Template-Type: ReDIF-Article 1.0 Author-Name: Anupam Mehrotra Author-X-Name-First: Anupam Author-X-Name-Last: Mehrotra Author-Name: Amit Kumar Pandey Author-X-Name-First: Amit Kumar Author-X-Name-Last: Pandey Author-Name: Ashok Chopra Author-X-Name-First: Ashok Author-X-Name-Last: Chopra Title: Macroeconomic variables and exchange rates - a complex interplay and the role of a central bank Abstract: Fluctuations in exchange rates are both the causes and consequences of changes in major macroeconomic variables. One triggers the other in a complex interplay of forces, and there is a chain of actions and reactions leading to a sizeable shift in certain macroeconomic variables at an unacceptable pace or in an undesired direction calling, at times, central banking or government intervention to break the momentum. The paper analyses the nexus between some major macroeconomic variables and the exchange rate. It also examines the situations where it may be required for the central bank to make changes in the policy rates or other components of its monetary policy to curb excessive volatility in exchange rates and/or to influence the macroeconomic variables in the general interest of the economy. Journal: Int. J. of Information and Decision Sciences Pages: 39-59 Issue: 1 Volume: 18 Year: 2026 Keywords: exchange rate; macroeconomic variables; central bank; GDP; inflation; interest rates. File-URL: http://www.inderscience.com/link.php?id=152461 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:18:y:2026:i:1:p:39-59 Template-Type: ReDIF-Article 1.0 Author-Name: Donald Douglas Atsa'am Author-X-Name-First: Donald Douglas Author-X-Name-Last: Atsa'am Author-Name: Benjamin Terzungwe Tough Author-X-Name-First: Benjamin Terzungwe Author-X-Name-Last: Tough Author-Name: Barr. Doose Atsa'am Author-X-Name-First: Barr. Doose Author-X-Name-Last: Atsa'am Title: A classification model for terror incidents by affiliation category of perpetrators Abstract: A terror incident could be perpetrated by either a lone wolf who acts on their own or affiliated terrorists who work for a terror group. In this study, the data from the global terrorism database and the artificial neural network algorithm were employed to construct a classification model that could predict the probable affiliation category of the perpetrator(s) of a terror incident. The model uses information such as type of attack, casualty figure, claim of responsibility, and damage to property to distinguish a lone wolf attack from a terror group attack. Various metrics of model diagnostics were employed to test the suitability of the model for predictions, and it yielded a balanced classification accuracy of 85%. The model adds another dimension to the existing criteria for terrorism classification. Further, the model could serve as a useful tool in the study of terrorism and counterterrorism. Journal: Int. J. of Information and Decision Sciences Pages: 60-70 Issue: 1 Volume: 18 Year: 2026 Keywords: lone wolf; affiliated terrorist; affiliation category; classification model; artificial neural network; ANN. File-URL: http://www.inderscience.com/link.php?id=152462 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:18:y:2026:i:1:p:60-70 Template-Type: ReDIF-Article 1.0 Author-Name: Zahra Jamebozorg Author-X-Name-First: Zahra Author-X-Name-Last: Jamebozorg Author-Name: Fatemeh Jafarkhani Author-X-Name-First: Fatemeh Author-X-Name-Last: Jafarkhani Author-Name: Khaled Nawaser Author-X-Name-First: Khaled Author-X-Name-Last: Nawaser Title: The effect of software-based mind map of educational design in development of the electrical engineering students' learning level Abstract: The present study aims to investigate the effect of a software-based mind map for educational design in the electrical engineering students' learning level development. The qualitative phase of the research featured deductive method of content analysis while the quantitative phase was characterised by a quasi-experimental design. The analytical framework employed for the first phase of the research entailed open and axial coding, whereas the second stage involved the utilisation of descriptive and inferential statistical analyses. Results of the first phase of the study including 54 participants revealed the components of instructional design for mind map. The lesson plans for the treatment phase were prepared with respect to the educational design elements, and they were conducted by the experimental group including 23 students. Results also showed that applying mind map into the instructional design has improved students' learning level. Journal: Int. J. of Information and Decision Sciences Pages: 1-14 Issue: 1 Volume: 18 Year: 2026 Keywords: instructional design model; mind map; software-based; electrical engineering; learning level. File-URL: http://www.inderscience.com/link.php?id=152463 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:18:y:2026:i:1:p:1-14 Template-Type: ReDIF-Article 1.0 Author-Name: S. Palanikumar Author-X-Name-First: S. Author-X-Name-Last: Palanikumar Author-Name: Kaliyappan Sivakumar Author-X-Name-First: Kaliyappan Author-X-Name-Last: Sivakumar Author-Name: R. Harikrishnan Author-X-Name-First: R. Author-X-Name-Last: Harikrishnan Author-Name: K. Selvi Author-X-Name-First: K. Author-X-Name-Last: Selvi Title: Optimising accuracy rate of genomic image representation of human coronavirus sequences for COVID-19 detection Abstract: Due to the significant mortality rate associated with the coronavirus disease 2019 (COVID-19), it is impossible to ignore this newly discovered illness that has an impact on healthcare on a worldwide scale. At this time, physicians are making use of pictures produced by computed tomography (CT) in order to aid them in recognising COVID-19 in its earlier stages. In this study, a COVID-19 diagnostic system is built with the help of a convolutional neural network (CNN) and stacked autoencoder. Before using the three different CT imaging methods to tell the difference between normal and COVID-19 cases. During the training phase of the deep learning model that was used, a demanding and large-scale CT image dataset was utilised. This allowed for accurate reporting of the model's ultimate performance. This model was correct 88.30% of the time, sensitive 87.65% of the time, and specific 87.97% of the time. Journal: Int. J. of Information and Decision Sciences Pages: 71-82 Issue: 1 Volume: 18 Year: 2026 Keywords: COVID-19; artificial intelligence; AI; deep learning; machine learning tasks; supervised and un-supervised learning. File-URL: http://www.inderscience.com/link.php?id=152464 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:18:y:2026:i:1:p:71-82 Template-Type: ReDIF-Article 1.0 Author-Name: Piyush Bhushan Singh Author-X-Name-First: Piyush Bhushan Author-X-Name-Last: Singh Author-Name: Pawan Singh Author-X-Name-First: Pawan Author-X-Name-Last: Singh Author-Name: Harsh Dev Author-X-Name-First: Harsh Author-X-Name-Last: Dev Title: Implementing shark smell algorithm and sigmoid function-based new position update optimisation for four-phased glaucoma detection model Abstract: Vertical and horizontal cup-disc ratio (CDR) is an important constraint, which is utilised clinically for detecting glaucoma or to scrutinise its progression and is evaluated manually from fundus images. Owing to the scarcity of glaucoma specialists and owing to the rise in glaucoma's affected persons, an automated approach can be much helpful for screening glaucoma. Here, the study intends to create a new glaucoma detection scheme with 4 phases. Initially, preprocessing is done via median filtering of the input image. Then, optic cup segmentation is done via an enhanced U-net method. Moreover, features like cup to disc ratio (CDR), inferior superior nasal temporal (ISNT) features, fractal features and proposed local Gabor features are determined. These characteristics are categorised using an improved version of the LSTM, and the weights are adjusted using the shark smell algorithm with sigmoid function-based new position update (SSA-SFNPU). Further, the examination is done regarding wide-ranging metrics. Journal: Int. J. of Information and Decision Sciences Pages: 15-38 Issue: 1 Volume: 18 Year: 2026 Keywords: glaucoma; pre-processing; Gabor features; long short-term memory; LSTM; optic disc ratio. File-URL: http://www.inderscience.com/link.php?id=152481 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijidsc:v:18:y:2026:i:1:p:15-38