Journal of Artificial Intelligence & Cloud Computing最新文献

{"title":"Big Data and Deep Learning Analytics","authors":"Nipun Tyagi","doi":"10.47363/jaicc/2023(2)120","DOIUrl":"https://doi.org/10.47363/jaicc/2023(2)120","url":null,"abstract":"There has been an enormous growth of the Internet, mobile phone, medical facilities, and many more in the 21st century, which can also be known as the beginning of the knowledge era. Knowledge is defined not for what it is, but for what it can do. In this fast-moving technological era, as a result, a huge amount of data is generated in different regions of the world and it is growing day by day, this growing data is known as “Big Data”. To extract useful information (analyze) from large unstructured data (like Web, sales, customer contact center, social media, mobile data, and so on) is a complex task, as data being generated is a combination of structured, semi-structured and unstructured data. Traditional systems are not capable to handle semi-structured or unstructured data generated whose volume could range in petabytes or exabytes, as the major challenges are limited memory usage, computational hurdles and slower response time, data redundancy, etc. This problem can be overcome with big data analytics having technologies like Apache Hadoop, Apache Spark, Hive, Pig, etc. which can extract useful information from these large data. Authors are going to explore more on them in these chapters. Alongside authors will explore “Deep Learning” also known as “Deep Neural Learning” or “Deep Neural Network”, which is a class of Machine Learning that progressively extract higher-level features from raw data automatically. It performs 'end-to- end learning' and uses layers of algorithms to process data, understand human speech, and visually recognize objects, which is an important part of it. Feature extraction, self-driving cars, fraud detection, healthcare, neural language processing, etc. are some of the areas where it is applied in daily life. Algorithms like RNN, CNN, FNN, Backpropagation, etc, are some of the algorithms used in deep learning. The authors will explore how Machine learning is different from deep learning. Deep learning (DL) is also associated with data science in many ways as the DL algorithms work better than older learning algorithms for prediction or feature extraction etc. Which has brought it, more closer towards one of its main objectives i.e., artificial intelligence (AI)? Hence it is immensely advantageous to the data scientists who aim for making predictions and draw useful information to analyze and interpret it for helping the organization in its growth. The processing of Big Data and the evolution of Artificial Intelligence are both dependent on Deep Learning. Deep learning technology came up along with big data analytics. The concept of deep learning is supportive in the big data analytics due to its efficient use for processing huge and enormous data. This chapter explains about deep learning and big data analytics use in healthcare and alongside authors will study about algorithms used in deep learning and technologies used in big data analytics with its architecture. After reading this chapter, authors must be able to ","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence & Cloud Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114739480","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":"Strategically Implementing A Series of Critical Refinements to Organization External-Facing Career Portal, How Can These Changes Effectively Entice and Engage Top-Tier, High-Caliber Talent as Part of the Organization Recruitment Initiatives","authors":"Sai Raj Kondogi Shiridi","doi":"10.47363/jaicc/2023(2)126","DOIUrl":"https://doi.org/10.47363/jaicc/2023(2)126","url":null,"abstract":"This paper presents a comprehensive proposal aimed at the enhancement of the external-facing career site, with a primary objective of elevating the user experience and strategically attracting high-caliber candidates, with minimal system changes","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence & Cloud Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139367564","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":"Artificial Intelligence (AI): What are the Impacts for Medicine?","authors":"Doepp Manfred","doi":"10.47363/jaicc/2023(2)117","DOIUrl":"https://doi.org/10.47363/jaicc/2023(2)117","url":null,"abstract":"AI is our destiny, no one can prevent it anymore, just as it was with the Industrial Revolution of about 200 years ago or the introduction of computers a few decades ago. Nevertheless, there are many warning voices not to allow its eventual domination over humanity and its abuse. This issue also arises in medicine, where AI is in many ways superior to physicians. This concerns their extreme memory for information, their speed, their flexibility, and their ability to combine information. What can medicine do? Medical personnel should extend the qualities that are unique to humans: caring, empathy, intuition, inspiration, and above all, love. Those who have a narrowed consciousness and can be manipulated will be replaced by AI. Those who have a wide and open consciousness can become partners of the AI. This, by the way, concerns the whole humanity, whose division into these two groups is going on at present. The establishment of the AI is thus an immense but necessary challenge for all of us.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence & Cloud Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133577937","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":"Achieving Workplace Equality With Cloud HR Software: Best Practices For Implementing A Successful Diversity and Inclusion Programs","authors":"Ramesh Nyathani","doi":"10.47363/jaicc/2023(2)127","DOIUrl":"https://doi.org/10.47363/jaicc/2023(2)127","url":null,"abstract":"The pursuit of Diversity and Inclusion (D&I) in organizational contexts has burgeoned into a pivotal business strategy, albeit with varying degrees of success. A myriad of D&I initiatives across industries has been met with challenges, manifesting in suboptimal outcomes and, in certain instances, outright failure. This paper seeks to explore the potential factors contributing to the lackluster performance of numerous D&I programs and endeavors to identify prospective avenues to circumvent these issues. Addressing workplace equality stands paramount in the evolution of a thriving, inclusive, and innovative organizational culture. The role of Human Resources (HR) is pivotal in orchestrating an environment that not only embraces diversity but also fosters an ethos of inclusivity and equality. Amidst the modern technological landscape, Cloud HR Software surfaces as a groundbreaking tool that amalgamates the virtues of cloud computing with HR functionalities, offering scalable, accessible, and robust solutions to mitigate disparities and anchor equal opportunities across all tiers of an organization. This paper elucidates the imperative of achieving workplace equality and how leveraging Cloud HR Software can potentially streamline this pursuit through comprehensive data analytics, real-time feedback, and developing equitable talent management strategies. I delve deep into various use-cases and empirically backed scenarios wherein organizations have successfully harnessed Cloud HR solutions to not only comprehend but also rectify structural and cultural inequalities, thereby propelling themselves toward a more egalitarian working environment. The exploration aims to shed light on practical strategies, inherent challenges, and ethical aspects interwoven with the deployment of Cloud HR Software while maintaining a keen eye on ensuring that technological advancements and organizational equality progress hand in hand. This white paper delves into the multifaceted challenges that permeate the implementation of D&I programs and elucidates pathways towards fruitful execution. Central to these challenges are superficial commitment, misaligned strategies, resistance to change, and the failure to intertwine inclusivity within organizational fabric, often culminating in suboptimal outcomes and disintegrated team dynamics. Through an exploratory lens, this paper concurrently sheds light on the critical success factors that underpin triumphant D&I initiatives.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence & Cloud Computing","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139367178","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":"Logic of Nature Seen in Particle Properties, in the Rise of the Universe and Consequences for the Structure of Complex Systems","authors":"H. Morsch","doi":"10.47363/jaicc/2023(2)113","DOIUrl":"https://doi.org/10.47363/jaicc/2023(2)113","url":null,"abstract":"To push realistic learning processes and simulations to extremes far beyond present artificial intelligence, they must be conform with nature and have to follow mathematical logic. To see in full strength the logic structure of nature, the study of basic systems - as hadrons and leptons - is best suited, because ambiguities due a large complexity are reduced to a minimum. To describe such systems, a new quantum theory has been derived recently, in which in addition to the usual treatment of fermions bosons are considered also to fulfill energy and momentum conservation explicitly. With negative binding energies of fermions and positive ones of bosons, the total energy is zero, giving rise to a quantitative description of hadron and lepton properties with uncertainties < 1/1000. In addition, this formalism provides a mechanism, by which particles could be created out of the vacuum, indicating that the universe emerged out of nothing (the vacuum). This leads finally to a realistic view of the cosmic evolution. Further, the present theory can be taken as basis for the description of complex systems in gravity, heavy atoms, molecules, materials and even biological structures.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence &amp; Cloud Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114778665","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":"Re-imagining Debt Recovery Process with Conversational AI","authors":"Saibal Samaddar, Tanushree Halder","doi":"10.47363/jaicc/2023(2)111","DOIUrl":"https://doi.org/10.47363/jaicc/2023(2)111","url":null,"abstract":"","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence &amp; Cloud Computing","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124067444","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":"Reduction between Aristotelian Modal Syllogisms Based on the Syllogism ◊I�A◊I-3","authors":"Cheng Zhang, Xiaojun Zhang","doi":"10.47363/jaicc/2023(2)112","DOIUrl":"https://doi.org/10.47363/jaicc/2023(2)112","url":null,"abstract":"In order to provide a consistent explanation for Aristotelian modal syllogistic, this paper reveals the reductions between the Aristotelian modal syllogism ◊I�A◊I-3 and the other valid modal syllogisms. Specifically, on the basis of formalizing Aristotelian modal syllogisms, this paper proves the validity of ◊I�A◊I-3 by means of the truth value definition of (modal) categorical propositions. Then in line with the symmetry of Aristotelian quantifiers some and no, the definition of inner and outer negations of Aristotelian quantifiers, and some rules in classical propositional logic, this paper deduces the other 47 valid Aristotelian modal syllogisms from the modal syllogism ◊I�A◊I-3. The reason why these syllogisms are reducible is that: (1) any of Aristotelian quantifier can be defined by the other three Aristotelian quantifiers; (2) the Aristotelian quantifiers some and no have symmetry; (3) the possible modality ◊ and necessary modality £ can be mutually defined. This formal study of Aristotelian modal syllogistic not only conforms to the needs of formalization transformation of various information in the era of artificial intelligence, but also provides a unified mathematical research paradigm for other kinds of syllogistic.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence &amp; Cloud Computing","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740640","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":"Prediction of Heart Disease Using Voted Perceptron","authors":"Safia Naveed S","doi":"10.47363/jaicc/2022(1)110","DOIUrl":"https://doi.org/10.47363/jaicc/2022(1)110","url":null,"abstract":"Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence &amp; Cloud Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122782562","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":"Rules-based Expert System to Assist Physicians in Pre-laboratory Screening for the Diagnosis of Pulmonary Tuberculosis Disease in Rural Areas","authors":"Humberto Cuteso Matumueni","doi":"10.47363/jaicc/2022(1)108","DOIUrl":"https://doi.org/10.47363/jaicc/2022(1)108","url":null,"abstract":"Worldwide more than 1.5 to 2 million deaths from tuberculosis occur each year. Healthcare professionals face many challenges in delivering good healthcare with unattended automation in hospitals where multiple patients require physician attention. The expert system we have built is designed to help medical experts in the process of pre-laboratory screening for pulmonary tuberculosis. The architecture consists of a rule base, a patient knowledge base. These units interact with the inference engine, which receives patient data through a user interface. The result of the usability rating reveals that the system has a usability rating of 5.6 on a scale of 1-7. This is an indication of above average system performance. Our Tuberculosis Diagnosis Expert System is an effective solution for implementing a rule-based expert system designed with Exsys Corvid.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence &amp; Cloud Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129713561","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":"Toward an Appropriate Approach for Intelligent Intrusion and Detection Systems","authors":"C. Leghris, Ouafae Elaeraj","doi":"10.47363/jaicc/2022(1)109","DOIUrl":"https://doi.org/10.47363/jaicc/2022(1)109","url":null,"abstract":"Now a days, the company’s information security become among a main priority. Indeed, the more the attack force on the network develops, the more it is necessary to develop the security and the network surveillance. The data is to be exchanged between the internal company network and the outside one such as Internet. It is therefore necessary to be protected against malicious intrusions into the company's network, but also to monitor the traffic inside the network in order to prevent possible internal attacks. Currently, security and reliability have become the major concerns of an individual or organization. A rule-based intrusion detection system (IDS) called Snort is an open-source software used as a network protection tool that can only detect recognized attacks. In order to detect advanced network attacks and detect fraudulent network traffic, this research paper proposes an advanced and more intelligent approach by applying machine learning. To find the best algorithm to use with Snort to improve its detection, the support vector machine (SVM) was chosen based on its accuracy. The proposed system has produced efficient detection rates versus other proposed approaches in the security intrusions detection field.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence &amp; Cloud Computing","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129595897","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}