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With the rapid advancement of lifestyle and technology, human lives are becoming increasingly threatened. Accidents, exposure to dangerous substances and animal strikes are all possible threats. Human lives are increasingly being harmed as a result of attacks by wild animals. Further investigation into the cases reported revealed that such events can be detected early on. Techniques such as machine learning and deep learning will be used to solve this challenge. The upgraded VGG-16 model with deep learning-based detection is appropriate for such real-time applications because it overcomes the low accuracy and poor real-time performance of traditional detection methods and detects medium- and long-distance objects more accurately. Many organizations use various safety and security measures, particularly CCTV/video surveillance systems, to address physical security concerns. CCTV/video monitoring systems are quite good at visually detecting a range of attacks associated with suspicious behavior on the premises and in the workplace. Many have indeed begun to use automated systems such as video analytics solutions such as motion detection, object/perimeter detection, face recognition and artificial intelligence/machine learning, among others. Anomaly identification can be performed with the data collected from the CCTV cameras. The camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. Many cases have been recorded where wild animals enter public places, causing havoc and damaging lives and property. There are many cases where people have lost their lives to wild attacks. The conventional approach of sifting through images by eye can be expensive and risky. Therefore, an automated wild animal detection system is required to avoid these circumstances.

The proposed system consists of a wild animal detection module, a classifier and an alarm module, for which video frames are fed as input and the output is prediction results. Frames extracted from videos are pre-processed and then delivered to the neural network classifier as filtered frames. The classifier module categorizes the identified animal into one of the several categories. An email or WhatsApp notice is issued to the appropriate authorities or users based on the classifier outcome.

Evaluation metrics are used to assess the quality of a statistical or machine learning model. Any system will include a review of machine learning models or algorithms. A number of evaluation measures can be performed to put a model to the test. Among them are classification accuracy, logarithmic loss, confusion matrix and other metrics. The model must be evaluated using a range of evaluation metrics. This is because a model may perform well when one measurement from one evaluation metric is used but perform poorly when another measurement from another evaluation metric is used. We must utilize evaluation metrics to guarantee that the model is running correctly and optimally.

The output of conv5 3 will be of size 7*7*512 in the ImageNet VGG-16 in Figure 4, which operates on images of size 224*224*3. Therefore, the parameters of fc6 with a flattened input size of 7*7*512 and an output size of 4,096 are 4,096, 7*7*512. With reshaped parameters of dimensions 4,096*7*7*512, the comparable convolutional layer conv6 has a 7*7 kernel size and 4,096 output channels. The parameters of fc7 with an input size of 4,096 (i.e. the output size of fc6) and an output size of 4,096 are 4,096, 4,096. The input can be thought of as a one-of-a-kind image with 4,096 input channels. With reshaped parameters of dimensions 4,096*1*1*4,096, the comparable convolutional layer conv7 has a 1*1 kernel size and 4,096 output channels. It is clear that conv6 has 4,096 filters, each with dimensions 7*7*512, and conv7 has 4,096 filters, each with dimensions 1*1*4,096. These filters are numerous, large and computationally expensive. To remedy this, the authors opt to reduce both their number and the size of each filter by subsampling parameters from the converted convolutional layers. Conv6 will use 1,024 filters, each with dimensions 3*3*512. Therefore, the parameters are subsampled from 4,096*7*7*512 to 1,024*3*3*512. Conv7 will use 1,024 filters, each with dimensions 1*1*1,024. Therefore, the parameters are subsampled from 4,096*1*1*4,096 to 1,024*1*1*1,024.

This study’s aim is to introduce a high-performance sorbent for the removal of both anionic (Congo red; CR) and cationic (methylene blue; MB) dyes from aqueous solutions.

Sodium silicate is adopted as a substrate for GO and AgNPs with positive charge are used as modifiers. The synthesized nanocomposite is characterized by FTIR, FESEM, EDS, BET and XRD techniques. Then, some of the most effective parameters on the removal of CR and MB dyes such as solution pH, sorbent dose, adsorption equilibrium time, primary dye concentration and salt effect are optimized using the spectrophotometry technique.

The authors successfully achieved notable maximum adsorption capacities (Qmax) of CR and MB, which were 41.15 and 37.04 mg g⁻¹, respectively. The required equilibrium times for maximum efficiency of the developed sorbent were 10 and 15 min for CR and MB dyes, respectively. Adsorption equilibrium data present a good correlation with Langmuir isotherm, with a correlation coefficient of R2 = 0.9924 for CR and R2 = 0.9904 for MB, and kinetic studies prove that the dye adsorption process follows pseudo second-order models (CR R2 = 0.9986 and MB R2 = 0.9967).

The results showed that the proposed mechanism for the function of the developed sorbent in dye adsorption was based on physical and multilayer adsorption for both dyes onto the active sites of non-homogeneous sorbent.

The as-prepared nano-adsorbent has a high ability to remove both cationic and anionic dyes; moreover, to the high efficiency of the adsorbent, it has been tried to make its synthesis steps as simple as possible using inexpensive and available materials.

The purpose of this paper is to answer the question whether it is possible to recognise the gender of a web browser user on the basis of keystroke dynamics and mouse movements.

An experiment was organised in order to track mouse and keyboard usage using a special web browser plug-in. After collecting the data, a number of parameters describing the users’ keystrokes, mouse movements and clicks were calculated for each data sample. Then several machine learning methods were used to verify the stated research question.

The experiment showed that it is possible to recognise males and females on the basis of behavioural characteristics with an accuracy exceeding 70 per cent. The best results were obtained while using Bayesian networks.

The first limitation of the study was the restricted contextual information, i.e. neither the type of web page browsed nor the user activity was taken into account. Another is the narrow scope of the respondent group. Future work should focus on gathering data from more users covering a wider age range and should consider the context.

Automatic gender recognition could be used in profiling a user to create personalised websites or as an additional feature in automatic identification for security reasons. It might be also considered as a confirmation of declared gender in web-based surveys.

As not all users perceive personalised ads and websites as beneficial, this application requires the analysis of a user perspective to provide value to the consumer without privacy violation.

Behavioural characteristics, such as mouse movements and keystroke dynamics, have already been used for user authentication and emotion recognition, but applying these data to gender recognition is an original idea.

This study aims to investigate the efficacy of Ag@GO/Na₂SiO₃ nanocomposite in eliminating As from aqueous solutions. Employing response surface methodology, the research systematically examines the adsorption process.

Various experimental parameters including sample pH, contact time, As concentration and adsorbent dosage are optimized to enhance the As removal process.

Under optimized conditions, the initial As concentration, contact time, pH and adsorbent dosage are determined to be 32 ppm, 50 mins, 6.5 and 0.4 grams, respectively. While the projected removal of As stands at 97.6% under these conditions, practical application achieves a 93% removal rate. Pareto analysis identifies the order of significance among factors as follows: adsorbent dosage > contact time > pH > As concentration.

This study highlights the potential Ag@GO/Na₂SiO₃ as a promising adsorbent for efficiently removing industrial As from aqueous solutions, and it is likely to have a good sufficiency in the filtration of water and wastewater treatment plans to remove some chemical pollution, including paints and heavy metals.

The simplicity of the nanocomposite preparation method without the need for advanced equipment and the cheapness of the raw materials and its potential ability to remove As are the prominent advantages of this research.