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Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible security risks in drone network B5G is a crucial aspect of ensuring reliable and safe drone operation. It is necessary to establish sophisticated and robust attack detection techniques to defend against security threats as the use of drones becomes increasingly widespread and their applications diversify. This is due to the lack of privacy and security consideration in the drone's system, including an inadequate computation capability and unsecured wireless channels to perform advanced cryptographic algorithms. Intrusion detection systems (IDS) and anomaly detection systems can identify suspicious activities and monitor network traffic, such as anomalous communication patterns or unauthorized access attempts. Therefore, the study presents an enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection (EDMOA-DLAD) in Networks B5G for the purpose of Drones technique. The presented EDMOA-DLAD technique aims to recognize the attacks and classifies them on the drone network B5G. Primarily, the EDMOA-DLAD technique designs a feature selection (FS) approach using EDMOA. To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. The outcome of EDMOA-DLAD approach can be verified on benchmark datasets. A wide range of simulations inferred that the EDMOA-DLAD method obtains enhanced performance of 99.79% over other classification techniques. [ABSTRACT FROM AUTHOR]

• Slender mongooses (Galerella sanguinea) were the most prolific veldt scavengers. • Slender mongooses scattered remains up to 10.5 m in north and/or southeast directions. • Slender mongooses scavenged during the advanced and dry decomposition stages. • Slender mongoose bone modifications appear to have a distinguishing species-specific pattern. • Hadeda ibis (Bostrychia hagedash) were the only urban scavengers recorded. Unidentified human remains are frequently recovered in urban environments in South Africa, which undergo forensic (medico-legal) investigations. These remains often exhibit animal scavenging modifications and are frequently scattered by animals. This impacts the collection and forensic analysis of the remains. This study aimed to identify scavenging animals present in two urban environments in Johannesburg, South Africa, and describe their scavenging and scattering behaviours. Six pig carcasses (Sus scrofa domesticus) (30–80 kg) were placed in a veldt in Johannesburg and in an abandoned building complex. Motion-activated cameras recorded the scavenging activities. Scavenger species were identified and their behaviours, scattering pattern, and scavenging bone modifications were described. Slender mongooses (Galerella sanguinea) were the most prolific veldt scavengers. They scattered remains to a maximum distance of 10.5 m in two directions: north and southeast. These mongooses scavenged during the advanced and dry decomposition stages. Gnawing on the angle of the mandible - with multiple parallel scores on the flat surfaces and the angle margin having a stepped appearance - may be a distinguishing scavenging modification feature of the slender mongoose. Hadeda ibis (Bostrychia hagedash) were the only scavengers recorded scavenging on the intestines of a pig carcass in the abandoned building complex. They favoured colonizing insects and created multiple, large holes in the skin and removed the lips to access the insects. The described scavenging behaviours will assist in the reconstruction of postmortem events in forensic cases and the location and collection of scattered remains in Southern Africa. [ABSTRACT FROM AUTHOR]

• Using optimized InceptionV4 for early diagnosis of kidney stone in CT images. • Combined Dwarf Mongoose Optimization (CDMO) to optimize InceptionV4 model. • Applied to the CT Kidney Dataset containing diverse CT images with and without kidney stones. • Performance compared with K-CNN, DFCWTCNN, EDNN, CNN-ELM, PNN/WA. In this study, a novel methodology is proposed to combine deep learning techniques and metaheuristic optimization to enable early diagnosis of patients with kidney stone using CT images. This approach focuses on the utilization of the InceptionV4 model, a state-of-the-art deep learning architecture known for its outstanding performance in image classification tasks. To further enhance the performance of InceptionV4, a modified metaheuristic algorithm called Combined Dwarf Mongoose Optimization (CDMO) algorithm is introduced. The CDMO algorithm optimizes the arrangement and selection of hyperparameters for the InceptionV4 model. By fine-tuning the model's arrangement, the aim is to improve its accuracy and efficiency in detecting kidney stones from CT images. This optimization process ensures that the InceptionV4 model is tailored specifically to the task of kidney stone diagnosis, maximizing its diagnostic capabilities. To evaluate the performance of the proposed methodology, it is applied to the CT Kidney Dataset, which contains a diverse range of CT images with and without kidney stones. The accuracy of the methodology is compared with existing methodologies, including Kronecker product-based Convolution Neural Network (K-CNN), Combined Discrete First Chebyshev Wavelets Transform and Convolutional Neural Network (DFCWTCNN), Enhanced Deep Neural Network (EDNN), hybrid Convolutional Neural Network and Extreme Learning Machine (CNN-ELM), and Probabilistic Neural Network (PNN) and Watershed Algorithm (PNN/WA). The results demonstrate the superior performance of the proposed method compared with other methods in terms of accuracy, F1-score, and Jaccard Index. These results provide evidence of the system's dependability, efficacy, and potential for use in medical picture classification tasks. The classification of CT scans as positive or negative for kidney stones demonstrates a notable level of accuracy, reaching 98.14%. This high accuracy is attributed to the model's sensitivity of 97.74% and specificity of 96.33%. Additionally, the model has a precision rate of 86.66%, demonstrating its ability to accurately forecast positive instances. Moreover, it maintains a balanced trade-off between sensitivity and accuracy, as shown by its F1-score of 92.51%. By enabling early diagnosis of kidney stones, the proposed methodology has the potential to improve patient's outcomes and reduce healthcare costs associated with delayed diagnosis and complications. [ABSTRACT FROM AUTHOR]

“If a snake moves on a surface and if a Mongoose comes before it then a neck to neck fighting starts finally snakes dies, it happens same in case of wave communication and Network related cases that bears different level of wavelets as unseen force”.This is Snake-Mongoose Principle as inventor and author of this paper claims. To exhibit and demonstrate, an identity and some supper position result of Differential equation may be taken in to account. To justify the fact a small short of identity (k²+1)2-(k²-1)²= 4k² may be taken. Here, this Simple application of taken identity leads to a very mould and convergent logic supposed as an important tool for any wave communication dealing with wave problem under problem under different Domain.Corresponding nodes used in the communication Network, where constructive and Destructive fringes collide, finally fightingover and Mongoose leads as a result in the right hand side of the identity may clearly explains the Snake-Mongoose Principle. Identity that may be taken for the consideration to this principle is (k²+1)2-(k²-1)²= 4k², where k is any real value of real time framework that allows the network coming forward one by one representing different curves in geometrical nature and try to hamper one another during overlapping. Higher order differential equations are of great significance in this study. A final nod is supposed to be located during the insertion of different practical value in a stipulated period. [ABSTRACT FROM AUTHOR]

Development of gingival enlargement and periodontitis is described in a young dwarf mongoose. Repeated treatments resulted in gingival resection and histologic evaluation however gingival enlargement was ultimately responsive to extraction of associated teeth. In cases such as these, surgical extraction of teeth associated with severe recurrent gingival enlargement should be considered to avoid the stress and risk of repeated immobilizations. [ABSTRACT FROM AUTHOR]

• Novel approach combining deep learning and medical imaging for brain tumor diagnosis. • Gated Recurrent Unit networks optimized by an improved political optimizer. • Ability to analyze large and complex datasets. • High accuracy and early-stage diagnosis capabilities. • Improved political optimizer algorithm enhances GRU network performance. Brain tumor detection is a challenging problem that requires accurate and robust methods to identify and locate the abnormal regions in the brain images. MRI is the most commonly used imaging modality for brain tumor diagnosis, as it can provide high-resolution and contrast images of the brain tissues. However, manual analysis of MRI images by human experts is time-consuming, subjective, and prone to errors. Therefore, there is a need for automated and intelligent methods that can analyze the MRI images and detect brain tumors effectively and efficiently. In this paper, we propose a novel machine learning method that combines the advantages of the Gated Recurrent Unit (GRU) networks and the Enhanced Hybrid Dwarf Mongoose Optimization (EHDMO) algorithm for brain tumor detection. The GRU networks are a type of Recurrent Neural Network (RNN) that can process sequential data, such as natural language or time series. We employ the EHDMO algorithm to fine-tune the parameters of the GRU networks, such as the weight matrices and bias vectors for each gate and the candidate's hidden state, along with the number of hidden units in the network. The proposed method is applied to the brain tumor detection problem using the "Brain-Tumor-Progression" dataset. Results show that the proposed method achieves a sensitivity of 0.98, a specificity of 0.97, a PPV of 0.98, an NPV of 0.98, and an accuracy of 0.95. These results indicate that the proposed method can accurately and robustly detect brain tumors from MRI images. The method also is compared with some of the most recent methods, such as BrainMRNet, VGG19, ASSO, CNN/POA, and YOLOv2. The proposed method outperforms these methods in terms of sensitivity, specificity, PPV, NPV, and accuracy, demonstrating its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

• A novel methodology for kidney stone diagnosis using CNN and metaheuristic approach. • Image quality enhancement through preprocessing techniques and data augmentation. • A new introduced metaheuristic, termed Flexible Dwarf Mongoose Optimizer (FDMO). • Network weights optimizing using FDMO algorithm, resulting in improved diagnostic accuracy. • Outperforms existing techniques in diagnosing kidney stones using the "CT Kidney Dataset". This study presents a novel approach to kidney stone diagnosis using convolutional neural networks (CNN), applied to computed tomography (CT) images. The research addresses the challenge of data imbalance and protocol variation in medical imaging, which often leads to poor generalization of deep learning models. The model first uses three preprocessing techniques to enhance the quality of raw images and increase their quantity for effective CNN training. The main idea is to optimize the main arrangement of the convolutional neural network based on the proposed flexible version of dwarf mongoose optimization (FDMO) algorithm to provide a good detector model in kidney stone diagnosis. The model is then trained and tested on images from "CT Kidney Dataset", and its comparison results with some other published works demonstrates its robustness and ability to generalize. The results indicate a significant improvement in diagnostic accuracy, potentially minimizing physician-induced errors and enhancing patient care. [ABSTRACT FROM AUTHOR]

The Egyptian mongoose ( Herpestes ichneumon ) is a carnivore game species in Portugal, whose fundamental ecology remains to be fully explored. For instance, the physical features of this species have not yet been studied through a continuous wide area, as an entire country. In this work, the averages of eight biometric parameters and body mass of both genders were determined for 559 free-ranging animals from four age classes that were established by dental growth. Sexual dimorphism in body size of adult Egyptian mongoose is reported for the first time in the Portuguese territory, with males being larger than females. Differences across regions are also evidenced, with animals from south of Tagus River being larger than those from the north. Comparison with similar data available from other mongoose populations of the Mediterranean basin suggests that adult specimens from Portugal are less heavy than animals from Doñana in Spain and Israel, while males appear to be larger in body length. Statistically significant differences encountered among gender, age and region, suggest that sexual selection, prey availability, human constrains and different habitat use patterns regulate the body size of this species. [ABSTRACT FROM AUTHOR]

This article analyses the evil qualities in spiritual beings which are venerated by the Bulsa and Koma of north-ern Ghana. In particular the mongoose, in both its animal shape and as a spiritual being, is regarded as malignant and harmful. Surprisingly, the Bulsa mongoose is associated and sometimes even equated with a particular ancestress, namely the shrine own-er's mother's mother. In addition, war and bloodshed are often strongly associated with the mongoose shrine. Among the Nan-kana, it can only be acquired by men who have killed anoth-er human being. These widely differing meanings and functions can perhaps be ascribed to the deep fear of a powerful and mali-cious supernatural being and by the desire to overcome this fear through ritual activities. [ABSTRACT FROM AUTHOR]

The article discusses a study which investigated whether hepatitis E virus (HEV) RNA was present in mongooses captured on Okinawa Island in Japan. Topics discussed include the detection of genotype 3 HEV RNAs via phylogenetic analysis, the similarity of mongoose-derived HEV sequences to mongoose HEV observed on the island and the possibility that the mongoose is a reservoir animal for HEV on Okinawa.

The article discusses the results of a study aimed at examining niche relationships, in terms of habitat, diet, and time dimensions, of carnivores residing with minimal human disturbance in southern Taiwan to gather information for their conservation and management. It predicts that niche segregation and complementary resource use would be observed for sympatric carnivores. It details the three groups of carnivores including the low- to mid-elevation group of the gem-faced palm civet and crab-eating mongoose, the mid- to high-elevation group of yellow-throated marten and Siberian weasel, and the Formosan ferret badger along the elevational gradient. Findings show that elevation was the strongest factor in explaining the composition of the carnivore community in the habitat dimension.

The article presents a study of the prevalence of antimicrobial resistance in 128 Escherichia coli (E. coli) isolates in two species of invasive alien mammals (IAMs), the small Asian mongoose (SAM) and Japanese weasel (JW), in Japan. It notes the presence of the SAM on the main island of Okinawa, and the JW on a small island. Based on the results of the comparison of the isolate frequencies between these two IAMs, JW exhibited significantly higher prevalence of resistance against drugs ampicillin, chlortetracycline and nalidixic acid than SAM. It says the findings imply that some IAMs were the carrier of antimicrobial-resistant bacteria and their genes.

In the study of animal behaviour, annotation and analysis is largely done manually either directly in the field or from recordings. An emerging field, computational ethology, is challenging this approach by using machine learning to automate the process. However, the use of such methods in general is complicated by a lack of modularity, leading to high cost and long development times. At the same time, the benefits of implementing a fully automated pipeline are often minuscule. We propose online analysis as a way to gain more from automating the process, such as making it easier to ensure that equipment is properly configured and calibrated, enabling the recording equipment to follow the animals, and even enabling closed-loop experiments. In this work, we discuss the requirements and challenges for such a system and propose an implementation based on modern IT infrastructure. Finally, we demonstrate the system in case studies of bats and mongoose. As more and more methods and algorithms are developed we expect online systems to enable new experimental setups to study behaviour, leading to new insights in the field. [ABSTRACT FROM AUTHOR]