Plant diseases are the main factor in plant mortality and destruction, especially in trees. Early discovery, however, can assist to manage and treat this issue efficiently. To increase output, crop and plant lesions are detected and stopped as soon as feasible. Because it relies solely on visual observation, manual inspection of plant leaf diseases is time-consuming and expensive. The authors offer methods for identifying and categorizing plant leaf diseases using computer vision. Pre-processing original images to visualize contaminated areas, feature extraction from unprocessed or segmented images, feature fusion, feature selection, and classification are a few examples of computer vision approaches. The fusion technique is used to combine the target's numerical data features, which go beyond the picture, with the extracted image features to increase the target's feature representation. The following are the principal issues that researchers found in the literature: Low-contrast infected regions. Extract redundant and irrelevant information, which degrades classification accuracy; Redundant and irrelevant information may lengthen computation times and the targeted models performance will suffer as a result. This study proposed a framework for classifying plant leaf diseases based on the best feature selection and a deep learning fusion model. In the suggested approach, contrast is first enhanced using a pre-processing model, and then the issue of an unbalanced dataset is resolved via data augmentation. The proposed Deep Fusion Learning Model (DFLM) shows an accuracy of 98.8% in comparison with other models.

It is crucial for organizations to secure their data in the internet era. The use of Intrusion Detection Systems (IDS) implies this security. Several researchers used various tools and methods to implement various IDS models. However, a few performance concerns that must be resolved are crucial from a security standpoint. The problems pertain to the IDS time efficiency referred as timeliness, accuracy as well as the fault tolerance. The proposed model of intrusion detection has two phases of detection. Every phase uses a different set of machine learning algorithms. Phase I employs Support Vector Machine (SVM) and k nearest neighbor (kNN), whereas Phase II uses Decision Tree and Naïve Bayes. This two phase detection takes care of reducing false positives and false negatives. To compensate the execution time of these four techniques, the big data environment—Hadoop Distributed File System (HDFS)—is utilized as the underlying storage and processing structure. With such arrangement of two phases, the model gives accuracy of 97.29% overall for known and unknown attacks. For known attacks it gives 99.49% and for unknown attacks it gives 96.28% accuracy in detecting intrusion. Also, the time efficiency is measured for training and testing of the model, for training with 10,000 records, it took 0.7 seconds which is very efficient as considered to existing systems. The detailed performance achievements are discussed in results section. Also, because of HDFS, it becomes distributed and fault tolerant intrusion detection system.

The widespread use of unmanned aerial vehicles in a variety of sectors poses major challenges to these issues of cyber security and data protection. In this paper, a detailed study of the security vulnerabilities in UAV-based systems was conducted and analytically categorized into three types: Software, hardware and communication links. These vulnerabilities include access rights without authentication, control channel tampering, data breaches and spoofing of the global positioning system. These vulnerabilities jeopardize the security, integrity of the data and privacy of not only the drone but also the users. The study analyzes some of the security protocols that aim to mitigate these risks. They range from advanced encryption techniques to authentication mechanisms and intrusion detection systems that can use classification models based on machine learning. The study also provides a context in which emerging technologies such as blockchain, machine learning and deep learning can make a greater contribution to securing drones. The detailed and in-depth discussion on the development of the security solution that the work provides emphasizes the importance of securing UAV operations and their data integrity aspects alongside securing public trust for autonomous systems.

Alzheimer’s disease (AD) is a neurological disorder that causes memory decline and loss of cognitiveabilities. AD directly impacts the brain activity of affected individuals, which can be reflected inelectroencephalogram (EEG) signals. Previous studies have typically relied on statistical, spectral, and waveletfeatures in order detect AD using EEG signals. Recurrence Quantification Analysis (RQA) is a non-lineartechnique that has been successfully used to analyze EEG signals in several other domains including emotionrecognition and autism detection. However, RQA features have not yet been fully investigated for AD diagnosis.The aim of this work is to thoroughly investigate the usefulness of RQA features for AD diagnosis. Fifteen RQAfeatures were computed along with statistical measures, Hjorth parameters, and relative power in order to comparethe performance of the RQA features to other commonly utilized EEG features. All features were computed fromthe different brain regions. Experimental results indicated that RQA features outperformed all other feature groupsregardless of the considered brain region. RQA features achieved accuracies ranging from 89.6% to 98.2% using asupport machine vector (SVM) classifier with leave-one-subject-out (LOSO) cross-validation. These results arebetween 25% to 40% higher than the three other considered feature groups. Feature ranking was performed to findthe most relevant RQA features, identifying seven of the fifteen computed RQA features. This work sheds light onthe potential of RQA features for reliable AD diagnosis and paves the way for their integration in computer-aidedAD diagnostic tools using EEG signals.

Speech signals often contain different forms of background and environmental noise. For the development of an efficient speech recognition system, it is essential to preprocess noisy speech signals to reduce the impact of these disturbances. Notably, prior research has paid limited attention to pink and babble noises. This gap in knowledge inspired us to develop and implement hybrid algorithms tailored to handle these specific noise types. We introduce a hybrid method that combines the Bionic Wavelet transform with Adaptive Filtering to enhance signal strength. The performance of this method is assessed using various metrics, including Mean Squared Error, Signal-to-Noise Ratio, and Peak Signal-to-Noise Ratio. Notably, our findings indicate that SNR and PSNR metrics are especially effective in enhancing the handling of pink and babble noises.

Epilepsy is a neurological disorder characterized by recurrent seizures, which can affect individuals of all age groups, but infants and older individuals are particularly vulnerable. Sudden epileptic attacks can pose significant risks and be life-threatening, impacting the overall quality of life of affected individuals. With the progress made in medical science, Electroencephalography (EEG) has emerged as a valuable tool for diagnosing and predicting seizure occurrences. The availability of wearable EEG devices, including caps and helmets, has become increasingly prominent in the market. As a result, there has been a recent surge in the development of deep learning-based systems. These systems are helpful for diagnosis in hospital settings and for mobile applications that provide timely warnings and predictions regarding seizure onset. Most of the existing state-of-the-art (SOTA) approaches focus on distinguishing between healthy and epileptic patients. Some studies categorize individuals into three classes: healthy, experiencing the onset of a seizure, or currently having a seizure, specifically focusing on mobile applications. However, limited literature is available on the five-class problem, which is valuable for localization and diagnosis in hospitals and mobile applications. In this regard, we propose our novel model, named EpilConNet, and conduct extensive experiments on a real-world dataset to demonstrate its efficacy in all modes of classification. EpilConNet results in a significant increase of 4% in accuracy in five-class classification.

Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models’ performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieved 97.6% and 94.0% respectively, while in terms of Area Under the Curve (AUC) score, both models achieved more than 99%, with the difference in inference time between each model over 844 images being 13 seconds.

The emergence of increasingly capable artificial intelligence (AI) systems has raised concerns about the potential extreme risks associated with them. The issue has drawn substantial attention in academic literature and compelled legislators of regulatory frameworks like the European Union AI Act (AIA) to readapt them to the new paradigm. This paper examines whether the European Parliament’s draft of the AIA constitutes an appropriate approach to address the risks derived from frontier models. In particular, we discuss whether the AIA reflects the policy needs diagnosed by recent literature and determine if the requirements falling on providers of foundation models are appropriate, sufficient, and durable. We find that the provisions are generally adequate, but insufficiently defined in some areas and lacking in others. Finally, the AIA is characterized as an evolving framework whose durability will depend on the institutions’ ability to adapt to future progress.

With the rise of high-quality forged images on social media and other platforms, there is a need for algorithms that can recognize the originality. Detecting copy-move forgery is essential for ensuring the authenticity and integrity of digital images, preventing fraud and deception, and upholding the law. Copy-move forgery is the act of duplicating and pasting a portion of an image to another location within the same image. To address these issues, we propose two deep learning approaches - one using a custom architecture and the other using transfer learning. We test our method against a number of benchmark datasets and demonstrate that, in terms of accuracy and robustness against various types of image distortions, it outperforms current state-of-the-art methods. Our proposed method has applications in digital forensics, copyright defence, and image authenticity.