人工智能技术学报(英文)最新文献

{"title":"Web Authentication Biometric 3D Animated CAPTCHA System Using Artificial Intelligence and Machine Learning Approach","authors":"N. Bora, Dinesh Chandra Jain","doi":"10.37965/jait.2023.0216","DOIUrl":"https://doi.org/10.37965/jait.2023.0216","url":null,"abstract":"The internet and web security are integral aspects of our daily lives. Many commercial firms provide clients with internet services. For web access, it is assumed that only the genuine user, who is a human, will register. Yet automated hacking programs can also do registrations with fake data that consume a lot of bandwidth, slowing down or occasionally even shutting down websites, leading to Distributed denial-of-service (DDOS) attacks. Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) is the solution. Complex CAPTCHA is challenging for humans to recognize, but simple CAPTCHA is simple for AI to decipher. With the developments in neural networks and machine learning bots are mimicking humans and it is becoming difficult to distinguish humans and bots apart. This generated a need to think of some more innovative and novel CAPTCHA. Now, utilizing the same AIML approach to increase the efficacy of CAPTCHA and make it stronger against the bot attack. Biometric 3D Animated (B3DA) Algorithm proposed in this research is a novel approach based on the Face Detection AI algorithm along with handwritten 3D animated characters selected randomly to create a string which makes CAPTCHA simple that humans can identify but very difficult for bots. The test results have proven this to be a very robust CAPTCHA. The machine learning algorithm employed will keep on increasing the efficacy of this CAPTCHA each time the bot tries to break this.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41417440","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":"Classification of Immature and Mature Coffee Beans Using Texture Features and Medium K Nearest Neighbor","authors":"Edwin R. Arboleda","doi":"10.37965/jait.2023.0203","DOIUrl":"https://doi.org/10.37965/jait.2023.0203","url":null,"abstract":"In  this  study , texture features  namely entropy,  contrast, energy and   homogeneity were  extracted  from  mature and  immature coffee  beans using  image  processing  and  the  values  were inputted  to MATLAB’s Classification Learner  App for  discrimination. Among  the  23 machine  learning  algorithms  the  best  performance  was  achieved  by  medium  K  nearest  neighbor   which  has 97 %  accuracy  and 0.14574 seconds  in speed.  When compared to previous studies that used RGB and HSV color features to differentiate mature and immature coffee beans, it can be concluded that texture features are far superior in distinguishing the two coffee bean groups.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41831576","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":"Machine Learning Approach for Phishing Attack Detection","authors":"Tarun Choudhary, Siddhesh Mhapankar, Rohit Bhddha, Ashish Kharuk, Rohini Patil","doi":"10.37965/jait.2023.0197","DOIUrl":"https://doi.org/10.37965/jait.2023.0197","url":null,"abstract":"Phishing is the easiest method for gathering sensitive information from unwary people. Phishers seek to get private data including passwords, login information, and bank account details. Cyber security experts are actively seeking for trustworthy and effective ways to identify phishing websites. In order to distinguish between legal and phishing URLs, we used machine learning (ML) technology. In this research work using ML technology extraction and analysis of both types of URLs was performed. Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were used to identify phishing websites. The goal was to identify phishing URLs and determine the most effective ML technique by comparing the accuracy rates of each algorithm. In this, proposed methodology two datasets were used. The accuracy of models was calculated on Phishtank and UCI dataset using kfold, feature selection and hyperparameter tuning method. Performance measures precision, recall and F1-score and Receiver Operating Characteristics (ROC) curve were calculated. RF provided an accuracy of 98.80% and 97.87% on the Phishtank dataset and UCI respectively. Highest precision, recall, F1-score value was 99% each and AUC-ROC value was 99.89% with Phishtank dataset. Validation with other researchers showed better results with proposed methodology. Therefore this methodology can be of help to identify phishing websites.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47424753","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":"Human Activity Recognition in a Realistic and Multiview Environment Based on Two-Dimensional Convolutional Neural Network","authors":"Ashish Khare, A. Kushwaha, O. Prakash","doi":"10.37965/jait.2023.0163","DOIUrl":"https://doi.org/10.37965/jait.2023.0163","url":null,"abstract":"Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42455826","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":"Studies on Multi-Criteria Decision-Making Based Healthcare Systems Using The Machine Learning","authors":"Sk Anamul Hoda, A. Mondal","doi":"10.37965/jait.2023.0167","DOIUrl":"https://doi.org/10.37965/jait.2023.0167","url":null,"abstract":"There is a lot of information in healthcare and medical records. However, it is challenging for humans to turn data into information and spot hidden patterns in today's digitally-based culture. Effective decision-support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments as well as in different healthcare management activities. Research paper presented an extensive literature survey for Healthcare systems using the machine learning that is based on Multiple criteria decision making (MCDM). Various existing studies are considered for review and a critical analysis is being done through the reviews study which can help to the researchers to explore further research area to cater the need of the field","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46398963","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":"Intelligent Network Slicing in V2X Networks – A Comprehensive Review","authors":"M. Abood, Hua Wang, Dongxuan He, Ziqi Kang, Agnes Kawoya","doi":"10.37965/jait.2023.0208","DOIUrl":"https://doi.org/10.37965/jait.2023.0208","url":null,"abstract":"ABSTRACT- The rise of the internet of things (IoT) and autonomous systems has made connecting vehicles more critical. Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications. With the advent of 5G networks, vehicle-to-everything (V2X) networks are expected to be highly intelligent, reside on superfast, reliable, and low-latency connections. Network slicing, Machine Learning (ML), and Deep Learning (DL) are related to network automation and optimization in V2X communication. Machine Learning and Deep Learning (ML/DL) with network slicing aims to optimize the performance, reliability of the V2X network, personalized services, reduced costs, and scalability and enhance the overall driving experience. These advantages can ultimately lead to a safer and more efficient transportation system. However, existing Long-Term Evolution (LTE) systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels. Machine learning algorithms mitigate complexity levels, which can be highly instrumental in such vehicular communication systems. This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing, a different configuration of slicing, the requirements of slicing, and the ML algorithm used to control and manage to slice. This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases, focusing on V2X network slicing and considering efficient control and management. The enabler technologies are considered in light of the network requirements, particular configurations, and the underlying methods and algorithms, with a review of some critical challenges and possible solutions available. The paper concludes with a future roadmap by discussing some open research issues and future directions.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45524500","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":"IOT Based Wrist Band for Women Safety","authors":"V.Ebenezer, Uvaana Falicica J, M. Roshni Thanka, Rithika Baskaran, Agatha Celesty, Sejal R Eden","doi":"10.37965/jait.2023.0179","DOIUrl":"https://doi.org/10.37965/jait.2023.0179","url":null,"abstract":"In the modern world, women now have tremendous success in every field. They can play, learn, and earn as much as men. But what about safety? Do they have the same secure environment that men and boys do? The answer is ”NO”. Women and girls have been subjected to numerous incidents, including acid throwing, rape, kidnapping, and harassment. It is common to read a lot of news like this in newspapers every day. These incidents make women feel unsafe in this society. Our freedom came a long time ago, but women still lack complete security in this society. All women can’t fight or shout all the time when some danger is happening to them. What can the physically challenged person and Children do? To make women feel safe, we designed ”Wrist Band” using IoT for Women Safety. As the sensors sense information from the body, it will always update the information such as pulse, temperature and vibration to the well wishers through the blynk app","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46428438","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 of Fine Life Cycle Prediction System for Failure of Medical Equipment","authors":"Haowei Ma, Cheng Xu, J. Yang","doi":"10.37965/jait.2023.0161","DOIUrl":"https://doi.org/10.37965/jait.2023.0161","url":null,"abstract":"The inquiry process of traditional medical equipment maintenance management is complicated, which seriously affects the efficiency and accuracy of medical equipment maintenance management, and causes a lot of waste of manpower and materials. In order to accurately predict the failure of medical equipment, an accurate prediction system for failure life cycle of medical equipment was designed. The system is divided into four modules: the whole life cycle management module constructs the life cycle data set of medical devices from the three parts of the management in the early stage, the middle and the later stage; the status detection module monitors the main operation data of the medical device components through the normal value of the relevant sensitive data in the whole life cycle management module; the main function of the fault diagnosis module is based on the medical equipment whole life cycle management module. The operation data of equipment is diagnosed by inference machine; the fault prediction module builds a fine prediction system based on least square support vector machine algorithm, and uses AFS ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters, and the optimal model is used to predict the medical equipment failure. In order to verify the effectiveness of the design system, comparative experiments are designed to verify. The results show that the designed system can accurately predict the failure of electrocardiogram diagnostic instrument and incubator, and has high support and reliability. Compared with the comparison system, the prediction error of the design system is the smallest and the program running time is the shortest. Therefore, the design system can accurately predict the different failure types and causes of medical devices. \u0000 ","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46987122","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":"CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis","authors":"Jiaji Wang","doi":"10.37965/jait.2023.0166","DOIUrl":"https://doi.org/10.37965/jait.2023.0166","url":null,"abstract":"(Background) To solve the cluster analysis better, we propose a new method based on the chaotic particle swarm optimization (CPSO) algorithm. \u0000(Methods) In order to enhance the performance in clustering, we propose a novel method based on CPSO. We first evaluate the clustering performance of this model using the Variance Ratio Criterion (VRC) as the evaluation metric. The effectiveness of the CPSO algorithm is compared with that of the traditional Particle Swarm Optimization (PSO) algorithm. The CPSO aims to improve the VRC value while avoiding local optimal solutions. The simulated dataset is set at three levels of overlapping: non-overlapping, partial overlapping, and severe overlapping. Finally, we compare CPSO with two other methods. \u0000(Results) By observing the comparative results, our proposed CPSO method performs outstandingly. In the conditions of non-overlapping, partial overlapping, and severe overlapping, our method has the best variance ratio criterion values of 1683.2, 620.5, and 275.6, respectively. The mean VRC values in these three cases are 1683.2, 617.8, and 222.6. \u0000(Conclusion) The CPSO performed better than other SOTA methods for cluster analysis problems. CPSO is effective for cluster analysis.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44536536","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":"ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses","authors":"M. AlAfnan, Samira Dishari, Marina Jovic, Koba Lomidze","doi":"10.37965/jait.2023.0184","DOIUrl":"https://doi.org/10.37965/jait.2023.0184","url":null,"abstract":"This empirical study examines ChatGPT as an educational and learning tool. It investigates the opportunities and chal-lenges that ChatGPT provides to the students and instruc-tors of communication, business writing, and composition courses. It also strives to provide recommendations. After conducting 30 theory-based and application-based ChatGPT tests, it is found that ChatGPT has the potential of replacing search engines as it provides accurate and relia-ble input to students. For opportunities, the study found that ChatGPT provides a platform for students to seek an-swers to theory-based questions and generate ideas for ap-plication-based questions. It also provides a platform for in-structors to integrate technology in classrooms and conduct workshops to discuss and evaluate generated responses. For challenges, the study found that ChatGPT, if unethically used by students, may lead to human unintelligence and un-learning. This may also present a challenge to instructors as the use of ChatGPT negatively affects their ability to dif-ferentiate between meticulous and automaton-dependent students, on the one hand, and measure the achievement of learning outcomes, on the other hand. Based on the out-come of the analysis, this study recommends communica-tion, business writing, and composition instructors to (1) re-frain from making theory-based questions as take-home as-sessments, (2) provide communication and business writing students with detailed case-based and scenario-based as-sessment tasks that call for personalized answers utilizing critical, creative, and imaginative thinking incorporating lec-tures and textbook material, (3) enforce submitting all take-home assessments on plagiarism detection software, espe-cially for composition courses, and (4) integrate ChatGPT generated responses in classes as examples to be discussed in workshops. Remarkably, this study found that ChatGPT skillfully paraphrases regenerated responses in a way that is not detected by similarity detection software. To maintain their effectiveness, similarity detection software providers need to upgrade their software to avoid such incidents from slipping unnoticed. \u0000 ","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41585856","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}