Deep Learning-based Weapon Detection using Yolov8

Deep learning (DL), a subset of machine learning (ML), has demonstrated remarkable success in image recognition and object detection tasks. This study presents a deep learning-based approach for offline weapon detection using the YOLOv8m architecture. A custom YOLO-formatted dataset was developed, comprising over 10,000 annotated images spanning two weapon categories: guns (all types of firearms) and knives (all types). The model achieved a Mean Average Precision ([email protected]) of 0.852. and [email protected]:0.95 of 0.622, with precision and recall scores of 0.89 and 0.80, respectively. The class-wise evaluation revealed strong detection across both weapons, with [email protected] of 0.871 for knives and 0.831 for guns. Despite occasional false positives and class confusion, the system shows promise for offline weapon detection tasks

Bioremediation of Textile Disperse Dyes using White-Rot Fungi Trametes versicolor

Disperse dyes, frequently used in textile dyeing processes, present a particular challenge because of their recalcitrant nature. With an emphasis on wastewater effluent treatment, white-rot fungi Trametes versicolor were used. The fungus was cultured on different media and optimized various biochemical parameters (temperature, pH, inoculum size, dye concentration, and culturing time). After their biomass, disperse Red-I (DR1) and disperse Blue-I (DB1), and textile wastewater were biodegraded with the fungi T. versicolor. The growth of T. versicolor is time taking but maximum degradation by T. versicolor (0.02 to -0.11 during 3 days) is observed. In DB1 solutions and wastewater, absorbance values started at different points. However, the efficiency of fungi was found to be more than 80%. The potential of degradation of fungi in wastewater treatment can be further maximized to reduce environmental impact

Challenges Faced by Stakeholders during the Requirement Engineering Phase: An Exploratory Study

Stakeholders are the backbone of any organization and play a vital role in the completion of any product. Different stakeholders with different roles, skills, natures, and experiences are involved throughout the Software Development Life Cycle (SDLC). Unlike other phases of SDLC, Requirement Engineering (RE) requires more stakeholders, active participation, focus, and collaboration. However, stakeholder involvement makes the RE phase more difficult and impacts other phases of Software Development. The inherent complexity of the RE phase is due to numerous factors, including diverse skill sets, language disparities, comprehension issues, and lack of interest, thereby rendering it particularly challenging for stakeholders. Literature also highlights some practices to resolve these issues, like enhancing communication and building trust among team members to overcome these challenges, but still, all these challenges affect software development in one way or another, and lead projects toward failure

A Comparative Evaluating Auditing Tools for Unverified Smart Contracts on Ethereum Blockchain

The Ethereum blockchain has transformed decentralized finance (DeFi) and is widely used to issue ERC20 tokens. However, many of these tokens rely on unverified smart contracts, which pose serious security risks. Hackers can take advantage of vulnerabilities in these unverified ERC20 tokens, leading to scams, financial losses, and a decline in user trust. Although several tools are available to audit smart contracts, their effectiveness in analyzing unverified ERC20 tokens remains uncertain. This study examines three auditing tools HoneyBadger, Maian, and Mythril by testing how well they detect security issues in unverified ERC20 tokens. The SmartBugs framework was used to support the auditing process, enabling parallel execution, standardized reports, and bulk auditing of contracts. For a thorough evaluation, two datasets were used: one from 50,581 Ethereum blockchain blocks and another from the DappRadar list of blacklisted ERC20 tokens. These datasets were chosen to provide a broad and realistic view of how the tools perform on both typical and high-risk contracts. The tools were compared based on their ability to detect issues, their execution speed, and their overall effectiveness. The results revealed clear differences in performance: some tools were better at finding vulnerabilities accurately, while others focused more on speed than depth. This study emphasizes the need to improve smart contract auditing methods and highlights the importance of developing more effective security tools to strengthen the Ethereum blockchain

An Intelligent Intrusion Detection System Using Ensemble Learning for Ultra-Dense IoT Networks

Intrusion detection refers to the process of observing and analyzing network or system incidents in a perpetual manner to identify unauthorized accesses, malicious acts, or violations of the rules. It plays a pivotal role in the protection of critical information, the prevention of security breaches, and the safety, confidentiality, and availability of company assets. Strong methods to identify and stop harmful activity are required because cybersecurity threats have grown more complex due to the quick expansion of digital infrastructure. Various researchers have conducted different research studies for intrusion detection, and different methodologies, along with traditional as well as machine learning models, have been applied with various datasets for the proposed task. This research aims to address these challenges by developing an efficient and intelligent intrusion detection system using a stacking ensemble learning approach. The proposed model integrates multiple base classifiers: Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA) to capture diverse decision boundaries, with a Random Forest acting as the meta-classifier to aggregate and optimize final predictions. The publicly available UNSW-NB15 dataset is employed in this study for intrusion detection. Python and its libraries are used for simulation purposes. After simulation, it has been achieved that the stacked model, which combines the predictions of multiple base learners through a meta-classifier, achieved a significantly higher accuracy of 99.93%. While in comparison, LDA achieved the highest accuracy of 94.25%, followed closely by SVM at 93.05%, DT at 91.00%, NB at 90.55%, and KNC at 89.81%. This demonstrates that ensemble learning, particularly stacking, can effectively leverage the strengths of individual models to greatly enhance intrusion detection performance for complex datasets

Automated Detection and Classification of Tomato Leaf Diseases Using EfficientNetB0 and Deep Learning Techniques

Tomato leaf diseases significantly impact agricultural productivity worldwide, necessitating accurate and timely detection methods. This research proposes a robust and efficient deep learning framework leveraging the “EfficientNetB0” architecture for the detection and classification of multiple tomato leaf diseases. Utilizing transfer learning alongside advanced data augmentation techniques, the model was trained on a comprehensive dataset comprising six disease categories and healthy samples, sourced from Kaggle. The proposed approach achieved an overall accuracy of 88.4%, outperforming traditional methods such as CNN, AlexNet, and S-V-M by a notable margin across all disease classes. Evaluation metrics, including precision, recall, and F1-score, further validate the model’s ability to accurately distinguish subtle disease symptoms despite class imbalance challenges. Additionally, the lightweight design of “EfficientNetB0” enables potential real-time applications in mobile and edge computing environments. These findings highlight the model’s promise as an effective tool for precision agriculture, facilitating early disease intervention and reducing crop loss. Future work will focus on expanding the dataset diversity and deploying the system in real-world agricultural settings through mobile and drone platforms

Cow Face Detection for Precision Livestock Management using YOLOv8

Precision livestock management is transforming traditional agricultural practices by boosting productivity, increasing yield, and automating tasks, all while reducing labor requirements and minimizing errors. Conventional methods for animal recognition are often unreliable, which has led to a growing preference for using cameras to identify animals, monitor their health, manage data, and maintain cattle records. However, small-scale farms with limited livestock, such as cows and goats, frequently face overfitting problems in traditional machine learning models due to insufficient training data. Identifying individual cows based on facial features becomes more effective after detecting the cow’s face. This study addresses these challenges by fine-tuning YOLOv8, a pretrained model, using a mix of self-captured images and publicly available datasets to detect cow faces in complex environments. Integrating publicly available data and leveraging a pretrained COCO model has significantly improved the model’s ability to generalize and accurately detect cow faces. YOLOv8, equipped with the COCO pretrained model, successfully detects nearly all types of cow faces, which can then be used for individual cow classification. This approach enhances cow recognition accuracy, contributing to more efficient farm management applications

Delving into the Practices Involved in the Creation and Dissemination of Misinformation

This study investigates the authenticity of news with specific training features validating the same with specific machine-learning techniques. The contents of fake news are created to make credible information that would create mass opinions and provide a strong basis to convince the readers or confuse them utterly. The fake information is usually disseminated using numerous automated algorithms. Therefore, it is very quintessential to identify the sources and authenticity of such information. With recent advancements in information communication technology, there exists a cluster of deep knowledge from which a user intends to retrieve relevant information such as news articles. For data mining and classification tasks such as fake news classification, the approach of machine learning can be employed for effective experimentation. To address the raised issues in this study, a comprehensive and diversified dataset was required that must contain relevant knowledge with sentiment tags such as authentic and fake news. To fulfill the same, a corpus comprising over 44k authentic and fake news items is collected. Moreover, this study emphasizes news classification as fake or authentic using data mining and analytics

Assessing the Efficacy of Pixel-based and Object-based Classification Techniques and Classifiers for Land Cover Mapping Using Landsat-8 and Sentinel-2 Data in Complex Mountainous Terrain

Disaster mitigation and climate-resilient planning heavily depend on accurate Land Use and Land Cover (LULC) datasets. Well-classified LULC data optimizes hazard modeling, surface runoff estimation, and sustainable land use planning, enabling informed decision-making and proactive risk reduction. However, supervised LULC classification faces challenges such as selecting optimal Machine Learning (ML) algorithms, differences in spatial and spectral resolution, and seasonal variability. This study adopts a multi-tiered approach to generate effective LULC maps for Gilgit District, Pakistan, by comparing pixel-based classification and object-based image analysis (OBIA) methods. Pixel-based classification was performed on Google Earth Engine (GEE) using Landsat-8 and Sentinel-2 imagery, applying three classifiers: Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). OBIA involved multi-resolution segmentation, followed by training and classification on image objects using the same algorithms. Validation using independent samples revealed that object-based maps were visually smoother and more realistic. Quantitatively, pixel-based RF yielded the highest accuracy: 82.9% for Landsat-8 and 78.02% for Sentinel-2. In contrast, OBIA k-NN achieved superior accuracy: 81.3% on Landsat-8 and 83.6% on Sentinel-2. Remaining classifiers also provided nearby results in both classification methods. Lower accuracy in Sentinel-2 may be due to within-class spectral variability at 10m spatial resolution, while Landsat-8’s lower resolution (30m) reduced object-based segmentation performance, resulting in object heterogeneity and misclassification. Although pixel-based classification provided promising results, OBIA ultimately demonstrated superior overall accuracy. This study highlights the importance of resolution-context compatibility and algorithm choice in enhancing LULC classification, which is essential for reliable climate-responsive planning, disaster preparedness, and sustainable development

A Robust Deep Learning Model for Early Glaucoma Detection Using Retinal Imaging

The Glaucoma Detection System is developed in such a way that it can enable early diagnosis of glaucoma by incorporating the latest technology with the patient-centric healthcare paradigm. It uses a user-friendly interface written in the Tkinter language and a Convolutional Neural Network (CNN) model, and is mostly useful in processing medical images. The purpose of the methodology is to democratize ocular care, focus on the insidious nature of glaucoma, and emphasize the need to have a highly accurate CNN model to detect the disease at the earliest stage. The key features are preset structures and real-time image processing, which will speed up detection and allow healthcare professionals to prioritize severe cases. The system encourages the development of multimodal integration and feedback of data in order to promote efficacy, proactive eye health, as well as the principles of fair access to care