International Journal of Recent Development in Computer Technology & Software Applications [ISSN: 2581-6276 (online)]

Application of Random Forest Algorithm in Healthcare Sectors

G. Haripriya, K. Abinaya, N. Aarthi, P. Praveen Kumar

Safety culture is a multidimensional phrase in healthcare delivery systems that have been linked to medical errors and patient safety incidents. Despite this, there is little evidence to suggest that safety culture characteristics have an impact on overall patient safety. Aside from that, complex statistical analysis has only been applied sparingly in previous data studies on safety culture practices. In healthcare, there is a centralized organization delivery systems is a multifaceted issue that is linked to medical errors and patient safety concerns. Using health facility aggregate data from the United States, this study investigates the communication between recognized organizational safety components and patient security grade to address these safety concerns. Random forests computer vision program based on trees technique, researchers use to predict accurate and stable relationships between variables. Amidst this, there is little information available about which aspects of patient safety culture are most important. Furthermore, advanced statistical analysis in previous reviews of data on safety culture has been limited, as previously mentioned. Additionally, random forests, a machine learning technique based on trees, estimate precise and stable associations between variables. As a result, researchers used data from a study in collective US hospitals to investigate the relationship between identified safety culture components and patient safety grades. As a result of the findings, the scientists found that healthcare quality knowledge, organizational factors, and top management objectives all play a crucial influence in determining patient safety grades. Security concerns in the work unit, as well as the work environment created by hospital management, have an impact on patient safety outcomes.

Lung Cancer Diagnosis using Deep Learning Methods in Health Care Systems

P. Manju Bala, S. Usharani, G. Leema Roselin, A. Balachandar, M. Pavithra

The deep learning method for detecting lung disease in thoracic X-rays of the general population will be verified. Retrospective assessment of a deep learning system with patients' chest x-rays. The field under Receiver Operating characteristic feature Curves (ROC) including clinical metrics comprising sensitiveness and false-positive (FPR) have been generated to evaluate the effectiveness of the algorithm for the identification of visible lung cancers. Tests utilizing relatively good and negative results are compared. That deep learning system was used between 2008 and 2012, as its performance was calculated for a screening sample receiving thoracic radiography. Researchers are now striving to boost the effectiveness of CAD in computational tomography screening for cancer through different deep learning approaches. For the detection of lung cancer most advanced depth education algorithms and architectures are used. These systems are split into two (1) detection systems that identify candidate nodules from the initial CT scan, and (2) false alarm control systems that categories them into malignant or benign tumors from a collection of candidate nodules. They are classified into two categories. The major features and performance of several methods are described. A deep learning model has found similarly effective lung nodules in radiologists that are beneficial for radiologists who treat low-incidence patients with lung conditions.

A Survey on Sentiment Analysis using Machine Learning

Shubham Jain

In this review, various machine learning methods are used for opinion analysis. For the most part, did feeling examination by using AI classifiers like SVM (support vector machine), Random Forest, Naïve Bayes. In this, we see a few papers that help the new specialists establish an appropriate way to explore further. In this, there is a proposed strategy for the latest research program. Online media is the greatest medium to impart individuals' insights on various subjects. Feeling examination using AI strategies and with no human interference, machines will give individuals a precise opinion. Opinion study transforms text into positive, negative or impartial. Thus, any organization, establishment, or film commentator can take individuals' viewpoints and make further strides, as shown by that.

Shielding the Vault: A Review of Cyber-security Issues and Strategies in Contemporary Banking

Neetu Chauhan, Manu Chauhan

This paper examines the cyber security challenges faced by modern Indian banks and explores potential solutions to mitigate these risks. The increasing reliance on technology, the growing volume of sensitive data, and the sophistication of cyber-attacks have made cyber security a critical imperative for financial institutions. The paper analyzes common threats such as phishing, malware, ransom ware, and social engineering attacks. It also discusses the challenges of complying with regulatory requirements and the technological vulnerabilities associated with modern banking systems. The human factor and the role of social engineering in compromising security are explored. Potential solutions and mitigation strategies are presented, including technological measures like encryption, firewalls, and intrusion detection systems, organizational initiatives like employee training and incident response planning, and regulatory measures to strengthen the cyber security framework.

The paper concludes by emphasizing the need for a comprehensive and proactive approach to cyber security in Indian banking, involving a combination of technological, organizational, and regulatory measures. By addressing these challenges and implementing effective security practices, banks can protect themselves from the growing threat landscape and safeguard sensitive customer data.