A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.

Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history. One approach is to use ML to analyse genetic data, or patient demographics and medical history, to predict the likelihood of colon cancer. However, due to the challenges imposed by variable gene expression and the high dimensionality of cancer-related datasets, traditional transductive ML applications have limited accuracy and risk overfitting. In this paper, we propose a new hybrid feature selection model called HMLFSM-Hybrid Machine Learning Feature Selection Model to improve colon cancer gene classification. We developed a multifilter hybrid model including a two-phase feature selection approach, combining Information Gain (IG) and Genetic Algorithms (GA), and minimum Redundancy Maximum Relevance (mRMR) coupling with Particle Swarm Optimization (PSO). We critically tested our model on three colon cancer genetic datasets and found that the new framework outperformed other models with significant accuracy improvements (95%, ~97%, and ~94% accuracies for datasets 1, 2, and 3 respectively). The results show that our approach improves the classification accuracy of colon cancer detection by highlighting important and relevant genes, eliminating irrelevant ones, and revealing the genes that have a direct influence on the classification process. For colon cancer gene analysis, and along with our experiments and literature review, we found that selective input feature extraction prior to feature selection is essential for improving predictive performance.

Predicting new crescent moon visibility applying machine learning algorithms.

The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%-from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of Ramadan month within the Muslim community. This is mainly due to the imprecise observations of the new crescent Moon in different locations. Artificial intelligence and its sub-field machine learning have shown great success in their application in several fields. In this paper, we propose the use of machine learning algorithms to help in determining the start of Ramadan month by predicting the visibility of the new crescent Moon. The results obtained from our experiments have shown very good accurate prediction and evaluation performance. The Random Forest and Support Vector Machine classifiers have provided promising results compared to other classifiers considered in this study in predicting the visibility of the new Moon.

A deeper look into cybersecurity issues in the wake of Covid-19: A survey.

This study analyzed the Coronavirus (COVID-19) crisis from the angle of cyber-crime, highlighting the wide spectrum of cyberattacks that occurred around the world. The modus operandi of cyberattack campaigns was revealed by analyzing and considering cyberattacks in the context of major world events. Following what appeared to be substantial gaps between the initial breakout of the virus and the first COVID-19-related cyber-attack, the investigation indicates how attacks became significantly more frequent over time, to the point where three or four different cyber-attacks were reported on certain days. This study contributes in the direction of fifteen types of cyber-attacks which were identified as the most common pattern and its ensuing devastating events during the global COVID-19 crisis. The paper is unique because it covered the main types of cyber-attacks that most organizations are currently facing and how to address them. An intense look into the recent advances that cybercriminals leverage, the dynamism, calculated measures to tackle it, and never-explored perspectives are some of the integral parts which make this review different from other present reviewed papers on the COVID-19 pandemic. A qualitative methodology was used to provide a robust response to the objective used for the study. Using a multi-criteria decision-making problem-solving technique, many facets of cybersecurity that have been affected during the pandemic were then quantitatively ranked in ascending order of severity. The data was generated between March 2020 and December 2021, from a global survey through online contact and responses, especially from different organizations and business executives. The result show differences in cyber-attack techniques; as hacking attacks was the most frequent with a record of 330 out of 895 attacks, accounting for 37%. Next was Spam emails attack with 13%; emails with 13%; followed by malicious domains with 9%. Mobile apps followed with 8%, Phishing was 7%, Malware 7%, Browsing apps with 6%, DDoS has 6%, Website apps with 6%, and MSMM with 6%. BEC frequency was 4%, Ransomware with 2%, Botnet scored 2% and APT recorded 1%. The study recommends that it will continue to be necessary for governments and organizations to be resilient and innovative in cybersecurity decisions to overcome the current and future effects of the pandemic or similar crisis, which could be long-lasting. Hence, this study's findings will guide the creation, development, and implementation of more secure systems to safeguard people from cyber-attacks.

A framework model using multifilter feature selection to enhance colon cancer classification.

Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer. The selection of biomarker genes from these profiles is significant and crucial for cancer detection. This paper presents a framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification. Colon cancer is being extremely common nowadays among other types of cancer. There is a need to find fast and an accurate method to detect the tissues, and enhance the diagnostic process and the drug discovery. This paper reports on a study whose objective has been to improve the diagnosis of cancer of the colon through a two-stage, multifilter model of feature selection. The model described deals with feature selection using a combination of Information Gain and a Genetic Algorithm. The next stage is to filter and rank the genes identified through this method using the minimum Redundancy Maximum Relevance (mRMR) technique. The final phase is to further analyze the data using correlated machine learning algorithms. This two-stage approach, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells. It is found that Decision Tree, K-Nearest Neighbor, and Naïve Bayes classifiers had showed promising accurate results using the developed hybrid framework model. It is concluded that the performance of our proposed method has achieved a higher accuracy in comparison with the existing methods reported in the literatures. This study can be used as a clue to enhance treatment and drug discovery for the colon cancer cure.

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

BACKGROUND AND OBJECTIVES: This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. METHODS: In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. RESULTS: It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). CONCLUSIONS: It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society.