International Journal of Managing Information Technology最新文献

{"title":"\"Predictive Modelling of Air Quality Index (AQI) Across Diverse Cities and States of India using Machine Learning: Investigating the Influence of Punjab's Stubble Burning on AQI Variability\"","authors":"Kamaljeet Kaur Sidhu, Habeeb Balogun, Kazeem Oluwakemi Oseni","doi":"10.5121/ijmit.2024.16102","DOIUrl":"https://doi.org/10.5121/ijmit.2024.16102","url":null,"abstract":"Air pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where they survive. With this motive, this research has predicted the AQI based on different air pollutant concentrations in the atmosphere. The dataset used for this research has been taken from the official website of CPCB. The dataset has the air pollutant concentration from 22 different monitoring stations in different cities of Delhi, Haryana, and Punjab. This data is checked for null values and outliers. But, the most important thing to note is the correct understanding and imputation of such values rather than ignoring or doing wrong imputation. The time series data has been used in this research which is tested for stationarity using The Dickey-Fuller test. Further different ML models like CatBoost, XGBoost, Random Forest, SVM regressor, time series model SARIMAX, and deep learning model LSTM have been used to predict AQI. For the performance evaluation of different models, I used MSE, RMSE, MAE, and R2. It is observed that Random Forest performed better as compared to other models.","PeriodicalId":479518,"journal":{"name":"International Journal of Managing Information Technology","volume":"342 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140417358","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":"Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model","authors":"Drici Mourad, Kazeem Oluwakemi Oseni","doi":"10.5121/ijmit.2024.16101","DOIUrl":"https://doi.org/10.5121/ijmit.2024.16101","url":null,"abstract":"Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans.","PeriodicalId":479518,"journal":{"name":"International Journal of Managing Information Technology","volume":"72 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422969","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":"Intrusion Detection System Using Customized Rules for Snort","authors":"","doi":"10.5121/ijmit.2023.15301","DOIUrl":"https://doi.org/10.5121/ijmit.2023.15301","url":null,"abstract":"These days the security provided by the computer systems is a big issue as it always has the threats of cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security provided by the blue team operations tends to be costly if done in large firms as a large number of systems need to be protected against these attacks. This leads these firms to turn to less costly security configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS attacks which are done on both physical and network layers. These attacks in turn result in loss of extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it, and taking appropriate action to not only stop the attack but also locate its source IP address. This whole process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will result in not only the detection of the attack but also the source IP address of the machine on which the attack is initiated and completed. The end product of this research will result in sets of default rules for the Snort tool which will not only be able to provide better security than its previous versions but also be able to provide the user with the IP address of the attacker or the person conducting the attack. The system involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata which is another intrusion detection system capable of detecting all these types of attacks as mentioned. Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for traffic monitoring and incident response than any other of its alternatives in the market. Since this system is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS automation in its capability to capture and report the malicious packets found during the network scan. All of this gives the IDS a capability of a low budget automated threat detection system. This paper gives complete guidelines for authors submitting papers for the AIRCC Journals.","PeriodicalId":479518,"journal":{"name":"International Journal of Managing Information Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135181211","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}