Leaf diseases pose a significant threat to the productivity and quality of mango crops, necessitating effective detection and management strategies. This study presents an automated system for the detection of mango leaf diseases using machine learning techniques. Using image processing methods to extract relevant features from leaf images, various machine learning models were trained to accurately classify common mango leaf diseases. This approach involved using a comprehensive dataset of diseased and healthy mango leaves, preprocessing images, and extracting features such as color, texture, and shape. Features were extracted using MobileNetV2 and EfficientNetV2. Feature fusion was performed using a dense layer. Principal component analysis was used to reduce dimensionality. These reduced features were then fed to a support vector classifier to classify the mango leaf images. Eight different classes were considered, including the seven most common diseases in mango leaves and one class for healthy ones. The proposed model achieved a remarkable accuracy of 99.83 %. These results demonstrate that machine learning models can achieve high accuracy in the early detection of mango leaf diseases. Implementing this system in agricultural practices can significantly help farmers in timely disease management, reducing crop losses, and improving mango production.

Integrating three-dimensional printing (3DP) in healthcare has modernized medical diagnostics and therapies by presenting various accurate, efficient, and patient-specific tailored solutions. This review critically examines the integration of 3DP in the development of miniaturized devices specifically tailored for point-of-care testing (PoCT) applications in healthcare. Focusing on progressive additive manufacturing techniques, such as material extrusion, vat photopolymerization, and powder bed fusion, the review classifies and evaluates their contributions toward designing compact, portable, and patient-specific diagnostic devices. Unlike previous reviews that treat 3DP or PoCT generically, this work uniquely bridges the technical innovations of 3DP with clinical applications by analyzing wearable sensors, biosensors, lab-on-chip systems, and microfluidic platforms. It highlights recent case studies, performance metrics, and the role of 3DP in enhancing diagnostic speed, accessibility, and personalization. The review also explores challenges such as material standardization and regulatory hurdles while outlining future directions involving artificial intelligence (AI), the Internet of Things (IoT), and multifunctional integration. This focused assessment establishes 3DP as a transformative force in decentralized and precision healthcare.

In today's digital age, online spaces offer incredible opportunities for communication, networking, and personal expression. However, they also harbor the potential for harassment and abuse, which can be particularly detrimental to women. Online harassment, including cyberbullying, stalking, and threats, can have profound emotional, psychological, and even physical consequences. To combat this pervasive issue and create safer digital environments, AI-driven cybersecurity solutions are increasingly playing a vital role in shielding women from online threats. This chapter explores the crucial role of artificial intelligence (AI) in addressing the pervasive issue of online harassment. The chapter makes a comprehensive survey and analysis of AI algorithms, natural language processing, and machine learning models to focus role of these technologies in reshaping the landscape of digital safety.

Sleep Apnea (SA) can cause health complications including heart stroke and neurological disorders. The Polysomnography (PSG) test can detect the severity of sleep disturbance. However, it is expensive and requires a dedicated sleep laboratory and expertise to examine the patients. Therefore, it is not available to a large population in developing countries. This leads to the development of cost-effective and automated patient examination methods for the detection of sleep apnea. This study suggests an approach of using the ECG signals to categorize sleep apnea. In this work, we have devised an original technique of feature space designing by intelligently hybridizing the multirate processing, a mix of wavelet-empirical mode decomposition (W-EMD), modes-based Hjorth features extraction, and Adam-based optimized Multilayer perceptron neural network (MLPNN) for automated categorization of apnea. A publicly available ECG dataset is used for evaluating the performance of the suggested approach. Experiments are performed for four different sub-bands of the considered ECG signals. For each selected sub-band, five "Intrinsic Mode Functions" (IMFs) are extracted. Onward, three Hjorth features: complexity, activity, and mobility are mined from each IMF. In this way, four feature sets are formed based on wavelet-driven selected sub-bands. The performance of optimized MLPNN, for the apnea categorization, is compared for each feature set. Five different evaluation parameters are used to assess the performance. For the same dataset, a systematic comparison with current state-of-the-artwork has been done. Results have shown a classification accuracy of 98.12%.

New technologies are constantly emerging in the modern world and changing the way we live our everyday lives. Although technology has many useful applications, there are various ways it can potentially be abused. Unmanned aerial vehicles (UAVs) are one of the most rapidly developing technologies, with potentially far-reaching consequences. A new focus on UAV applications has fueled rising concerns with regards to security, specifically around networked UAVs. UAVs may be managed from a remote place with relative ease. Essential operations involving the use of military tactics and weapons involve employing them in a variety of situations, such as reconnaissance, surveillance, and offensive, defensive, and civilian capacities. Message insertion, message manipulation, jamming, and GPS spoofing are the most commonly used cyber-attacks against these systems. Ensuring the security of electronics and communications in systems that employ several UAVs is of utmost importance to guarantee their safety and dependability in military and civilian activities. Many technological methods have been developed over the past decade for securing UAVs from cyber-attacks. This paper attempts to summaries the problems that can arise with unmanned aerial vehicles (UAVs), cyber-attacks, and the countermeasures used to protect against them. This is the first paper that details all of the past cyber-attacks on unmanned aerial vehicles (UAVs).