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To eliminate the possibilities of getting various contradicting solutions to a single problem during diagnosis, a single regular Agent oriented Approach (AoA) is replaced by Intelligent Artificial Agents that act like human and even dynamically decide in any situations known as Intelligent Searching Approach (ISA) is proposed. These agents are used to analyse the medical forums and results or findings are derived accurately than any manual approach. Multiple Agents have been used to analyse the blogs by dividing the work areas and communicating themselves using Agent Communication Language (ACL) and FIPA. The local solutions thus formed are forwarded to a global agent. This Global Agent controls all operations and makes the decision about the best solution. As the Global Agent controls all other agents, it eradicates unwanted and ineffective communication between the various local agents and hence keeping the time taken for communication at the minimum level. Based on these solutions a prioritization matrix is formed using advanced clustering techniques to create a prioritized content of suggested best solutions. Once the decision is made, the refining process runs several times recursively checking for all possible better solutions solving the input. On completion of this process, the Global Agent returns the exact result of the discussion. This process saves time rather than researching the entire blog for result data. This advanced approach lights a different way of obtaining solution keeping the time taken for discussion and intercommunication between the agents to the minimal level but not compromising on the perfection of the solution at the same time.

Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model. The proposed PPSF-BODL model includes the collection of primary data using sensing tools. Besides, z-score normalization is also utilized to transform the actual data into useful format. Besides, Chameleon Swarm Optimization (CSO) with Attention Based Bidirectional Long Short Term Memory (ABiLSTM) model is employed for detection and classification of intrusions. CSO is employed for optimal hyperparameter tuning of ABiLSTM model. At the same time, Blockchain (BC) is utilized for secure transmission of the data to cloud server. This cloud server is a decentralized, distributed, and open digital ledger that is employed to store the transactions in different methods. A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSF-BODL model over recent approaches with a maximum accuracy of 97.46%.

In medical image processing, Brain tumor segmentation plays an important role. Early detection of these tumors is highly required to give Treatment of patients. The patient’s life chances are improved by the early detection of it. The process of diagnosing the brain tumoursby the physicians is normally carried out using a manual way of segmentation. It is time consuming and a difficult one. To solve these problems, Enhanced Convolutional Neural Networks (ECNN) is proposed with loss function optimization by BAT algorithm for automatic segmentation method. The primary aim is to present optimization based MRIs image segmentation. Small kernels allow the design in a deep architecture. It has a positive consequence with respect to overfitting provided the lesser weights are assigned to the network. Skull stripping and image enhancement algorithms are used for pre-processing. The experimental result shows the better performance while comparing with the existing methods. The compared parameters are precision, recall and accuracy. In future, different selecting schemes can be adopted to improve the accuracy.

The Internet of Things (IoT) is a rapidly growing technology that has been generating increasing amounts of traffic from multiple devices. However, this growth in traffic has also created vulnerabilities that need to be addressed. To identify attacking traffic while preserving data, it is important to quickly process intrusive data. Federated learning is a popular solution for decentralized training that preserves data, but it can also be susceptible to federated poisoning attacks caused by malicious clients. This work proposes a clustering-based client selection strategy to identify malicious clients based on their run time, followed by a trigger-set-based encryption mechanism that verifies the authenticity of the clients. This approach allows unreliable clients with plain text-based gradients to be ignored by the global model. The methodology was evaluated using the IoT23 dataset, and its efficiency, robustness, false alarms, and ability to handle some of the poisoning attacks that occur due to tuning and pruning were verified. The LeNet and DeepCtrl algorithms were used to determine detection accuracy, and after the implementation of a watermarking strategy, the detection accuracy improved significantly. For the DeepCtrl classifier, the detection accuracy improved from 89.90 to 99.8%, while for the LeNet classifier, it improved from 86.21 to 96.54%. This proposed methodology can be a useful tool for identifying attacking traffic and improving the security of IoT networks.

One such prevalent kind of cancer among women is cervical cancer (CC). Fatality rates and incidence are progressively increasing, mainly in developing countries, due to a lack of experienced specialists, inadequate public awareness, and limited screening facilities. Nevertheless, CC cells exhibit composite textural features, and smaller changes among dissimilar cell subcategories result in greater challenges for the higher-accuracy screening of CC. This systematic analysis aims to assess the predictive value of artificial intelligence (AI) technologies for diagnosing, screening, and predicting CC and precancerous lesions. Deep learning (DL) and AI generally have a positive impact on computer-aided clinical diagnosis, particularly with the increasing accessibility of larger amounts of medical data that can aid AI methods in achieving high performance on various medical tasks. In this paper, a Fusion of Advanced Feature Reduction and Deep Representation Learning Approaches for Cervical Precancerous Lesion Classification (FAFRDRL-CPLC) technique using biomedical image analysis is proposed. The primary purpose of the FAFRDRL-CPLC technique is to serve as a valuable tool for assisting clinicians in the initial study and treatment planning of cervical precancerous lesions. Initially, the FAFRDRL-CPLC approach applies an anisotropic diffusion filtering (ADF) method for pre-processing to reduce noise while preserving crucial edges and lesion details. Furthermore, the fusion of advanced feature reduction models, such as the maximally scalable vision transformer (MaxViT-v2), the simple framework for contrastive learning of visual representations (SimCLR), and the Twins-spatially separable vision transformer (Twins-SVT) models, is employed to capture diverse and complementary representations from the pre-processed images. Finally, the stacked auto-encoder (SAE) classifier is utilized for the precancerous lesion detection process. The FAFRDRL-CPLC method is examined through experimentation using the Malhari dataset. The comparison study of the FAFRDRL-CPLC method demonstrated a superior accuracy value of 98.62% over existing approaches.

From the past few decade, many manufacturing industries are using heat exchangers for reducing the energy consumption and hence reducing the fuel costs. Most widely used types of heat exchangers in industries are Double Pipe Heat Exchangers and Shell & Tube Heat Exchangers. It is recently that industry people and researchers are becoming more aware of the advantage of using Spiral Heat Exchangers for heat transfer between two different fluids.A Spiral Heat Exchanger is formedby a coiled sheet arrangement with two channels coiled one around the other. The distance between the sheets is kept constant to maintain the area of cross section through out the spiral path of the channels. In this work, flow pattern and heat transfer in a Spiral Heat Exchanger are analyzed using a couterflow model geometry. The results obtained for the fluid flow and heat transfer gives an idea about how we can optimize the flow rate of the fluids thus increasing the efficiency of the heat exchanger.

The main objective of image de-noising is to remove the noise present in the noisy image. Like that, main objective of proposed methodology is to restore the impulse noised standard test image based on hybrid filter, fuzzy logic system and genetic algorithm. The proposed HFGOA method consists of three steps. In the first step noisy image is de-noised using mean filter and median filter, individually. In the second step the difference vector is calculated using two filters output then it is given as input to fuzzy logic system. Fuzzy rules were generated from the difference vector value using triangular membership function. In the third step using genetic optimization algorithm optimal rule will be selected. Fitness value (PSNR) calculated for each population. The new population was repeatedly created using genetic operator until getting best fitness value. The performance of the proposed method was measured using PSNR value. HFGOA method is tested over standard test image (lena image) for different percentage of salt and pepper noise. The Experimental results of HFGOA method is compared with results of different exiting filters. An experimental result shows the HFGOA method rectifies the drawbacks of exiting filters and increases the visual quality of the image by increasing the PSNR value.

An embedded system has been designed to make the journey of the passengers inside a vehicle safe and secure with using the Internet of things. IOT is almost an adjustable technology that is capable of providing relevant information about its own operation. It provides the necessary accident information to the vehicle. In today's world safety and security plays an important role, Vehicles are important in our fast-paced society, hence we move towards intelligent security systems while travelling. There are too many warning systems incorporated in the vehicles to alert drivers about various factors like eye blink, driver's head movement, detecting alcohol consumption. This wireless system monitors the outside environment of the vehicle. This model consists of a node MCU, a motor, an ultrasonic sensor, a PIC controller and IOT. A PIC microcontroller chip is a Peripheral Interface Controller. Distance of vehicles are determined by using an ultrasonic sensor, and the distance is calculated over a simple period of time that detects distance of obstacles outside the vehicle through ultrasonic sensor that continuously sends signals, If any obstacles spotted then the motor turns slow that indicates the speed of a vehicle. All these distances are stored on to the server using the node MCU module. This accident information can be seen through website or application that will enhance the security of the system.

Wireless sensor network (WSN) is employed in variety of applications ranging from agriculture to military. WSN is vulnerable to various security attacks, in which jamming attacks obstruct and disturb the exchange of information between sensor nodes in WSN by transmitting signals to jam legitimate transmission to cause a denial of service. Hence, it is essential to secure the sensor networks from jamming attacks. In this paper, two approaches: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS)-based jamming detection system are proposed for detecting the presence of jamming by computing two jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. FIS approach is based on Takagi–Sugeno fuzzy logic which optimizes the jamming detection metrics. ANFIS approach combines fuzzy logic and learning ability of the neural network to optimize the metrics for detecting various types of jamming. The proposed approaches are compared with existing system and themselves. The simulation result shows that the proposed ANFIS approach detects the jamming attacks as high as true detection ratio.