The brain tumor is a cluster of the abnormal tissues, and it is essential to categorize brain tumors for treatment using Magnetic Resonance Imaging (MRI). The segmentation of tumors from brain MRI is understood to be complicated and also crucial tasks. It can be further use in surgery, medical preparation, and assessments. In addition to this, the brain MRI classification is also essential. The enhancement of machine learning and technology will aid radiologists in diagnosing tumors without taking invasive steps. In this paper, the method to detect a brain tumor and classification has been present. Brain tumor detection processes through pre-processing, skull stripping, and tumor segmentation. It is employing a thresholding method followed by morphological operations. The number of training image influences the feature extracted by the CNN, then CNN models overfit after some epoch. Hence, deep learning CNN with transfer learning techniques has evolved. The tumorous brain MRI is classified using CNN based AlexNet architecture. Further, the malignant brain tumor is classified using GooLeNet transfer learning architecture. The performance of this approach is evaluated by precision, recall, F-measure, and accuracy metrics.

Wireless Sensor Network is extensively utilized in numerous places, such as protection surveillance. In Wireless Sensor Network, sensing unit networks are particular arbitrarily in addition to likewise in-network relying upon the technique is used to extend the network. As sensing unit nodes make use of strength from batteries for noticing the facts in addition to forwarding data, it uses the capability for those answers. The sizable troubles in cordless networks include power optimization, protection, directing, and project type. In this paper, current procedures in escaping power utilization of Wireless Sensor Network in addition to distinctive protocols and also Methods are researched. Additionally, destiny research have a look at on strength efficiency in Wireless Sensor Network putting forward new terms as well as targets for in addition examination is mentioned. Depiction of optimizing strategies like particle swarm optimization set of rules as well as ant swarm optimization Formula is already possible for lowering the electricity loss and complements the life of sensor community but those strategies take in greater time. This paper gives surveying extraordinary different optimization techniques below the multi-objective facet that takes region in tradeoffs. Information extracting in sensing unit networks is the technique of obtaining software-enabled plans in addition to patterns with gratifying accuracy from a constant, speedy, in addition to probable non-ended flow of facts streams from sensor networks. Various boundaries in preceding optimization Algorithms and suggesting a great deal better treatment through applying Data extracting Strategies for Wireless Sensing unit Networks is carried out.

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To process huge volume of data efficiently in video surveillance system, it is essential to find out high efficient video retrieval technique and advanced video compression techniques. In this paper, a video coding scheme based on hybrid DWT-DCT transform, quantization and construction of minimum redundancy code using Huffman coding is introduced. The proposed method uses the motion vectors, found from estimation using adaptive rood pattern search and is compensated globally. The hybrid DWT-DCT transform exploits the properties of both the DWT and DCT techniques and provides a better compression. The hybrid compressed frame is quantized and entropy coded with Huffman coding for gene rated bit streams are transmitted to the decoder. The algorithm achieves the size of the compressed frame saving by about 98% in its storage space.

Presently, due to the establishment of a sensor network, residual buildings in urban areas are being converted into smart buildings. Many sensors are deployed in various buildings to perform different functions, such as water quality monitoring and temperature monitoring. However, the major concern faced in smart building Wireless Sensor Networks (WSNs) is energy depletion and security threats. Many researchers have attempted to solve these issues by various authors in different applications of WSNs. However, limited research has been conducted on smart buildings. Thus, the present research is focused on designing an energy-efficient and secure routing protocol for smart building WSNs. The process in the proposed framework is carried out in two stages. The first stage is the design of the optimal routing protocol based on the grid-clustering approach. In the grid-based model, a grid organizer was selected based on the sailfish optimization algorithm. Subsequently, a fuzzy expert system is used to select the relay node to reach the shortest path for data transmission. The second stage involves designing a trust model for secure data transmission using the two-fish algorithm. A simulation study of the proposed framework was conducted to evaluate its performance. Some metrics, such as the packet delivery ratio, end-end delay, and average residual energy, were calculated for the proposed model. The average residual energy for the proposed framework was 96%, which demonstrates the effectiveness of the proposed routing design.