The growth of Internet of Things (IoT) gadgets has ushered in a new era of connectedness and convenience, but it has also sparked worries about security flaws. Long Short-Term Memory (LSTM) networks are used in this research's use of intrusion detection as a novel strategy to strengthen IoT security. The proposed LSTM-based model excels in detecting both known and evolving cyber-attack patterns with an accuracy rate of 98.75% and an F1 score of 98.59% in extensive experimental evaluations using the vast CIC-IoT2023 dataset, representing a varied array of IoT network traffic scenarios. This research contributes significantly to IoT security while addressing the urgent need for adaptable intrusion detection systems to defend against changing cyber threats. It is an essential step toward ensuring IoT technology's long-term development and dependability in a world that is becoming more interconnected.

Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called ‘HE-HER2Net’ has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.

The integration of Generative Artificial Intelligence (GAI) tools like ChatGPT, Google Bard, and Bing Chat in higher education shows excellent potential for transformation. However, this integration also raises issues in maintaining academic integrity and preventing plagiarism. In this study, we investigate and analyze practical approaches for efficiently harnessing the potential of GAI while simultaneously ensuring the preservation of assignment integrity. Despite the potential to expedite the learning process and improve accessibility, concerns regarding academic misconduct highlight the necessity for the implementation of novel GAI frameworks for higher education. To effectively tackle these challenges, we propose a conceptual framework, PAIGE (Promoting Assignment Integrity using Generative AI in Education). This framework emphasizes the ethical integration of GAI, promotes active student interaction, and cultivates opportunities for peer learning experiences. Higher education institutions can effectively utilize the PAIGE framework to leverage the promise of GAI while ensuring the preservation of assignment integrity. This approach paves the way for a responsible and thriving future in Generative AI-driven education.

The emergence of Artificial Intelligence (AI) has brought about a significant change in higher education. It has led to the adoption of more digitally advanced and collaborative models. This paper examines the potential of AI in promoting dynamic virtual teamwork and improving the collective experience in the academic world. It discusses how AI tools can be integrated into various sectors of virtual teamwork, such as academic learning, group projects, communication, assessment, research collaboration, administrative efficiency, engagement strategies, and continuous feedback mechanisms. The paper provides a comprehensive analysis of AI's role in these areas, showing how AI can personalize learning, facilitate complex group tasks, streamline communication, and provide real-time feedback. Ultimately, this will prepare students for the challenges of the professional world and enhance educational efficacy. The paper evaluates the significance of AI in each sector, offering insights into how higher education institutions can use these technologies to create an environment that fosters advanced virtual collaboration. The paper argues that strategic integration of AI is crucial in equipping students with the necessary skills and competencies for the evolving digital landscape of the 21st century.

With the advent of large language models like ChatGPT, there is interest in leveraging these tools as teaching assistants in higher education. However, important questions remain regarding the effectiveness and appropriateness of AI systems in educational settings. This study evaluated ChatGPT's potential as a teaching assistant for an introductory programming course. We conducted an experimental study where ChatGPT was prompted in response to common student questions and misconceptions from a first-year programming course. This study was conducted over a period of 2 weeks with 20 undergraduate students and 5 faculty members from the department of computer science. ChatGPT's responses were evaluated along several dimensions—accuracy, completeness, pedagogical soundness, and the ability to resolve student confusion by five course faculties through a survey. Additionally, another survey was administered to students in the course to assess their perception of ChatGPT's usefulness after interacting with the tool. The findings suggested that while ChatGPT demonstrated strengths in explaining introductory programming concepts accurately and completely, it showed weaknesses in resolving complex student confusion, adapting responses to individual needs, and providing tailored debugging assistance. This study highlighted key areas needing improvement and provided a basis to develop responsible integration strategies that harness AI to enrich rather than replace human instruction in technical courses. The results, based on the limited sample size and study duration, indicated that ChatGPT has potential as a supplemental teaching aid for core concepts, but also highlighted areas where human instruction may be particularly valuable, such as providing advanced support. Further research with larger samples and longer study periods is needed to assess the generalizability of these findings.

An increase in cyber threats directed at interconnected devices has resulted from the proliferation of the Internet of Things (IoT), which necessitates the implementation of comprehensive defenses against evolving attack vectors. This research investigates the utilization of machine learning (ML) prediction models to identify and defend against cyber-attacks targeting IoT networks. Central emphasis is placed on the thorough examination of the CIC-IoT2023 dataset, an extensive collection comprising a wide range of Distributed Denial of Service (DDoS) assaults on diverse IoT devices. This ensures the utilization of a practical and comprehensive benchmark for assessment. This study develops and compares four distinct machine learning models Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to determine their effectiveness in detecting and preventing cyber threats to the Internet of Things (IoT). The comprehensive assessment incorporates a wide range of performance indicators, such as F1-score, accuracy, precision, and recall. Significantly, the results emphasize the superior performance of DT and RF, demonstrating exceptional accuracy rates of 0.9919 and 0.9916, correspondingly. The models demonstrate an outstanding capability to differentiate between benign and malicious packets, as supported by their high precision, recall, and F1 scores. The precision-recall curves and confusion matrices provide additional evidence that DT and RF are strong contenders in the field of IoT intrusion detection. Additionally, KNN demonstrates a noteworthy accuracy of 0.9380. On the other hand, LR demonstrates the least accuracy with a value of 0.8275, underscoring its inherent incapability to classify threats. In conjunction with the realistic and diverse characteristics of the CIC-IoT2023 dataset, the study's empirical assessments provide invaluable knowledge for determining the most effective machine learning algorithms and fortification strategies to protect IoT infrastructures. Furthermore, this study establishes ground-breaking suggestions for subsequent inquiries, urging the examination of unsupervised learning approaches and the incorporation of deep learning models to decipher complex patterns within IoT networks. These developments have the potential to strengthen cybersecurity protocols for Internet of Things (IoT) ecosystems, reduce the impact of emergent risks, and promote robust defense systems against ever-changing cyber challenges.