International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE)
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IoT and AI-based Plant Monitoring System
No full textPlants plays a vital role in the environment because it provides the health support through absorbing the carbon dioxide and releasing the oxygen to the atmosphere. Although, it required to maintain the proper plant growth and health as well as provide the appropriate monitoring. To overcome these concerns, an Artificial Intelligence (AI) and Internet of Things (IoT) based solution is proposed to monitor the plant’s growth and health. This study demonstrates the real-time monitoring of the plants via environmental sensors such as DHT 11 and soil moisture sensors. Real-time values stored in the cloud server and applied the machine learning models to predict the plant’s growth. The Statistical parameters such as RMSE, MAE are used to analyze the resulting outcome from the system. 
The Analysis of Ripening of Pineapple Fruits Using Machine Learning Technique
No full textDuring the Fourth Industrial Revolution, artificial intelligence is being widely applied in a variety of fields. However, in the current agricultural model, humans are still used as the primary labor force, which is costly in terms of both finance and human resources. Furthermore, each region\u27s typical fruits, particularly pineapple, have a rather complicated ripening period. It is difficult to control and manage hundreds of hectares of land. As a result, in this paper, we propose using deep learning models to aid in the identification and detection of ripe pineapple growth stages in order to ensure that care and harvesting are completed on time
Enhancing the Accuracy of Indoor Positioning Using System Delay Time Compensation
No full textIndoor positioning based on the Hidden Markov Model (HMM), which utilizes a combination of Received Signal Strength Indicator (RSSI) from Access Points (APs) and inertial sensors, has been exploited broadly due to its superiority compared to other approaches. Some previous studies, which have utilized a combination of two methods, have often assumed the users do not move in the system estimated time and normally this time has been neglected. However, when the number of reference points is huge, and the user moves a considerable distance, the computational time of the system increases considerably. In this case, the system computational time can not be canceled. This paper presents an approach to improving the accuracy of the positioning system. By considering the processing time of the system when it estimated the position of the user, and then cooperating the measured information from the inertial sensor, the localization of the user is estimated more accurately. The simulation results show that the proposed approach achieves a remarkable effect compared to previous studies with the same scenario even if the user moves or does not move in a large area
E-Recruitment In HR Consultants via E-Technology: E-Recruitment In HR Consultants
No full textHuman Resources consulting is that branch of management which concentrates on the process of efficiently utilizing employees to attain the objectives of the organization. A proficient and effective HR consultant can help the business to become productive by guiding company in a varied array of matters. This study was oriented to go through the usefulness of E-recruitment or online recruitment. The study specially aimed to govern the recruitment via electronic medium. It also aims to understand how major job portals perform their operations and provide services. It is found that majority of the respondents agreed that number of successful candidates, cost per hire, time taken to close position and candidate and employer satisfaction impact the e-recruitment
Novel Approach for Analysis of Face Recognition using Stereo Matching Algorithm
No full textThis paper depicts a face acknowledgment structure that is equipped for preparing pictures across posture and enlightenment. The primary goal of this paper is to manufacture programmed face acknowledgment frameworks. This paper comprises of three primary segments of face acknowledgment structure. The principal segment is to construct the exhibition pictures of appearances alongside three milestone focuses. The subsequent segment bargains the enlightenment variety. The last segment handles the posture variety. The coordinating strategy of sound system handles the posture and articulation variety issues
Designing Hand-Held Vibration Measuring Device for Industrial Machines
No full textEvaluating the quality of industrial machines, the vibration meter is used to measure the machine’s actual vibration. The two most important parameters describing machine vibration, amplitude, and frequency, are the key factors for determining the cause of vibrations. Spectral analysis of the vibration signal will give information about the vibration level and choose which part of the machine leads to the observed vibration signal. This paper presents a self-made machine vibration measuring device with a simple structure, compact size, high accuracy at a reasonable price. The device can display the measured vibration signals’ real-time value and analyze the computer’s spectral to produce the required parameters
IJMLNCE Editorial Note Volume No 03, Issue No 04: Special Issue Volume No 03, Issue No 04
No full textThe International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) with ISSN: 2581-3242 is now indexed in popular databases such as BASE (Bielefeld Academic Search Engine), CNKI Scholar, CrossRef, CiteFactor, Dimensions, DRJI, Google Scholar, Index Copernicus, JournalTOCs, J-Gate, Microsoft Academic, PKP-Index, Portico, ROAD, Scilit, Semantic Scholar, Socolar or WorldCat-OCLC. We are now proud to present the ninth volume of the Journal, Volume No-03 Issue No-04, with five high-quality papers written by international authors and covering different aspects related to machine learning and collaborative engineering.
The First research article authored by Priyanka and Manju Khari has written their article on the title "A Survey of Cloud Computing Security Issues." In the world of computer networking, cloud computing makes a technical shift in computing services being provided locally to being provided remotely by third-party service providers. The data which was previously retained by the control of users now under the control of service providers. Cloud computing conveys numerous economic and practical assistance, along with severe security alarms that might impend commercial endurance and business status. The cloud computing definition is still not clear in a huge portion, as of the extent of security threats and the broad expanse of virtual information being distributed over the unsecured area. This manuscript aims to assess in what way security risk issues are affecting the surviving and eventual cloud platform. This survey examines the published resources and studies, examines available concerns laterally with existing countermeasures to assess the complete assertion level of security of the cloud. The primary goal of the survey is to analyse the security risks and the existing security algorithm\u27s performance in terms of different security parameters. This study includes the basics of cloud computing by adding its characteristics, models, and their categories. The analysis also embraced the existing security concerns faced by researchers and their imposed methodologies.
The Second research article suggested "The mechanism for Predictive Load Control in the Implementation Framework through Genetic Intelligence" authored by T.Pushpalatha , S.Nagaprasad. Cloud Storage is a pay-per-use range of resources. The consumer wants to ensure that all requirements met in a limited time for optimal performance in cloud applications that are every day. Load balancing is also crucial, and one of the essential cloud computing issues. It is also called the NP-full load balancing problem since load balancing is harder with increasing demand. This paper provides a genetic algorithm (GA) framework for cloud load. Depending on population initialization duration, the urgent need for the proposal considered. The idea behind the emphasis is to think about the present world. Real-World Scenario structures have other targets that our algorithms can combine. Cloud Analyst models the suggested method. A load-balancing algorithm based on the forecasts of the end -to - end Cicada method given in this paper. The result indicates the possibility of offering a quantitative workload balancing approach that can help manage workloads through the usage of computer resources. The next generation of cloud computing would make the network scalable and use available resources effectively. This article introduces a new approach to genetic algorithm (GA) power loads. When trying to reduce the complexity of a particular task, the algorithm handles the cloud computing fee. A software analyst model evaluated the proposed method of load balancing. Results from simulations for a standard sample program show that the suggested algorithms outperform current methods like FCFS, Round Robbing (RR), and local search algorithms Stochastic Hill Climbing (SHC).
The Third research article entitled "Exploring the adoption of the Artificial Intelligence in the Public Sector" written by Vasileios Yfantis, Klimis Ntalianis. In this paper, the evolution of artificial intelligence boosts its usage in the private sector was discussed. However, the public sector seems to lag behind. There are specific reasons which prevent the civil servants and the citizens from using this innovative technology. This paper first identifies the advantages and potential challenges for the implementation of artificial intelligence in the public sector to prove its benefits. Afterward, a gamification framework called Octalysis is suggested as a technique to affect the intent of the stakeholders to use the artificial technology. Octalysis consists of 8 core drives that describe the types of motivations and the game elements that the ideal gamified system should have. Finally, the Octalysis model is applied to an existing chatbot of the public sector which is used to offer information about the public administration of Dubai. The application of Octalysis results in the rating of the information system regarding its potentiality of becoming a gamified system. Finally, several game elements are suggested to improve the overall score of the system and help the users to adopt successfully the artificial technology. The practical value of this paper lies in the fact that it suggests gamification and Octalysisas a useful tool for decision-makers that aim to adopt this technology in public organizations. Games could be the next big thing in both entertaining and helping the public sector to use new technologies. Unless the public administration adopts this exciting concept then the citizens will lose the opportunity to enjoy all the benefits that AI will offer for the digital world.
Fourth research article of this volume was authored by Bhavna Dwivedi entitled "Scanning the Database with The XSS Detection Using the Fitness Algorithm". In this paper, we provide an overview of the tool used in XSS detection. This tool helps us to detect the XSS attacker. XSS is the malware that allows the attacker to attack in any web-application and stolen the client data from the server, which the client or customer is storage when even the fill form in that web application. We analyze a new and efficient algorithm that helps us to secure the database for the server-side. The Genetic Fitness Algorithm is used to secure the database for the server-side, there are many algorithms like multi-path, crossover, which is used to detect the XSS attacker, but this algorithm is not accurate and satisfied the database security. We will analyze the genetic fitness algorithm and have many properties to achieve security for the database. It is complicated for which it is difficult for any attackers to break the security and steal the data from the server site.
Finally, the last or the fifth research article of this volume was entitled "A Study on Biomedical Engineering in Healthcare" authored by Ayushi and Somesh. In this paper, they discussed various introductory terms related to biomedical engineering and the health care industry, which are amalgamated together. The paper further discusses the pros and cons of biomedical engineering in the health care industry. This paper mainly focuses on some of the latest medical tools, instruments and technologies like biosensors, biomedical signal processing, biomedical imaging and image processing, bioinformatics and computational biology, health informatics, biomechanics, bio robotics, diagnostic, cardiopulmonary systems engineering, and therapeutic systems, neural engineering, rehabilitation engineering, variable and implantable technologies, micro and nanotechnologies, tissue engineering and regenerative medicine, biomedical engineering in the education industry and society. A case study has also been included to support the understanding of the above technologies viz. a case study on image-guided interventions. The discussion has been concluded with the observation that biomedical engineering can be deeply integrated with healthcare, and various devices and instruments can be designed to cure various diseases
IJMLNCE Editorial Note Volume No 04, Issue No 01
No full textIt gives me pleasure to present the editorial preface of the International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) Volume 04 No 01 (2020). This issue comprises five manuscripts contributed by authors.
The first paper contributed by Ramachandran A et al. titles, "Efficient Distributed Web Crawler Using Hefty and Enhanced Bandwidth Algorithms for Drug Website Search," In this paper, a novel Hefty Algorithm and enhanced bandwidth algorithm are combined for a better-distributed crawling system. The hefty algorithm was implemented to provide efficient and robust surfing results while applying on the drug web search. Refabricate a proficient search structure is very important due to the current scale of the web. Search engines mine information from the web, and a web crawler program that surfs the web in an efficient manner. A distributed crawler belongs to a variant of a web crawler, uses a dispersed computation method. In this paper, we design and implement the concept of Efficient Distributed Web Crawler using enhanced bandwidth and hefty algorithms. Mostly Web Crawler does not have any distributed cluster performance system and any implemented algorithm. We also implemented an Enhanced Bandwidth algorithm to improve the efficiency of the proposed crawler.
The second paper, titled "Revealing Brain Tumor Using Cross-Validated NGBoost Classifier," is authored by Shawni Dutta and Samir Bandyopadhyay. In this article, the author writes that the brain is the most complicated and delicate anatomical human body structure. Statistics prove that, among various brain ailments, brain tumors are most fatal, and in many cases, they become carcinogenic. Brain tumors are characterized by abnormal and uncontrolled growth of brain cells. It takes up space within the cranial cavity and varies in shape, size, position, and characteristics viz. It can be benign or malignant, which makes the detection of brain tumors very critical and challenging. A neurologist or neurosurgeons vital information needs to have is the precise size and location of cancer in the brain and whether it is causing any swelling or compression of the brain that may need urgent attention. This paper exploits ensemble strategy-based Machine Learning (ML) algorithms for revealing brain tumors. NGBoost algorithm and 5-fold stratified cross-validation scheme are proposed as classifier models that automatically detect patients with brain tumors. The proposed method is implemented with necessary fine-tuning parameters compared against ensemble-based baseline classifiers such as AdaBoost, Gradient Boost, Random Forest, and Extra Trees Classifier. An experimental study implies that the proposed method outperforms baseline models with significantly improved efficiency. The interfering features that impact brain tumor classification are ranked, and this ranking is retrieved from the best classifier model.
Abdulmohsen Alotaibi contributed the third article for this issue titled "Transfer Learning for Detecting Covid-19 Cases Using Chest X-Ray Images". In this interesting work, he is discussing the COVID-19 pandemic, which is a global health crisis that has already infected more than 3.5 million people and caused more than 250 thousand deaths around the globe. He focused on developing a more efficient way to detect and treat this illness. This paper utilizes transfer learning techniques to detect normal, COVID-19, and viral pneumonia cases from Chest X-Ray images. Four pre-trained models on ImageNet were chosen as the base model, which are ResNet50, VGG19, DenseNet121, and InceptionV3. The performance metrics of each fine-tuned model are overall similar. With an average recall, precision, f1-score, and accuracy of 97.42%, 97.42%, 97.23%, 98.3% respectively.
The Fourth article, title " The Economic Impact of Social Media Fraud and it\u27s Remedies," was contributed by shakik mahmud et al. This paper presents the economic impact of social media fraud in Bangladesh and its IT-based prevention model. Online privacy and security problems become a big concern online day by day—many types of issues growing up here, for example, phishing, hacking, sabotage, etc. Social media is a popular and powerful tool to express personal life and also business purposes in Bangladesh. Social communicating websites such as Facebook, Twitter, WhatsApp, and LinkedIn are popular social sites. Facebook is the most popular one. Through these media, people communicate with their friends and family and share thoughts, photos, videos, and lots of data. Many types of business and commerce have developed on social media. Presently, people depend on it, so it\u27s marketing value increases day by day well. Some Tech fraud groups have been formed and wake up to hack money in some tricky way in this big virtual society. At present, social media is one of the critical areas for fraudsters. This paper will show based on our study how much money is being spent through it and others by the IT-based prevention model of this problem.
The fifth article of this issue, titled " The Performance Enhancement Systems of Human Iris Pattern and Recognition Method through Digital Authentication Application," is contributed by Krishnaveni N et al. Authors discusses that human iris and recognition patterns have been recognized as the best biometric marking ever found. The iris and the textured iris patterns\u27 uniqueness tend to remain natural, unchangeable, and recognizable through existence. Mathematical analyses of the unique stable patterns formed within the iris include Iris detection methods, and a comparative analysis is carried out utilizing an established database. In this document, a clean electoral system is created to build a fraud-free ID list of electors. To find the Iris and Eyes, the algorithm of canny edge detection is used, Dougman\u27s normalization procedure is used, object filters are added, and the corresponding process is finally conducted for the Euclidian set. Biometric authentication confirms our identification by being a simple and increasingly secure method. They implemented a weighted, majority voting process for all biometric authentication systems utilizing a bitwise contrast between inscription and biometric models to resolve this problem and enable Iris identification in less than ideal images. We also observed that the approach outdoes the current majority and efficient bit sorting strategies through a set of tests with the database CASIA iris. Our process is an easy and efficient way to boost the accuracy of established iris detection systems.
I am sure that these five papers included in the International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) Volume 04 No 01 (2020) will be useful to the readers and researchers. At this end, I am thankful to the Editorial board member for their timely support in the review. I am looking forward to receiving your unpublished research work for Volume 04 No 04 (2020)
SCANNING THE DATABASE WITH THE XSS DETECTION USING THE FITNESS ALGORITHM
No full textIn this paper, we provide an overview of the tool used in XSS detection. This tool helps us to detect the XSS attacker. XSS is the malware which helps the attacker to attack in any web-application and stolen the client data from the server, which the client or customer is storage when even the fill form in that web application We analyze a new and efficient algorithm that helps us to secure the database for the server-side. The Genetic Fitness Algorithm is used to secure the database for the server-side, there are many algorithms like multi-path, crossover which is used to detect the XSS attacker but this algorithm is not accurate and satisfied the database security. We will analyze the genetic fitness algorithm and have many properties to achieve security for the database. It is complicated for which it is difficult for any attackers to break the security and steal the data from the server site
Utilize Machine Learning Methods to Detect Plaintext Passwords
No full textEvery company is a target today, no matter the type of business it does. Hackers and cybercriminals are after data which they can monetize in many ways. Being proactive and have a defensive and protective plan in place such as evaluating and assessing IT security is a great recipe for avoiding data breaches and consequently, business disasters. Passwords are the most popular authentication method, mainly because they are easy to implement, require no special hardware or software, and are familiar to users and developers. Unfortunately, most users store their sensitive information or credentials in plain-text that might be accessible to attackers. Since the information is not encrypted and stored or transferred in cleartext, attackers will be able to read it easily. Storing a plaintext password in a configuration file allows anyone who can read the file access to the password-protected resource. Developers sometimes believe that they cannot defend the application from someone who has access to the configuration, but this attitude makes an attacker’s job easier. Good password management guidelines require that a password must never be stored in plaintext.
The question is why not utilizing a machine learning platform that can be trained to search text in a computer resource, detect a string of plaintext characters, and analyze the string of characters to predict or detect a plaintext password on a computer resource asset. Since plaintext passwords can be stored anywhere on a computer network, including on a computer resource asset, such as, for example, a file (for example, a configuration file), a router, a switch, a computer, a server, a database or source code, the solution can be arranged to target computer resource assets on the network and search those computer resource assets.
The machine will be able to detect a plaintext password in a character string by analyzing plaintext character strings for common password complexity, such as, for example, including at least one uppercase letter, lowercase letter, number, special character, and text length (for example, minimum of eight characters). Then check the similarity of the character string against a database comprising passwords, including, for example, passwords that were previously found or identified by the solution, or passwords that were input or loaded into the database from a list, table, record, file, or a computer resource that can input passwords to the database. Also, it will predict a level of certainty that a character string includes a password and output a confidence score based on the predicted level of certainty. Finally, it will categorize the confidence score in any number of prediction certainty levels, including, for example, three levels – high, medium, or low.