Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children. Novelty of the proposed research is to design a system that is based on autism spectrum disorder detection on social media and face recognition. To identify such landmarks, deep learning techniques may be used, but they require a precise technology for extracting and producing the proper patterns of the face features. This study assists communities and psychiatrists in experimentally detecting autism based on facial features, by using an uncomplicated web application based on a deep learning system, that is, a convolutional neural network with transfer learning and the flask framework. Xception, Visual Geometry Group Network (VGG19), and NASNETMobile are the pretrained models that were used for the classification task. The dataset that was used to test these models was collected from the Kaggle platform and consisted of 2,940 face images. Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate the results of the three deep learning models. The Xception model achieved the highest accuracy result of 91%, followed by VGG19 (80%) and NASNETMobile (78%).

Plant diseases pose the greatest threat to food supply integrity, and it is a significant challenge to identify plant diseases in their earliest stages to reduce the potential for associated economic damage. Through the use of computer vision, a cutting-edge artificial intelligence is offered as a solution to this problem so that tomato leaf diseases may be classified. The proposed network is expected to provide excellent results. Transfer learning is also used to make the model efficient and cost effective. Since tomato diseases may significantly influence crop output and quality, early identification and diagnosis of these diseases are essential for successful treatment. Deep learning has shown a great deal of promise in plant disease identification, providing excellent accuracy and efficiency. In this investigation, we compared the performance of three different deep learning models—DenseNet169, ResNet50V2, and a transform model, namely ViT, with regard to diagnosing diseases affecting tomatoes. Both diseased and healthy tomato samples were included in the dataset of photos of tomato diseases used for training and testing the models. The DenseNet121 model had the best results, with a training accuracy of (99.88%) and a testing accuracy of (99.00%). This gave it the greatest overall accuracy. Both the ResNet50V2 and VIT models attained high levels of accuracy, with testing accuracies of (95.60% and 98.00%), respectively. Our results demonstrate deep learning’s potential for accurate and efficient tomato disease detection, which could aid in early disease management and ultimately improve crop yield and quality. The experimental findings show that the suggested ensemble models stand out due to the short amount of time required for training and testing as well as their exceptional classification performances. Because of this study, professionals will be able to facilitate the early diagnosis of plant diseases in a straightforward and expedient way, thereby preventing the emergence of new infections.

Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as “The Internet of Things”. WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network life-time, but also affects the availability of these resources for other tasks. To increase life-time and provide an energy-efficient WSN, here we have proposed a new scheme called “A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs”. CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network life-time by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

Wireless sensor networks (WSNs) is an infrastructure free organization of various operational devices. Due to their overwhelming characteristics, these networks are used in different applications. For WSNs, it is necessary to collect real time and precise data as critical decisions are based on these readings in different application scenarios. In WSNs, authentication of the operational devices is one the challenge issue to the research community as these networks are dynamic and self-organizing in nature. Moreover, due to the constraint oriented nature of these devices a generalized light-weight authentication scheme is needed to be developed. In this paper, a light-weight anonymous authentication techniques is presented to resolve the black-hole attack issue associated with WSNs. In this scheme, Medium Access Control (Mac) address is used to register every node in WSNs with its nearest cluster head(CH) or base station module(s). The registration process is performed in an off-line phase to ensure authenticity of both legitimate nodes and base stations in an operational network. The proposed technique resolves the black-hole attack issue as an intruder node needs to be registered with both gateway and neighbouring nodes which is not possible. Moreover, a hybrid data encryption scheme, elliptic curve integrated encryption standard (ECIES) and elliptic curve deffi-hellman problem (ECDDHP), is used to improve authenticity, confidentiality and integrity of the collected data. Simulation results show the exceptional performance of the proposed scheme against field proven techniques in terms of minimum possible end-to-end delay & communication cost, maximum average packet delivery ratio and throughput in presence of malicious node(s).

Recently, hackers intend to reproduce malicious links utilizing several ways to mislead users. They try to control victims’ machines or get their data remotely by gaining access to private information they use via cyberspace. QR codes are two-dimensional barcodes with the capacity to encode various data types and can be viewed by digital devices, such as smartphones. However, there is no approved protocol in QR code generation; therefore, QR codes might be exposed to several questionable attacks. QR code attacks might be perpetrated using barcodes, and there are some security countermeasures. Some of these solutions are restricted to malicious link detection techniques with knowledge of cryptographic methods. Therefore, this study aims to detect malicious links embedded in 1D (linear) and 2D (QR) codes. A cybercrime attack was proposed based on barcode counterfeiting that can be used to perform online attacks. A dataset of 100000 malicious and benign URLs was created via several resources, and their lexical features were obtained. Analyses were conducted to illustrate how different features and users deal with online barcode content. Several artificial intelligence models were implemented. A decision tree classifier was identified as the most suitable model for identifying malicious URLs. Our outcomes suggested that a secure artificial intelligence barcode scanner (BarAI) is recommended to detect malicious barcode links with an accuracy of 90.243%.

In Saudi Arabia, there is a scarcity of water used for agriculture and human consumption. Therefore, the aquaponic technique needs to be considered. Aquaponics is a modern, environmentally friendly agricultural technology that combines hydroponics and aquaculture into one system. However, the key to a successful aquaponic system is optimizing the stocking density for the target species. This study estimates the effect of three stocking densities—3 kg/m⁻³, 6 kg/m⁻³, and 9 kg/m⁻³—in five replicates on the growth performance and water quality of fingerling Nile tilapia (Oreochomis niloticus), as well as the yield of spinach (Spinacia oleracea) grown in the nutrient film technique (NFT) aquaponic system. As for the planting density, 36 spinach plants are planted per m⁻² for each replicate. The experiment is set up for 8 weeks. The findings reveal that the average final body weight, weight gain, specific growth rate, and survival rate of Nile tilapia were significantly higher in the 3 kg/m⁻³ treatment. It is evident that the total yield of spinach increased as the stocking density increased (p < 0.05). Most of the water quality measurements are significant, pH values range from 6.74 to 7.47, dissolved oxygen is 4.33 to 6.35, ammonia is 0.13 to 0.17 mg/L⁻¹, nitrite is 0.045 to 0.089, and nitrate is 2.44 to 3.35. Therefore, to maximize the productivity of spinach and tilapia while achieving the maximum benefit from fresh water, it can be recommended to use the stocking density of Nile tilapia fingerlings in the range of 6 kg/m⁻³ in the aquaponic system.