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<jats:p>Autodect framework protects management information&amp;nbsp;systems&amp;nbsp;(MIS)&amp;nbsp;and&amp;nbsp;databases&amp;nbsp;from&amp;nbsp;user&amp;nbsp;input SQL injection attacks. This framework&amp;nbsp;overcomes intrusion or penetration into the&amp;nbsp;system&amp;nbsp;by&amp;nbsp;automatically&amp;nbsp;detecting&amp;nbsp;and&amp;nbsp;preventing attacks from the user input end.&amp;nbsp;The&amp;nbsp;attack&amp;nbsp;intentions&amp;nbsp;is&amp;nbsp;also known&amp;nbsp;since&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;it is linked to a proxy database,&amp;nbsp;which&amp;nbsp;has&amp;nbsp;a&amp;nbsp;normal and abnormal code vector profiles that&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;helps to gather&amp;nbsp;information&amp;nbsp;about&amp;nbsp;the&amp;nbsp;intent as&amp;nbsp;well&amp;nbsp;as&amp;nbsp;knowing&amp;nbsp;the&amp;nbsp;areas&amp;nbsp;of&amp;nbsp;interest while conducting the attack. The information&amp;nbsp;about&amp;nbsp;the&amp;nbsp;attack&amp;nbsp;is&amp;nbsp;forwarded&amp;nbsp;to&amp;nbsp;Autodect&amp;nbsp;knowledge&amp;nbsp;base&amp;nbsp;(database), meaning&amp;nbsp;that&amp;nbsp;any&amp;nbsp;successive&amp;nbsp;attacks&amp;nbsp;from&amp;nbsp;the&amp;nbsp;proxy&amp;nbsp;database&amp;nbsp;will&amp;nbsp;be&amp;nbsp;compared to the existing attack pattern logs&amp;nbsp;in&amp;nbsp;the&amp;nbsp;knowledge&amp;nbsp;base,&amp;nbsp;in&amp;nbsp;future&amp;nbsp;this&amp;nbsp;knowledge&amp;nbsp;base-driven&amp;nbsp;database&amp;nbsp;will&amp;nbsp;help&amp;nbsp;organizations to analyze trends of attackers,&amp;nbsp;profile them and deter them. The research&amp;nbsp;evaluated&amp;nbsp;the&amp;nbsp;existing&amp;nbsp;security&amp;nbsp;frameworks used to prevent user input&amp;nbsp;SQL injection; analysis was also done on&amp;nbsp;the factors that lead to the detection of SQL&amp;nbsp;injection.&amp;nbsp;This&amp;nbsp;knowledge-based&amp;nbsp;framework&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;is able to predict the end goal of any&amp;nbsp;injected attack vector. (Known and unknown&amp;nbsp;signatures). Experiments were conducted&amp;nbsp;on true and simulation websites and open-source&amp;nbsp;datasets&amp;nbsp;to&amp;nbsp;analyze&amp;nbsp;the&amp;nbsp;performance&amp;nbsp;and&amp;nbsp;a&amp;nbsp;comparison&amp;nbsp;drawn&amp;nbsp;between&amp;nbsp;the Autodect&amp;nbsp;framework&amp;nbsp;and&amp;nbsp;other&amp;nbsp;existing&amp;nbsp;tools.&amp;nbsp;The research showed that Autodect framework has an accuracy level of 0.98. The research found a gap that all existing tools and frameworks never came up with a standard datasets for sql injection, neither do we have a universally accepted standard data set.</jats:p>

<jats:p>Reviewer Acknowledgements for Computer and Information Science, Vol. 16, No. 2, 2023</jats:p>

<jats:p>These trends in business librarianship and data librarianship matter for the management of today&amp;rsquo;s academic libraries and this topic is important to discuss because librarians must respond to the developments in data science and big data. Industry leaders such as Yuanqing Yango, CEO of Lenovo refer to &amp;ldquo;new IT&amp;rdquo; and the coming revolution stemming from the usage of smart devices, edge and cloud computing, 5G networks, and (AI) Artificial Intelligence (Lenovo, 2022). Lenovo (2022) researchers undertook a study of 500 Chief Technology Officers (CTOs)from diverse industries to ascertain their perceptions about the future of technology. Both scholars and industry leaders alike agree that the technologies that will dominate will be forged so that humanity can meet the challenges of the future and the control of information will be at the forefront of these changes. Information professionals must learn about and master the technologies that industry leaders are reimagining as innovations that will try to improve our lives because librarianship is becoming increasingly data-driven. Faculty, staff, and students rely on information professionals to help them to understand the role of &amp;ldquo;new IT&amp;rdquo; and the opportunities that it creates. We also need more informed professionals because research is data-driven. More decision makers are using big data to make effective organizational decisions. Librarians must be cognizant of the trends that are governing innovations in technology to effectively provide information services to key stakeholders. </jats:p>

<jats:p>The effect of the educational robot on the motor reaction on some karate skills&amp;gt; have revolutionized various aspects of life, including education and training. Which integrate artificial intelligence with the emotional aspect of the learner. And the overall learning process. By incorporating artificial intelligence, these programs can provide personalized learning experiences and meet individual needs. To calculate the improvement ratio and the difference between the means, as well as the effect size ratio, we can use the following formulas: Average motor reaction time Difference between means= Average motor reaction time Average skill performance time Effect Size Ratio= Difference between means= Standard Deviation Let&amp;#39;s calculate these values for each skill: -85.55 Difference between means= -46.42 Difference between means= -88.9 Difference between means= -83.7 Difference between means= 88 Difference between means= -49.762 Effect Size Ratio= Difference between means= Standard Deviation Using the provided standard deviation of 0.078, let&amp;#39;s calculate the effect size ratio for each skill: Difference between means= -33.815 Effect Size Ratio= Difference between means= -41.438 Effect Size Ratio= Difference between means= -41.894 Effect Size Ratio= Difference between means= -39.737 Effect Size Ratio= Difference between means= -49.762 Effect Size Ratio= Negative values indicate a decrease in performance. Noting that the results are negative is not evidence of poor results, but to measure the reaction rate and response speed, I need a little time through the treatments, The difference between means is -33.815, The effect size ratio is -433. Indicating a large effect size. The difference between means is -41.438, The effect size ratio is -530. Indicating a large effect size. The difference between means is -41.894, The effect size ratio is -536. Indicating a large effect size. The difference between means is -39.737, The effect size ratio is -509. Indicating a large effect size. The difference between means is -49.762, The effect size ratio is -63.79, indicating a large effect size by incorporating these recommendations.</jats:p>

<jats:p>This study presents a measure-theoretic approach to estimate the upper bound on the total variation of the di erence between hypergeometric and binomial distributions using the Kullback-Leibler information divergence. The binomial distribution can be used to find the probabilities associated with the binomial experiments. But if the sample size is large relative to the population size, the experiment may not be binomial, and a binomial distribution is not a good choice to find the probabilities associated with the experiment. The hypergeometric probability distribution is the appropriate probability model to be used when the sample size is large compared to the population size. An upper bound for the total variation in the distance between the hypergeometric and binomial distributions is derived using only the sample and population sizes. This upper bound is used to demonstrate how the hypergeometric distribution uniformly converges to the binomial distribution when the population size increases relative to the sample size.</jats:p>

<jats:p>Cancer Classification &amp;amp; Prediction Is Vitally Important for Cancer Diagnosis. DNA Microarray Technology Has Provided Genetic Data That Has Facilitated Scientists Perform Cancer Research. Traditional Methods of Classification Have Certain Limitations E.G. Traditionally A Proposed DSS Uses A Single Classifier at A Time for Classification or Prediction Purposes. To Increase Classification Accuracy, Reject Option Classifiers Has Been Proposed in Machine Learning Literature. In A Reject Option Classifier, A Rejection Region Is Defined and The Samples Fall in That Region Are Not Classified by The Classifier. The Unclassifiable Samples Are Rejected by Classifier in Order to Improve Classifier&amp;rsquo;s Accuracy. However, These Rejections Affect the Prediction Rate of The Classifier as Well. To Overcome the Problem of Low Prediction Rates by Increased Rejection of Samples by A Single Classifier, the &amp;ldquo;Delegating Classifiers&amp;rdquo; Provide Better Accuracy at Less Rejection Rate. Different Classifiers Such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K Nearest Neighbor (KNN) Etc. Have Been Proposed. Moreover, Traditionally, Same Learner Is Used As &amp;ldquo;Delegated&amp;rdquo; And &amp;ldquo;Delegating&amp;rdquo; Classifier. This Research Has Investigated the Effects of Using Different Machine Learning Classifiers in Both of The Delegated and Delegating Classifiers, And the Results Obtained Showed That Proposed Method Gives High Accuracy and Increases the Prediction Rate.</jats:p>

<jats:p>Traditional methods for evaluating water quality have a number of drawbacks. They need expensive, specialized equipment as well as knowledgeable employees first. Second, data loss may result from human error. Thirdly, because people rather than algorithms will be analyzing the obtained data, these schemes cannot foresee future patterns. Additionally, changes in the characteristics of water may result from the sample transit process. Therefore, it is challenging to consistently check water quality using outdated monitoring techniques. The disadvantages of traditional environmental monitoring techniques have been covered in this study. </jats:p>

<jats:p>Reviewer Acknowledgements for Computer and Information Science, Vol. 16, No. 3, 2023</jats:p>