Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data.

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Nowadays, owing to the openness of transmission medium, wireless sensor networks (WSNs) suffer from a variety of attacks, together with DoS attacks, tampering attacks, sinkhole attacks, and so on. Therefore, an effectual system is necessary for recognizing the intrusions in WSN. This paper aims to set up a novel intrusion detection system (IDS) via a deep learning model. Initially, optimal cluster head (CH) is selected among the sensor nodes, from which the sensor nodes that have high energy will be prioritized to act as CH. In this proposed work, the CH selection is evaluated optimally by not only considering the energy parameter, further under the constraints like delay and distance. For optimal selection, a novel approach named as self‐improved sea lion optimization (SI‐SLnO) model is introduced in this work. As per the proposed strategy, the trust of CH and nodes is evaluated based on a multidimensional two‐tier hierarchical trust model by considering content trust, honesty trust, and interactive trust. Finally, the deep learning‐based intrusion detection takes place via optimized neural network (NN), where the training is done by the proposed SI‐SLnO algorithm via the optimal weight tuning process. At last, the supremacy of the developed approach is examined via evaluation over numerous extant techniques.

The problem of smart agriculture has been well studied and the security in Wireless Sensor Networks (WSN) has been analyzed in detail. There are a number of approaches discussed in the literature to support the growth of agriculture by considering different factors. But still the performance of plant management is not up to the expected level in terms of plant management and security concern. To handle these issues, an efficient multi view image based plant management technique which consider color and contrast features to obtain the features of fluid, plant, climate to compute different supportive measures like Fluid Specific Growth Support (FSGS), Plant Specific Growth Support (PSGS) and Climate Specific Growth Support (CSGS) measures to compute the value of Plant Growth Measure (PGM) and Crop Yield Measure (CYM). Also, using the same support measures, the presence of diseased plants is identified and fertilizers are regulated accordingly. Similarly, the wireless sensor network has been used as monitoring environment which has several routes to monitor different locations of agriculture lands. The presences of different routes are monitored for the transmission of different agriculture data. To handle the security issues, a low rate attack detection scheme is presented which finds the routes and for each route the method computes Service centric Legitimate Support (SCLS) to find low rate attacks. Similarly, the data security by controlling different smart devices in agriculture lands is enforced by using service centric data encryption (SCDE) scheme which uses different encryption scheme and keys to encrypt the data being used for controlling the devices of agricultural lands. The proposed method improves the performance of smart agriculture and improves the data security with higher low rate detection accuracy.

Background: In the era of Internet of Things (IoT) automation of home and security systems was becoming remarkably easy. Many research works have been also conducted to bring the most convenient way of automating the home appliances and security systems but insignificant research has been carried out in providing the most comprehensive and autonomous or self-controlled home automation. The aim of this paper is to provide a comprehensive solution to mitigate the existing need. End users can interact with household appliances in a variety of means irrespective of platforms used and their geographical location through smartphone application developed. Using their speech as input users can also operate the electrical appliances via voice commands. In addition to the mobile application and voice command, by integrating some cloud services an attractive web interface would be provided as another alternative for better data presentation and analysis. The autonomous feature would allow the home to monitor and control its environment by itself through installed sensors, sharing data into one another and taking a relevant measurement without a need for human intervention Method: In order to achieve those performances the work will deploy an MQTT (Message Queuing Telemetry Transport) messaging protocol to enable the modules easily communicates one another. Results: Multiple alternative means of controlling and interacting with home appliances was provided. Both autonomous mode and safe mode features would make the system to behave and run autonomously. Conclusion: The paper also embraces the presentation for an application of multiway, cross-platform and user oriented home automation and security system as well as convenient means of controlling and monitoring household electrical appliances.