Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Individuals with hearing impairments may struggle with integrating into society because the general population does not understand sign language. Consequently, this can lead to isolation and exclusion from social and professional opportunities. To address this issue, this paper proposes a system for the real-time interpretation of American Sign Language (ASL) using computer vision technology. This system uses a normal webcam to detect and interpret 26 letters of the English alphabet and three auxiliary signs. To achieve this goal, the pre-trained lightweight single-shot multibox detection network model, from the TensorFlow object detection application programming interface (API), SSD-MobileNet was used. After the training phase of the proposed model with a personally collected dataset, the obtained results in testing are promising, with a precision of 82.8% and a recall of 85%. The proposed system represents a forward step in sign language translation. Furthermore, it can be adapted to interpret other sign languages.

Accurate weed detection remains crucial for ultra-localised control in robotic solutions, simulating manual weeding in agriculture. Although many studies have been conducted in the field of weed detection using machine learning, most have focused mainly on direct detection, which can present challenges in the face of weed diversity. In this study, we propose an integrated approach based on vegetation/soil segmentation, followed by discrimination between crops and weeds using an object detector. Segmentation models such as UNet, FPN, and LinkNet have been thoroughly trained to discriminate efficiently between vegetation and soil. The results obtained are promising, with the trained models being able to generate binary images (masks) with an accuracy (Jaccard and Dice similarity indices) of over 89%. In addition, the execution speed reached 217 frames per second (fps). The integration of the localisation results from the detection model with the segmented images provides a robust method for accurately determining the position of weeds in the agricultural context, opening up new prospects for automated, targeted weed control solutions.

India faces a significant challenge with rising unemployment, exacerbated by a lack of awareness among students regarding career prospects. The gap between current undergraduate and postgraduate courses and market demands adds to the issue. This study addresses the disparity by utilising machine learning models, including multiple linear regression, KNN, random forest, and XGBoost. By predicting future job trends, admissions, and placements, the integrated system aims to guide students in making informed course choices. Additionally, the research addresses the shortage of relevant datasets by creating a comprehensive dataset encompassing India's state-wise admission and placement, Gujarat District-level data, state-wise start-up information, and educational institutional analysis, compiled from diverse sources.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

We improve on an existing instance segmentation model with a probabilistic extension to the encoded neighbourhood branch model (Bailoni et al., 2020) - we call it multiple outputs encoded neighbourhood branch (mENB) model. The mENB predicts - for each voxel in a 3D volume, a distribution of central masks, where each mask represents affinities of its central voxel and the neighbouring voxels within the mask. When post-processed using a graph partition algorithm, these masks collectively delineates the boundaries of each instance of the target class within the input volume. Our algorithm is efficient - due to active learning, more accurate and it is robust to Gaussian noise and model weights perturbations. We conducted two experiments: 1) the first experiment compared mask predictions of our technique against the baseline (Bailoni et al., 2020) using the CREMI 2016 neuron segmentation dataset and the results showed a more accurate masks predictions with uncertainty quantification; 2) in the second experiment, we tested segmented instances against the popular proposal-based mask-RCNN and the results showed that our technique yields better precision and intersection over union.

Intrusion detection systems (IDS) are the basic security line for any network. Internet of things (IoT) networks have been in trend and usage. It thus raises security challenges in IoT networks, and thus there is a requirement for IDS for IoT. There has been promising research on artificial immune system (AIS) algorithms for intrusion detection. In this paper, AIS algorithms, namely negative selection algorithm (NSA) and clonal selection algorithm (CSA), for intrusion detection in IoT are compared. These algorithms are evaluated using Python on a popular public BoT-IoT-L01 dataset. Various performance metrics like detection rate, classification accuracy, false-positive rate, and false-negative rate are used for comparison. The results of the research show that the CSA is better than the NSA-based intrusion detection for IoT in terms of accuracy. CSA achieved an overall detection rate of 91% in comparison to NSA's, with 79%. It was found that NSA was more efficient than CSA in detecting rare kinds of attacks. The research findings indicate that AIS can be a powerful tool for IoT-based IDS. The selection of an appropriate AIS algorithm for IDS in IoT depends on the specific requirements and characteristics of the IoT network.