance is dependent on the skin effect and enhances the detailed discussion below. The utilization of optical or quantum DRAs is as optical sensors in biomedical engineering, speedy wireless communication, and optical image solutions. Analyte material has been used for monitoring frequency deviation.
{"title":"Mathematical modeling and parameter analysis of quantum antenna for IoT sensor-based biomedical applications","authors":"Ram Krishna, R. Yaduvanshi, Harendra Singh, A. Rana, Nitin Goyal, Ravinder Kumar","doi":"10.32629/jai.v6i2.578","DOIUrl":"https://doi.org/10.32629/jai.v6i2.578","url":null,"abstract":"In this paper, an equivalent combination of series and parallel R-L-C high-pass filter circuit is derived for a nano (quantum) antenna for the Internet of thing (IoT) based sensors for speedy data or organ image displaying in medical line surgeries. The proposed method utilized the sample frequency behavior of characteristics mode to develop a fundamental building block that superimposes to create the complete response. The resonance frequency, input impedance, and quality factor have been evaluated along with basic and higher-order resonating modes. The relation between quality factor, bandwidth, resonance frequency, and selectivity for higher order, increases the quantum circuits in terms of increased order of a filter, quality factor, and odd and even harmonics factors. Therefore, the basic circuits derivation factor of frequency coefficients are expanded in terms of polynomials and then they are expressed as a simple rational function from which the basic circuit parameters are calculated. In this circuit input impedance of each circuit’s element is complex. The real part of input impedance depends on frequency, depending on the frequency positive or negative value of the resistor, and the imaginary part of impedance modelling an inductor or capacitor due to the value of frequency. At cutoff frequency 511 THz, z11 and VSWR parameters are 34 Ω and 1.11, respectively. The proposed quantum DRA is tested at 5 THz, 10 THz, and 500 THz by calculating the electrical parameters like R, L, C and model performance is quite good as compared to existing ones. The dynamic impedance is dependent on the skin effect and enhances the detailed discussion below. The utilization of optical or quantum DRAs is as optical sensors in biomedical engineering, speedy wireless communication, and optical image solutions. Analyte material has been used for monitoring frequency deviation.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46325807","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}
Adaptive transport technologies based on vehicular ad hoc networks (VANET) has proven considerable potential in light of the developing expansion of driver assistance and automobile telecommunication systems. However, confidentiality and safety are the vital challenges in vehicular ad hoc networks which could be seriously impaired by malicious attackers. While protecting vehicle privacy from threats, it is imperative to stop internal vehicles from putting out bogus messages. Considering these issues, a novel machine learning based message authentication combined with blockchain and inter planetary file system (IPFS) is proposed to achieve message dissemination in a secured way. Blockchain is the emerging technology which attempts to solve these problems by producing tamper proof events of records in a distributed environment and inter planetary file system used in the framework is a protocol designed to store the event with content addressability. Along with this combined technology, the source metadata information collected from the inter planetary file system is stored via a smart contract and uploaded to the distributed ledger technology (DLT). For performing event authentication, K-means clustering and support vector machine (SVM) classifier is employed in this framework. K-means clustering performs clustering of vehicles and it is marked malicious or not malicious. After clustering, support vector machine classifier detects the malicious event messages. By this way, the malicious messages are identified and it is dropped. Only the secure messages are forwarded in the network. Finally, our approach is capable of creating a safe and decentralized vehicular ad hoc network architecture with accountability and confidentiality through theoretical study and simulations.
{"title":"Novel machine learning based authentication technique in VANET system for secure data transmission","authors":"Anand N. Patil, Sujata V. Mallapur","doi":"10.32629/jai.v6i2.828","DOIUrl":"https://doi.org/10.32629/jai.v6i2.828","url":null,"abstract":"Adaptive transport technologies based on vehicular ad hoc networks (VANET) has proven considerable potential in light of the developing expansion of driver assistance and automobile telecommunication systems. However, confidentiality and safety are the vital challenges in vehicular ad hoc networks which could be seriously impaired by malicious attackers. While protecting vehicle privacy from threats, it is imperative to stop internal vehicles from putting out bogus messages. Considering these issues, a novel machine learning based message authentication combined with blockchain and inter planetary file system (IPFS) is proposed to achieve message dissemination in a secured way. Blockchain is the emerging technology which attempts to solve these problems by producing tamper proof events of records in a distributed environment and inter planetary file system used in the framework is a protocol designed to store the event with content addressability. Along with this combined technology, the source metadata information collected from the inter planetary file system is stored via a smart contract and uploaded to the distributed ledger technology (DLT). For performing event authentication, K-means clustering and support vector machine (SVM) classifier is employed in this framework. K-means clustering performs clustering of vehicles and it is marked malicious or not malicious. After clustering, support vector machine classifier detects the malicious event messages. By this way, the malicious messages are identified and it is dropped. Only the secure messages are forwarded in the network. Finally, our approach is capable of creating a safe and decentralized vehicular ad hoc network architecture with accountability and confidentiality through theoretical study and simulations.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45588953","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 importance of energy conservation and emission reduction has become the consensus of the international community, and Iraq is also actively improving the urban public transportation system to control carbon emissions. This paper collects panel data of Tikrit city in Iraq in the past 3 years, constructs a random effect variable coefficient model, and studies the impact of the development of urban low-carbon transportation system on the energy consumption structure. The study finds that the government can use public transportation pricing strategies to influence consumers. In order to realize the optimization of energy consumption structure, the impact of electric vehicles on energy consumption structure will decrease with the increase of urban development. The transportation sector can increase the purchase and travel costs of traditional cars by restricting travel, purchases, and charging parking fees, which affects the number of private cars and reduces the obstacles to optimizing the energy consumption structure. The government should increase financial subsidies, improve rail transit and reasonable bus (electric) vehicle operation systems, increase investment in new energy vehicle research and development, and encourage high energy density and low power consumption technologies. development, increase residents’ demand for new energy passenger vehicles, and optimize the energy consumption structure.
{"title":"Energy consumption structure model considering urban green and low-carbon transportation","authors":"Kaled H. Mudhee","doi":"10.32629/jai.v6i2.879","DOIUrl":"https://doi.org/10.32629/jai.v6i2.879","url":null,"abstract":"The importance of energy conservation and emission reduction has become the consensus of the international community, and Iraq is also actively improving the urban public transportation system to control carbon emissions. This paper collects panel data of Tikrit city in Iraq in the past 3 years, constructs a random effect variable coefficient model, and studies the impact of the development of urban low-carbon transportation system on the energy consumption structure. The study finds that the government can use public transportation pricing strategies to influence consumers. In order to realize the optimization of energy consumption structure, the impact of electric vehicles on energy consumption structure will decrease with the increase of urban development. The transportation sector can increase the purchase and travel costs of traditional cars by restricting travel, purchases, and charging parking fees, which affects the number of private cars and reduces the obstacles to optimizing the energy consumption structure. The government should increase financial subsidies, improve rail transit and reasonable bus (electric) vehicle operation systems, increase investment in new energy vehicle research and development, and encourage high energy density and low power consumption technologies. development, increase residents’ demand for new energy passenger vehicles, and optimize the energy consumption structure.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42310138","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}
Peter Appiahene, Vijayakumar Varadarajan, Zhang Tao, Stephen Afrifa
Nowadays, social media has become a forum for people to express their views on issues such as sexual orientation, legislation, and taxes. Sexual orientation refers to individuals with whom you are attracted and wish to be engaged. In the world, many people are regarded as having different sexual orientations. People categorized as lesbian, gay, bisexual, transgender, queer, and many more (LGBTQ+) have many sexual orientations. Because of the public stigmatization of LGBTQ+ persons, many turn to social media to express themselves, sometimes anonymously. The present study aims to use natural language processing (NLP) and machine learning (ML) approaches to assess the experiences of LGBTQ+ persons. To train the data, the study used lexicon-based sentiment analysis (SA) and six distinct machine classifiers, including logistic regression (LR), support vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF), and gradient boosting (GB). Individuals are positive about LGBTQ concerns, according to the SA results; yet, prejudice and harsh statements against the LGBTQ people persist in many regions where they live, according to the negative sentiment ratings. Furthermore, using LR, SVM, NB, DT, RF, and GB, the ML classifiers attained considerable accuracy values of 97%, 96%, 88%, 100%, 92%, and 91%, respectively. The performance assessment metrics used obtained significant recall and precision values. This study will assist the government, non-governmental organizations, and rights advocacy groups make educated decisions about LGBTQ+ concerns in order to ensure a sustainable future and peaceful coexistence.
{"title":"Experiences of sexual minorities on social media: A study of sentiment analysis and machine learning approaches","authors":"Peter Appiahene, Vijayakumar Varadarajan, Zhang Tao, Stephen Afrifa","doi":"10.32629/jai.v6i2.623","DOIUrl":"https://doi.org/10.32629/jai.v6i2.623","url":null,"abstract":"Nowadays, social media has become a forum for people to express their views on issues such as sexual orientation, legislation, and taxes. Sexual orientation refers to individuals with whom you are attracted and wish to be engaged. In the world, many people are regarded as having different sexual orientations. People categorized as lesbian, gay, bisexual, transgender, queer, and many more (LGBTQ+) have many sexual orientations. Because of the public stigmatization of LGBTQ+ persons, many turn to social media to express themselves, sometimes anonymously. The present study aims to use natural language processing (NLP) and machine learning (ML) approaches to assess the experiences of LGBTQ+ persons. To train the data, the study used lexicon-based sentiment analysis (SA) and six distinct machine classifiers, including logistic regression (LR), support vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF), and gradient boosting (GB). Individuals are positive about LGBTQ concerns, according to the SA results; yet, prejudice and harsh statements against the LGBTQ people persist in many regions where they live, according to the negative sentiment ratings. Furthermore, using LR, SVM, NB, DT, RF, and GB, the ML classifiers attained considerable accuracy values of 97%, 96%, 88%, 100%, 92%, and 91%, respectively. The performance assessment metrics used obtained significant recall and precision values. This study will assist the government, non-governmental organizations, and rights advocacy groups make educated decisions about LGBTQ+ concerns in order to ensure a sustainable future and peaceful coexistence.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46906545","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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