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Autonomous driving has passed the point of being called the biggest step, as the smart car revolution is already taking shape around the world. Self-driving cars are relevant if not prevalent and the biggest obstacles to reach the mass adoption are customer acceptance, cost, infrastructure, and the reliance on several onerous algorithms that include perception, lane marking detection, path planning, and variation in pathways. This study tackled the mentioned problems with a straightforward and cost-effective solution, using end-to-end learning and replacing the numerous sensors with a camera and commandeering just the forward, backward, left, and right controls. In this research, authors have used the most popular method of deep learning that is convolutional neural network to train the collected data on the VGG16 model. Later these have optimized directly by the proposed system with cropping each unnecessary image and mapping pixels from a single front-facing camera to direct steering instructions. It has been observed from the experimental work that the proposed model has given a better result than the existing work, that is, increase in the accuracy from 88% (Udacity training dataset) to 98% (proposed). This model is suitable for industrial use and robust in real time scenarios, therefore can be applied in modern industrialized systems.

The recent advancements in ubiquitous sensing powered by Wireless Computing Technologies (WCT) and Cloud Computing Services (CCS) have introduced a new thinking ability amongst researchers and healthcare professionals for building secure and connected healthcare systems. The integration of Internet of Things (IoT) in healthcare services further brings in several challenges with it, mainly including encrypted communication through vulnerable wireless medium, authentication and access control algorithms and ownership transfer schemes (important patient information). Major concern of such giant connected systems lies in creating the data handling strategies which is collected from the billions of heterogeneous devices distributed across the hospital network. Besides, the resource constrained nature of IoT would make these goals difficult to achieve. Motivated by aforementioned deliberations, this paper introduces a novel approach in designing a security framework for edge-computing based connected healthcare systems. An efficient, multi-factor access control and ownership transfer mechanism for edge-computing based futuristic healthcare applications is the core of proposed framework. Data scalability is achieved by employing distributed approach for clustering techniques that analyze and aggregate voluminous data acquired from heterogeneous devices individually before it transits the to the cloud. Moreover, data/device ownership transfer scheme is considered to be the first time in its kind. During ownership transfer phase, medical server facilitates user to transfer the patient information/ device ownership rights to the other registered users. In order to avoid the existing mistakes, we propose a formal and informal security analysis, that ensures the resistance towards most common IoT attacks such as insider attack, denial of distributed service (DDoS) attack and traceability attacks.

IoT-enabled medical services are nowadays gaining the major ground in modern lifestyle management strategies due to the increasing trends of advancements in the design and development of healthcare devices. A growing number of necessities in creating connected environments for effective treatment of a chronic disorder are further adding the objectivity in modern healthcare services. The IoT environments in healthcare paradigm consist of mainly the data acquisition system that captures the data from patient body and data aggregators or the data commuters that transform the physical layer data into the cloud layer data for analysis. This data is made accessible to medical professionals for further analysis and disease prediction from the cloud space. Data in the cloud storage needs to be protected by various encryption schemes from the unauthorized accesses. It is critically important to offer the data and device services timely during emergency situations in healthcare scenarios. In this view, authors designed a lightweight access control framework that enables the users to access encrypted data and devices in two modes: attribute oriented access and emergency break-glass access. Normally, a policy-based, multilayered authentication technique is used in encrypting data and a medical professional who satisfies the policy guidelines can decrypt the data through set of attributes. Whereas, during emergency situations the need of break-glass access arises so as to bypass the access policies and stringent security walls in order to timely access the data by rescue workers. The proposed framework is lightweight and fast since there are minimal calculations which are required at device and storage level thus by forming low computational and latency complexities. The proposed model is proved secure against the standard security models, and the efficiency is demonstrated through numerous experimentations.

These days’ security plays the greatest role in order to provide safe platforms and surveillance becomes the essential need to provide accurate results in case of security breach. Gait recognition is a biometric technique that does not need human intervention. Through this technique, human can be uniquely identified. Gait is defined as the way human walks (human locomotion) and this can be used as biometric identity because the manner in which every person walks can uniquely categorize person. But there are many challenges like variation in viewpoints, clothing variations, carrying conditions and so on. A novel approach using deep learning is proposed to address this challenge. To address these limits, we give an idea of having multi-view gait-based recognition system, to provide robust system that is capable of handling one camera and subject walking on different angles from 0° to 180° of view. To achieve the results 3D CNN-based model is used in order to obtain spatio-temporal features. Also, the computation capability of algorithm has been enhanced via transfer learning mechanism. To carry out the experiments, OU-ISIR and CASIA-B dataset are used. Once these features are obtained, these are passed into LSTM network to obtain the long-term dependencies from gait sequence. Network is trained and tested and experiment result shows the proposed technique in this paper outperforms the state of art techniques.

E-nose is a self-controlled smoke detector developed using Arduino microcontroller and sensors. It is capable of sensing the smell in the ambiance via gas sensors and programmed to alert the smokers giving red signal with buzzer. Smoke detection algorithm facilitates the intelligent warning to the cigarette smokers. Nowadays, smokers’ count is increasing with a high-speed raising health and moral issues to the people especially in case of passive smoking at public places. Today “No Smoking” rules are only written, but to make these rules being followed this work would be a milestone. To implement e-Nose, a cigarette smoke detector with red/green LED and buzzer using Arduino uno board R3 is developed. MQ-2 gas sensor is used to detect cigarette smoke and combination of buzzer and LEDs is used to warn smokers. This work demonstrates the development of e-Nose and evaluation of the performance of developed product. In nutshell, this e-Nose which is cigarette smoke detector is designed to achieve high degree of awareness among public and to reduce the smokers’ count especially at public places. We detect the smoke through e-Nose and upload on the cloud. The cloud analysis is done using various machine learning techniques to categorize the smoke with pollution data. And result shows smoking percentage.

In the age of digitalization, Internet-based applications are gaining popularity at an exponential rate. Today, everyone wants to make their lives easier and devices smarter. In the age of automation, most of the devices we interact with on a day-to-day basis, for example, air conditioners, refrigerators, etc. are made increasingly intelligent to simplify our lives and make it comfortable. Using the principles of IoT and AI, we can create home automation devices such as automatic security devices and e-meters that make our homes smarter and more secure. Keeping a track of how much electricity is consumed per household becomes imperative seeing the rate at which global warming is increasing. Gone are the days where users had to go to meter reading room and take down readings. By employing IoT concepts, we can simplify this tedious process and record the reading over cloud for easy accessibility. The major advantage of digitalizing the process is that the user has the facility to view his consumption remotely, i.e., anywhere in the world. This also enables the user to keep a log of how many units of electricity a device is consuming and with how much amount the user is being charged fairly or not.

The weather can have great impact on lives. Weather changes can influence wide range of human activities and affect agriculture and transportation. The main aim of this paper is to monitor and report weather conditions so that one is informed beforehand and necessary actions can be taken to reduce the damage by any calamity by forecasting it. Here we are using various sensors in order to collect the data and previous data is used in order to train the system and with current data collection we do the prediction. We will be analyzing temperature, pollutants, humidity and pressure and will predict the weather. Existing models are expensive in contrast to ours and hence it will make monitoring local area feasible as it will be cheaper.

The Classification of Humanoid locomotion is a troublesome exercise because of nonlinearity associate with gait. The high dimension feature vector requires a high computational cost. The classification using the different machine learning technique leads for over fitting and under fitting. To select the correct feature is also the difficult task. The hand craft feature selection machine learning techniques performed poor. We have used the deep learning technique to get the trained feature and then classification we have used deep belief network-based deep learning. Classification is utilized to see Gait pattern of different person and any upcoming disease can be detected earlier. So in this paper we first selected the feature and identify the principle feature then we classify gait data and use different machine learning technique (ANN, SVM, KNN, and Classifier fusion) and performance comparison is shown. Experimental result on real time datasets propose method is better than previous method as far as humanoid locomotion classification is concerned.

Push recovery is prime ability that is essential to be incorporated in the process of developing a robust humanoid robot to support bipedalism. In real environment it is very essential for humanoid robot to maintain balance. In this paper we are generating a control system and push recovery controller for humanoid robot walking. We apply different kind of pushes to humanoid robot and the algorithm that can bring a change in the walking stage to sustain walking. The simulation is done in 3D environment using Webots. This paper describes techniques for feature selection to foreshow push recovery for hip, ankle and knee joint. We train the system by K-Mean algorithm and testing is done on crouch data and tested results are reported. Random push data of humanoid robot is collected and classified to see whether push lie in safer region and then tested on given proposed system.

Push recovery is prime ability that is essential to be incorporated in the process of developing a robust humanoid robot to support bipedalism. In real environment it is very essential for humanoid robot to maintain balance. In this paper we are generating a control system and push recovery controller for humanoid robot walking. We apply different kind of pushes to humanoid robot and the algorithm that can bring a change in the walking stage to sustain walking. The simulation is done in 3D environment using Webots. This paper describes techniques for feature selection to foreshow push recovery for hip, ankle and knee joint. We train the system by K-Mean algorithm and testing is done on crouch data and tested results are reported. Random push data of humanoid robot is collected and classified to see whether push lie in safer region and then tested on given proposed system.

Clustering is done in order to group the entities which are alike in one group so that grouping of more similar objects can be done. The objects placed in one group are known as clusters. In this paper we are using clustering in order to identify the human locomotion and categories the dataset making clusters. We are using two clustering techniques i.e. SOM and K-mean. So we first selected the feature and identify the principle feature then we cluster gait data and use different machine learning technique (K-mean and SOM) and performance comparison is shown. Experimental result on real time datasets propose method is better than previous method as far as humanoid locomotion classification is concerned.

The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and selfdescriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour.