Journal of Autonomous Intelligence最新文献

{"title":"Concurrency versus consistency in NoSQL databases","authors":"Sonal Kanungo, Rustom D. Morena","doi":"10.32629/jai.v7i3.936","DOIUrl":"https://doi.org/10.32629/jai.v7i3.936","url":null,"abstract":"With the advent of cloud services, the proliferation of data has reached unprecedented levels. The load distribution across multiple servers, driven by web and mobile applications, has become a defining characteristic of contemporary data management. In contrast to this surge in data complexity, traditional relational databases have proven inadequate in handling vast amounts of unstructured data due to their inherent focus on structured data models. Additionally, the concept of clustering, vital for efficient unstructured data management, eluded relational databases, rendering them ill-equipped for customized clustering techniques and the optimal execution of queries. SQL (Structured Query Language) databases earlier emerged as a groundbreaking solution, introducing the relational database model that organized data into structured tables. They employed ACID (atomicity, consistency, isolation, durability) properties to maintain data integrity and enabled intricate querying through SQL. However, as applications grew in complexity, SQL databases encountered hurdles in handling various data types, rapid data expansion, and concurrent workloads. The limitations of SQL databases propelled the rise of NoSQL (Not Only Structured Query Language) databases, which prioritized adaptability, scalability, and performance. NoSQL databases embraced diverse data models such as documents, key-values, column families, and graphs, enabling effective management of structured, semi-structured, and unstructured data. The transition to NoSQL databases was justified by several factors; horizontally scaled across nodes, handling extensive read-write operations effectively, Agile development of accommodating changing data structures without schema constraints, optimization for specific tasks, providing low-latency access and high throughput, dynamic schemas aligned with modern iterative development, promoting adaptability, and adeptly managed diverse data types, spanning text, geospatial, time-series, and multimedia data. These databases are purposefully designed to accommodate the escalating demands of data storage. Notably, this data emanates from diverse nodes and is susceptible to concurrent access by numerous users. However, a critical challenge surfaces as the data present on one node may diverge from its counterpart on another node replica. In this context, the simultaneous execution of database operations, while preserving the integrity of the data, emerges as a pivotal concern. Maintaining data consistency amid concurrent access hinges upon the synchronization of operations across all replica nodes. Achieving this synchronization necessitates the adoption of a robust concurrency control technique. Concurrency control acts as the linchpin for upholding accuracy and reliability within a system where operations unfold concurrently. Hence, the focal point of this investigation lies in examining the assorted concurrency control methodologies employed by NoSQL systems. ","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"53 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139150960","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":"Automatic detection of fake news using recurrent neural network—Long short-term memory","authors":"Anil Kumari Shalini, Sameer Saxena, B. Suresh Kumar","doi":"10.32629/jai.v7i3.798","DOIUrl":"https://doi.org/10.32629/jai.v7i3.798","url":null,"abstract":"The propagation of deliberate misinformation is gaining significant momentum, especially across different social media platforms. Despite the fact that there are several fact-checking blogs and websites that distinguish between news that is genuine or fake, and regardless of the reality that there is ongoing research being conducted to restrict the propagation of fake news, the issue is still one that needs to be addressed. The most major barrier is a failure to spot monitors and to disclose false news in a reasonable timeframe, both of which are critical components. In this research, a system is proposed that utilizes deep learning model of Recurrent Neural Network—Long Short-Term Memory (RNN-LSTM) in order to put an end to the circulation of misleading information and put a stop to, and expose instances of fake news that are spread through reliable channels. The process of extracting hybrid features from text data, such as Lemmas, Bi-Gram, Tri-Gram, N-gram, Term Frequency Inverse Document Frequency (TF-IDF), part-of-speech, and dependency-based natural language processing features, is developed as a strategy. When compared to other traditional approaches to machine learning classifier, the RNN-LSTM method that was proposed obtains a greater level of accuracy than those other approaches. It achieves an accuracy rate of 99.10% both during training and testing, which is better to the accuracy achieved by common machine learning approaches such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT). In the proposed approach for detecting false news using RNN-LSTM, the three experiments are conducted to acquire, accuracy, precision, recall, and F-score with varying forms of cross validation (5-Fold, 10-Fold and 15-Fold). Based on the findings of the empirical research, the conclusion can be drawn that the RNN-LSTM with ReLu function provides more accurate detection than both the RNN-LSTM (Tan h) function and the RNN-LSTM (sigmoid) function with 15 fold cross validation.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"39 154","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139154532","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":"Digital assets for digital natives: Exploring familiarity and preference for cryptocurrency among millennials and Gen Z","authors":"R. S. Lekshmi, K. Jawaharrani, S. Vijayakanthan, G. Nirmala, K. Dheenadayalan, S. Vasantha","doi":"10.32629/jai.v7i3.1054","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1054","url":null,"abstract":"Cryptocurrency has gained significant attention and popularity in recent years, leading to increased awareness and preference among individuals worldwide. This study explores the intertwined concepts of awareness and preference and reasons for buying cryptocurrencies among Chennai city’s Millennials and Gen Z. This study did two things: a comprehensive but detailed systematic literature review on cryptocurrency and then conducted a survey, the study got 252 valid responses. After analysis, it has been observed that majority of the respondents are aware of cryptocurrencies and only a less percentage respondents own cryptocurrency. As individuals learnt about cryptocurrencies, their preference for these digital assets begins to take shape. Decentralized & transparent transactional system and financial inclusion and security & privacy emerge as significant factors driving preference, as individuals seek to escape the constraints of traditional financial systems and exercise direct control over their assets. Additionally, the potential for high returns attracts investors, drawing attention to the volatile but potentially rewarding nature of the cryptocurrency market. Global accessibility & speed and media & publicity are vital in promoting a sustainable and well-informed approach to the adoption and affinity for cryptocurrencies. This study holds the promise of exerting a positive influence on society by enabling better-informed decision-making, fostering greater financial and technological literacy, mitigating risks, and actively contributing to the responsible development of the digital asset industry.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"27 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139156018","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":"Explorative study on potential of machine learning and artificial intelligence for improved healthcare diagnosis and treatment","authors":"Prakash Date, Varsha Pimprale, S. Mandke","doi":"10.32629/jai.v7i3.1084","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1084","url":null,"abstract":"Machine learning (ML) and Artificial intelligence (AI) have demonstrated substantial promise for enhancing healthcare diagnostics and therapy. This study compares the benefits, drawbacks, and uses of these tools to examine their potential in healthcare. ML systems can find trends, increase diagnosis precision, and support professional judgment. Their efficacy may be constrained, though, by bad data quality, a lack of interpretability, and execution issues. On the other hand, AI can support clinical judgment, enhance patient results, and boost healthcare productivity. However, difficulties in implementing them can arise due to restricted generalizability, data protection issues, and legal conformance. To ensure the effective application and acceptance of these technologies in healthcare, it is essential to understand these benefits and constraints. Healthcare providers of the future will be able to make wiser choices regarding patient assessment and therapy options using AL and ML, resulting in an overall enhancement of healthcare services.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"4 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139156818","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":"Efficient machine learning model to detect early stage Parkinson’s disease","authors":"Raziya Begum, T. P. Kumar","doi":"10.32629/jai.v7i3.1093","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1093","url":null,"abstract":"Parkinson’s disease (PD) often manifests itself in memory loss and cognitive decline. The decline is inexorable, and damage to the brain’s cortex has already occurred. Numerous studies have shown that by detecting dementia early and beginning treatment, the disease’s course can be slowed, and any further atrophy can be prevented. Brain imaging data, such as from an MRI, is frequently used in the diagnosis of Parkinson’s disease (PD). In recent years, utilizing deep convolutional neural networks has greatly improved Parkinson’s disease diagnosis. However, getting to the level of quality needed for clinical use is still challenging. In this study, we introduce a machine learning-based approach for more accurately diagnosing Parkinson’s disease. This research makes use of information gleaned from single-photon emission computerized tomography (SPECT) scan and positron emission tomography (PET) scans performed on patients with Parkinson’s disease (PD) and healthy controls. The most crucial characteristics of these datasets are isolated with the aid of the Fisher discriminate ratio (FDR) and non-negative matrix factorization (NMF). The K-nearest neighbor, Decision Tree, Support vector machine (SVM), and Deep Convolution neural network (DCCN) classifiers with confidence bounds classify the NMF-transformed data sets with a decreased number of features. The proposed DCCN technique has a classification accuracy of up to 93.7 percent when compared to decision trees, K-Nearest Neighbor (KNN)s, and SVMs. The DCCN is now a reliable approach for classifying SPECT and PET, PD images.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"21 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139155294","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":"A novel human-to-robot interaction model based on transfer expert reinforcement learning with recurrent neural network","authors":"M. B. Gawali, Swapnali Sunil Gawali, Megharani Patil, Anand Khandare","doi":"10.32629/jai.v7i2.1011","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1011","url":null,"abstract":"The control tasks related to interaction tracking are mainly limited in robot manipulators-based traditional applications. In this, the desired motivations are specified based on the trajectories and the desired positions. The robots are programmed by using the teach-and-playback method in such applications that are assumed to be more convenient. Moreover, the advancements in sensing and robotic methodologies fulfill the satisfactory requirements of more demanding tasks. Several instructions are provided for interacting robots with humans in order to perform a sequence of more difficult tasks. It does not require learning the motions, but it only requires learning the positions of the motions in such applications, and this position is learned by using the robot controller. The major aim of this research work is to develop a new Transfer Expert Reinforcement Learning (TERL) method to offer efficient interaction between humans and computers. In this developed model, Reinforcement Learning (RL) is utilized to observe the movement of the robotic arm. Then, robot movement is considered with the help of a deep learning approach named Recurrent Neural Network (RNN) along with inputs of kinematic movement. Finally, the proposed model achieves a superior rate than conventional approaches in human to human-to-robot interaction model.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"11 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139157757","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":"Smart museum and visitor satisfaction","authors":"Siyang Liu, Jian Guo","doi":"10.32629/jai.v7i3.1242","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1242","url":null,"abstract":"The digitization of museums has not only changed the way people view exhibitions but also transferred some rights to the hands of visitors to meet their needs for personalized services. Through a review of literature, we found that research related to smart museums presents an increasing trend in the recent 15 years. Progress has been made in the definition of smart museums, intelligent system construction, and intelligent narrative and service. However, there are few studies on systems of assessment criteria for smart museums, let alone on the relationship between how smart a museum is and a visitor’s satisfaction with the experience offered at the museum. Our purpose in this study was to establish assessment criteria for smart museums, and then to use the assessment criteria to explore the relationship between degree of museum intelligence and visitor satisfaction. We collected survey data from 602 visitors at Beijing’s Palace Museum and ran an exploratory factor analysis on the data. The results showed that six factors of museum intelligence, taken as assessment criteria, were positively correlated with visitor satisfaction. The technology integration factor had the greatest correlation, while module performance had the greatest impact on visitor satisfaction.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159758","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":"Evaluating numerous techniques for the effects of electromagnetic waves on the Electro Cardio Gram (ECG)","authors":"Tukaram Shep, A. Sayyad","doi":"10.32629/jai.v7i2.959","DOIUrl":"https://doi.org/10.32629/jai.v7i2.959","url":null,"abstract":"Nowadays, due to the widespread use of mobile phones and the proliferation of mobile towers the human body parts especially the heart are getting affected. Furthermore, these waves are ubiquitous in our modern society, with various sources emitting these waves in our environment. As medical devices become more prevalent and wireless technologies continue to advance, concerns have been raised regarding the potential impact of electromagnetic waves on human health. It is important to monitor the condition of your heart. With the increase in the number of mobile phones, there is also an increase in electromagnetic radiation, which can affect the human heart. The heart is an important component of the human body and an electrocardiogram (ECG) can provide valuable information about its condition. ECG parameters can show how well the heart is working. In this paper, the author proposes how ECG parameters change under the influence of mobile phones in three different situations. A comprehensive experimental setup was devised. A group of healthy human subjects volunteered to participate in the study, with each subject undergoing ECG recording under controlled conditions. The subjects were exposed to varying intensities and frequencies of electromagnetic waves generated by a standardized source. Statistical analysis was performed to compare the ECG measurements obtained during exposure to electromagnetic waves with those obtained in a controlled environment without electromagnetic wave exposure. This study contributes to the growing body of research on the potential health effects of electromagnetic waves. By specifically focusing on the ECG signal, which is vital for cardiovascular diagnostics, this research provides valuable insights into the safety and reliability of using ECG in environments with electromagnetic wave exposure. The findings will help inform healthcare professionals, engineers, and policymakers to establish appropriate guidelines and safety measures concerning the use of medical devices and wireless technologies in proximity to patients or individuals with cardiac conditions.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139158053","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":"Efficient sensor anomaly detection using Markov-LSTM architecture for methane sensing","authors":"S. V. Kumar, G. Aloy, Anuja Mary, J. Chohan, Kanak Kalita","doi":"10.32629/jai.v7i3.1285","DOIUrl":"https://doi.org/10.32629/jai.v7i3.1285","url":null,"abstract":"The integration of the Internet of Things (IoT) into industrial activities has unlocked myriad possibilities, particularly in applications like environmental monitoring, which facilitates effective landfill management. Nevertheless, IoT environments present challenges, including resource constraints, heterogeneity and potential hardware/software failures. These issues often lead to sensor anomalies, triggering false alarms and stalling data-driven systems. Existing models for edge devices frequently overlook the sensor life cycle, leading to extensive training times and significant computational demands. In this paper, a collaborative approach is proposed wherein a Markovian architecture gauges the operational state of a sensor, assisting the Long Short-Term Memory (LSTM) model in outlier detection within real-world data. Commercially available MQ-4 sensor alongside a microwave RADAR-based Methane (CH4) sensor in a tandem setup is employed to evaluate our methodology. The Bathtub curve and the Pearson Correlation Coefficient (PCC) function as the switching mechanisms for the Markov chain. Real-time data validation yielded an impressive 92.57% accuracy and 94.86% efficiency in anomaly detection. When benchmarked against the Autoregressive Integrated Moving Average (ARIMA) and the Prophet algorithm, our method demonstrated superior anomaly rejection rates of 9.63% and 3.01%, respectively. Implementing the Markov-LSTM model in methane sensing significantly enhances the accuracy of recorded sensor values compared to standard methane sensors.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159384","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":"Hybrid machine learning method for classification and recommendation of vector-borne disease","authors":"Salim G. Shaikh, B. Suresh Kumar, Geetika Narang, N. N. Pachpor","doi":"10.32629/jai.v7i2.797","DOIUrl":"https://doi.org/10.32629/jai.v7i2.797","url":null,"abstract":"Vector-borne diseases (VBD) are a class of infectious illnesses that are transmitted to humans and animals through the bites of arthropod vectors, such as mosquitoes, ticks, and fleas. These diseases are caused by a variety of pathogens, including bacteria, viruses, and parasites, and are a significant global public health concern. Vector-borne diseases are prevalent in many parts of the world, particularly in tropical and subtropical regions, where the vectors thrive. This research has contributed by constructing a hybrid machine learning based prediction model, which helps to discover patients who are infected by vector-borne disease at an earlier stage and also helps with the categorization and diagnosis of severe vector-borne disease. The model that has been proposed is made up of units: data conversion, data preprocessing, normalization, extraction of feature, splitting of dataset, and classification and prediction unit. The fact that the suggested prediction model is capable of identifying vector-borne disease in its early phases as well as categorizing the kind of disease using the medical report of a sufferer is one of the innovative aspects of the model. The 7 distinct conventional machine learning and single hybrid machine learning (HML) are applied for classification and Recurrent Neural Network (RNN) based reinforcement learning are utilized for recommendation. In order to evaluate the effectiveness of the system that’s been proposed, a number of tests were carried out. A dataset consisting of 1539 different cases of a disease transmitted by vectors has been collected. The 11 common vector-borne diseases namely malaria, dengue, Japanese encephalitis, kala-azar and chikungunya were taken for experimental evaluation. The performance accuracy of the proposed prediction model has been measured at 98.76%, which assists the healthcare team in making decisions on a timely basis and ultimately helps to save the patient’s lives. The final phase system provides the recommendation for those classifiers resulting in four different classes such as normal, mild, moderate and severe respectively. The recommendation is also demonstrating future direction for cure of vector borne disease.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"4 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139158758","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}