Journal of Autonomous Intelligence最新文献

{"title":"Deciphering Themes and Trajectories: A Bibliometric Study on Learning Design & Technology over Four Decades","authors":"Shikha Mann, Sakshi Mann, Yogesh Mahajan, Amruta Deshpande, Amit Mittal","doi":"10.32629/jai.v7i5.1697","DOIUrl":"https://doi.org/10.32629/jai.v7i5.1697","url":null,"abstract":"The intersection of learning design with technology continually transforms the educational scene prompting deepstudy. Through an exhaustive bibliometric analysis powered by the myriad features of the Biblioshiny toolset from dataprocured from Scopus vast CSV file resource spanning 1977 to 2023. The study maps the evolution of themes withinthis field. It uncovers how initial conversations about e-learning foundations segued into contemporary dialogues onmobile learning approaches as well as pedagogical design intricacies. Central to this research is the identification ofleading figures in the arena. Highlighting paramount authors or resources offers a tailored breakdown on texts crucial toshaping both past discussions as well as the directions learning design with technology might take in future. Analyzingscientific production trends helps understand shifts over time in the space between innovation bursts to periodsdemanding thoughtful consolidation. By intertwining theme progressions along with key contributors or resourcefindings with production pattern assessments, this research offers a holistic bibliometric panorama. It thereforecondenses learning design with its technology-rich history in conjunction with the current state to guide future researchdirections in this critical educational field.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371594","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}

{"title":"Virtual Reality and Augmented Reality-Based Digital Pattern Design in the Context of the Blockchain Technology Framework","authors":"Youjian Wang, Muling Sheng, Dahlan Bin Abdul Ghani","doi":"10.32629/jai.v7i5.1663","DOIUrl":"https://doi.org/10.32629/jai.v7i5.1663","url":null,"abstract":"Through an in-depth analysis and evaluation of digital design in traditional graphic art, this paper focuses specifically on how blockchain can enhance the digital representation and management of these designs. The integration of AI and digital design art is taking a major leap forward, breathing new life into traditional cultural elements by harnessing AI's capabilities to create immersive interactive experiences. This paper uses fuzzy comprehensive evaluation method to evaluate the digital design of classic pattern art, highlighting the richness, interest, innovation and digitalization of pattern. Blockchain technology provides a secure and decentralized platform for the storage and verification of digital graphic art, preserving the cultural and artistic value of the design. By linking digital design to blockchain, we can create a transparent and verifiable record of the creation and ownership of each artwork, promoting ethical practices in the digital art market. The study shows that the combination of VR and AR with blockchain technology not only revolutionizes the way traditional Chinese pictorial paintings are perceived and appreciated, but also provides a powerful framework for managing and protecting related digital assets. The final scores obtained through the fuzzy comprehensive evaluation - 86.17 for richness, 89.24 for liking, 90.61 for innovation, and 91.38 for digitalization - underscore the significant advantages that the application of these technologies brings to the digital design of traditional pattern art.In conclusion, the application of blockchain technology in the realm of digital design of traditional pattern art is not merely a technological advancement but a cultural and ethical imperative. It ensures the preservation of traditional art forms in their digital manifestations, while also opening up new avenues for innovation and global appreciation.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379321","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}

{"title":"Securing large-scale data processing: Integrating lightweight cryptography in MapReduce","authors":"Marwa Khadji, Samira Khoulji, M. L. Kerkeb, Inass Khadji","doi":"10.32629/jai.v7i4.1390","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1390","url":null,"abstract":"In today’s rapidly evolving digital landscape, the imperative of data security stands paramount. With the proliferation of sensitive information being stored and transmitted online, the necessity for robust encryption algorithms has grown exponentially. However, the suitability of traditional encryption methods in resource-constrained settings, like mobile devices and cloud computing, remains a concern due to their computational intensity. To address this, researchers have introduced a novel category of encryption algorithms known as lightweight cryptography algorithms. These cryptographic solutions are designed to offer robust security while minimizing computational demands, thus striking a harmonious balance between security and efficiency. While lightweight cryptography algorithms present a promising solution, their adequacy for applications demanding exceptionally high security, particularly within Big Data environments, warrants careful consideration. In this study, we presented a novel approach involving the utilization of lightweight cryptography algorithms within the MapReduce framework. By subjecting these algorithms to rigorous experimentation, we assessed their performance using software-oriented metrics from various dimensions.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"353 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799016","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}

{"title":"Securing large-scale data processing: Integrating lightweight cryptography in MapReduce","authors":"Marwa Khadji, Samira Khoulji, M. L. Kerkeb, Inass Khadji","doi":"10.32629/jai.v7i4.1390","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1390","url":null,"abstract":"In today’s rapidly evolving digital landscape, the imperative of data security stands paramount. With the proliferation of sensitive information being stored and transmitted online, the necessity for robust encryption algorithms has grown exponentially. However, the suitability of traditional encryption methods in resource-constrained settings, like mobile devices and cloud computing, remains a concern due to their computational intensity. To address this, researchers have introduced a novel category of encryption algorithms known as lightweight cryptography algorithms. These cryptographic solutions are designed to offer robust security while minimizing computational demands, thus striking a harmonious balance between security and efficiency. While lightweight cryptography algorithms present a promising solution, their adequacy for applications demanding exceptionally high security, particularly within Big Data environments, warrants careful consideration. In this study, we presented a novel approach involving the utilization of lightweight cryptography algorithms within the MapReduce framework. By subjecting these algorithms to rigorous experimentation, we assessed their performance using software-oriented metrics from various dimensions.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"110 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858717","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}

{"title":"Design analysis of intelligent controller to minimize harmonic distortion and power loss of wind energy conversion system (grid connected)","authors":"V. Maurya, J. P. Pandey, Chitranjan Gaur, Shweta Singh","doi":"10.32629/jai.v7i4.1036","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1036","url":null,"abstract":"The controlling of internal parametric variations in addition to the non-linearity of a large conversion system of wind energy (WECS) is prime challenges to make the most of the generated energy, with less power loss and secure the proficiency (η) integration conventional grid. An adjustable speed control structure of grid-connected conversion system of wind energy (WECS), with the help of a Permanent Magnet Type Synchronous Generator with intelligent controller minimizes the power loss. The control system incorporates a pair of controllers dedicated to the converters of both the generator and grid edge. The controller at the generator side has the main function is to optimize power that can be withdrawal from the wind by intelligently regulating the turbine’s rotational speed. Meanwhile, the grid edge converter effectively manages active and reactive power by manipulating the d & q-axis current components, respectively. This paper discusses about the improvement in performance of the system when using Neuro-Fuzzy system as compared to Neural Network and Management of energy deliver system via direct control method. The findings reveal that the training time for Artificial Neural Networks (ANNs) is substantial, leading to the Neural Network-Direct Power Contol (NN-DPC) approach being the slowest option among the alternatives. Additionally, the NF-DPC system is less time-consuming than the NN-DPC, with a recorded duration of 24 seconds compared to the NN-DPC’s observation of 8 min and 5 s. However, it is worth noting that the NF-DPC system is somewhat more time-intensive than Common-Direct Power Contol (C-DPC).","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"266 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799564","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}

{"title":"Design analysis of intelligent controller to minimize harmonic distortion and power loss of wind energy conversion system (grid connected)","authors":"V. Maurya, J. P. Pandey, Chitranjan Gaur, Shweta Singh","doi":"10.32629/jai.v7i4.1036","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1036","url":null,"abstract":"The controlling of internal parametric variations in addition to the non-linearity of a large conversion system of wind energy (WECS) is prime challenges to make the most of the generated energy, with less power loss and secure the proficiency (η) integration conventional grid. An adjustable speed control structure of grid-connected conversion system of wind energy (WECS), with the help of a Permanent Magnet Type Synchronous Generator with intelligent controller minimizes the power loss. The control system incorporates a pair of controllers dedicated to the converters of both the generator and grid edge. The controller at the generator side has the main function is to optimize power that can be withdrawal from the wind by intelligently regulating the turbine’s rotational speed. Meanwhile, the grid edge converter effectively manages active and reactive power by manipulating the d & q-axis current components, respectively. This paper discusses about the improvement in performance of the system when using Neuro-Fuzzy system as compared to Neural Network and Management of energy deliver system via direct control method. The findings reveal that the training time for Artificial Neural Networks (ANNs) is substantial, leading to the Neural Network-Direct Power Contol (NN-DPC) approach being the slowest option among the alternatives. Additionally, the NF-DPC system is less time-consuming than the NN-DPC, with a recorded duration of 24 seconds compared to the NN-DPC’s observation of 8 min and 5 s. However, it is worth noting that the NF-DPC system is somewhat more time-intensive than Common-Direct Power Contol (C-DPC).","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"174 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859455","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}

{"title":"Correlating forensic data for enhanced network crime investigations: Techniques for packet sniffing, network forensics, and attack detection","authors":"Dhwaniket Kamble, Santosh B. Rathod, Manish Bhelande, Alok Shah, Pravin Sapkal","doi":"10.32629/jai.v7i4.1272","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1272","url":null,"abstract":"In today’s digitally saturated world, digital devices are frequently involved in criminal events as targets, mediums, or witnesses. Forensic investigations encompass the collection, recovery, analysis, and presentation of information stored on network devices, with specific relevance to network crimes. Such investigations often necessitate the use of diverse analysis tools and methods. This study introduces techniques that support digital investigators in correlating and presenting information derived from forensic data, with a primary focus on packet sniffing, network forensics, and attack detection. By leveraging these methodologies, investigators aim to achieve more valuable reconstructions of events or actions, resulting in enhanced case conclusions. The study emphasizes the importance of understanding how malware operates within the context of the Internet. It explores packet sniffing techniques to capture and analyze network data, enabling investigators to detect and trace the origins of malicious activities. Additionally, it delves into the realm of network forensics, proposing effective methods for gathering evidence from network devices and reconstructing digital events. Furthermore, the study covers the significance of attack detection in network crime investigations. It highlights techniques to identify and analyze attack patterns, facilitating the identification of perpetrators and their motivations. By correlating information obtained from forensic data, investigators can obtain comprehensive insights into the nature and impacts of network crimes. Overall, this study aims to arm digital investigators with the knowledge and tools necessary to navigate the complexities of packet sniffing, network forensics, and attack detection. By incorporating these techniques into their investigations, investigators can achieve more robust reconstructions of events, draw well-informed conclusions, and contribute to the successful resolution of network crime cases.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"53 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139865205","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}

{"title":"Exploration of the cultural attributes of Chinese character sculpture using machine learning technology","authors":"Zhen Luo","doi":"10.32629/jai.v7i4.1471","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1471","url":null,"abstract":"The article employs machine learning, specifically the CLIP (Contrastive Language-Image Pretraining) model, to analyze Chinese character sculptures’ cultural attributes. It overcomes challenges in multi-dimensional data processing and high digitization costs. The process involves normalizing sculpture images, using FastText for vector representations of Chinese characters, and mapping text to the same embedding space as images for word embedding. The CLIP model, through unsupervised training, minimizes the negative logarithmic likelihood loss between image and text embeddings to establish cultural attribute representations. Key findings include the CLIP model’s improved performance over the M3 model, with a 5.4% higher average AUC. The model demonstrates high efficiency and accuracy, evident in its low RMSE (0.034) and MAE (0.025) and fast analysis time of 182 ms. This approach effectively and accurately analyzes the cultural attributes of Chinese character sculptures, addressing existing research gaps.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"19 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139863946","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}

{"title":"Exploration of the cultural attributes of Chinese character sculpture using machine learning technology","authors":"Zhen Luo","doi":"10.32629/jai.v7i4.1471","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1471","url":null,"abstract":"The article employs machine learning, specifically the CLIP (Contrastive Language-Image Pretraining) model, to analyze Chinese character sculptures’ cultural attributes. It overcomes challenges in multi-dimensional data processing and high digitization costs. The process involves normalizing sculpture images, using FastText for vector representations of Chinese characters, and mapping text to the same embedding space as images for word embedding. The CLIP model, through unsupervised training, minimizes the negative logarithmic likelihood loss between image and text embeddings to establish cultural attribute representations. Key findings include the CLIP model’s improved performance over the M3 model, with a 5.4% higher average AUC. The model demonstrates high efficiency and accuracy, evident in its low RMSE (0.034) and MAE (0.025) and fast analysis time of 182 ms. This approach effectively and accurately analyzes the cultural attributes of Chinese character sculptures, addressing existing research gaps.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"77 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804351","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}

{"title":"Correlating forensic data for enhanced network crime investigations: Techniques for packet sniffing, network forensics, and attack detection","authors":"Dhwaniket Kamble, Santosh B. Rathod, Manish Bhelande, Alok Shah, Pravin Sapkal","doi":"10.32629/jai.v7i4.1272","DOIUrl":"https://doi.org/10.32629/jai.v7i4.1272","url":null,"abstract":"In today’s digitally saturated world, digital devices are frequently involved in criminal events as targets, mediums, or witnesses. Forensic investigations encompass the collection, recovery, analysis, and presentation of information stored on network devices, with specific relevance to network crimes. Such investigations often necessitate the use of diverse analysis tools and methods. This study introduces techniques that support digital investigators in correlating and presenting information derived from forensic data, with a primary focus on packet sniffing, network forensics, and attack detection. By leveraging these methodologies, investigators aim to achieve more valuable reconstructions of events or actions, resulting in enhanced case conclusions. The study emphasizes the importance of understanding how malware operates within the context of the Internet. It explores packet sniffing techniques to capture and analyze network data, enabling investigators to detect and trace the origins of malicious activities. Additionally, it delves into the realm of network forensics, proposing effective methods for gathering evidence from network devices and reconstructing digital events. Furthermore, the study covers the significance of attack detection in network crime investigations. It highlights techniques to identify and analyze attack patterns, facilitating the identification of perpetrators and their motivations. By correlating information obtained from forensic data, investigators can obtain comprehensive insights into the nature and impacts of network crimes. Overall, this study aims to arm digital investigators with the knowledge and tools necessary to navigate the complexities of packet sniffing, network forensics, and attack detection. By incorporating these techniques into their investigations, investigators can achieve more robust reconstructions of events, draw well-informed conclusions, and contribute to the successful resolution of network crime cases.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"14 s2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805080","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}