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With each passing day resolutions of still image/video cameras are on the rise. This amelioration in resolutions has the potential to extract useful information on the view opposite the photographed subjects from their reflecting parts. Especially important is the idea to capture images formed on the eyes of photographed people and animals. The motivation behind this research is to explore the forensic importance of the images/videos to especially analyze the reflections of the background of the camera. This analysis may include extraction/ detection/recognition of the objects in front of the subjects but on the back of the camera. In the national context such videos/photographs are not rare and, specifically speaking, an abductee’s video footage at a good resolution may give some important clues to the identity of the person who kidnapped him/her. Our aim would be to extract visual information formed in human eyes from still images as well as from video clips. After extraction, our next task would be to recognize the extracted visual information. Initially our experiments would be limited on characters’ extraction and recognition, including characters of different styles and font sizes (computerized) as well as hand written. Although varieties of Optical Character Recognition (OCR) tools are available for characters’ extraction and recognition but, the problem is that they only provide results for clear images (zoomed).

SYN flooding is one of the most challenging problems that many networks applications face, particularly those that are security-related. Disrupting a server's daily function and assigning it to other tasks leaves it a constantly busy server that processes little usable data. In this research, a comprehensive INDIGSOL approach is demonstrated that not only detects SYN flooding but also prevents the attacker(s) from making such attempts in the future. The designed approach has four modules such as node registration and validation, packet capturing, dynamic check system, and hook activation. The approach is further checked and compared with some state-of-the-art baselines on various parameters like detection time, response/processing time, and number of malicious packets detection. It is observed that INDIGSOL performed better than other baselines with an average accuracy of 99% malicious packet detection in six scenarios along with 13.4% faster detection time and 11.2% faster response/processing time. Overall, the provided solution is scalable, robust, and highly accurate that prevents SYN flooding in a timely manner.

Social network analysis has been increasingly employed to study patterns in diverse areas of disciplines such as crowd management, air passenger and freight transportation, business modelling and analysis, online social movements and bioinformatics. Over the years, human disease networks have been studied to analyze Human Disease, Genotype, and Phenotype networks. This study explores human Disease Network based on their symptoms by employing different social network analysis such as centrality measures of network, community detection, overlapping communities. We studied relationships of symptoms with diseases on meso-level in order to detect comorbidity pattern of communities in disease network. This help us to understand the underlying patterns of diseases based on symptoms and find out that how different disease communities are correlated by detecting overlapping communities.

The data mining methods have been extensively used in the process of decision making. The popularity of data mining methods is due to availability of high speed algorithms, processing and storage power of computers. The effective use of data mining methods help in mining datasets and taking better decisions. The data need to be preprocessed before applying data mining methods. Some datasets require little preparation like dealing with missing and redundant instances while some high-dimensional datasets require strong processing like dimensionality reduction. One of the techniques used for dimensionality reduction is feature selection. This study uses graph based centrality measure for feature selection. Graph based centrality measures are used for ranking features which is used for removing irrelevant attributes. After comparison of results with other approaches, it has been found that the proposed approach results in reduction of feature space without compromising accuracy. The results also shows that proposed approach performs better than some other feature selection approaches not only in terms of accuracy but also on the basis of larger reduction in feature space.

Realizing secure and private communications on the Internet of Things (IoT) is challenging, primarily due to IoT's projected vast scale and extensive deployment. Recent efforts have explored the use of blockchain in decentralized protection and privacy supported. Such solutions, however, are highly demanding in terms of computation and time requirements, barring these solutions from the majority of IoT applications. Specifically, in this paper, we introduce a resource-efficient, blockchain-based solution for secure and private IoT. The solution is made possible through novel exploitation of computational resources in a typical IoT environment (e.g., smart homes), along with the use of an instance of Deep Extreme Learning Machine (DELM). In this proposed approach, the Smart Home Architecture based in Blockchain is protected by carefully evaluating its reliability in regard to the essential security aims of privacy, integrity, and accessibility. In addition, we present simulation results to emphasize that the overheads created by our method (in terms of distribution, processing time, and energy consumption) are marginal related to their protection and privacy benefits.

Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.

In the modern era business intelligence (BI) has a pivotal role in articulating a strategy and taking correct measures based on data. Business intelligence plays a pivotal role in an inevitable decision support system that enables the enterprise to perform analysis on data and throughout the process of business. Machine learning predicts the forecasting of future demands of the enterprises. Demand forecasting is one of the main decision-making tasks of enterprise. For demand forecasting first raw sales data is collected from the market, then according to data, the future sale/product demands are forecasted. This prediction is based on collected data that compiles through different sources. The machine learning engine executes data from different modules and determines the weekly, monthly, and quarterly demands of goods/commodities. In demand forecasting, its perfect accuracy is non-compromising, the more accurate system model is more efficient. Furthermore, we test the efficiency by comparing the predicted data with actual data and determine the percentage error. Simulation results show that after applying the purposed solution on real-time organization data, we get up to 92.38 % accuracies for the store in terms of intelligent demand forecasting.

Proxy signcryption is essential security primitive for emerging secure communication such as e-business, mobile agents, online voting, contract signing, and online auction. It combines the functionality of a proxy signature and encryption to achieve basic security features maintaining a low computational and communicational cost. Ming proposed Proxy Signcryption (PSC) scheme in the standard computational model, claimed it to be secured against: (1) Indistinguishable Chosen Ciphertext Attack (IND-CCA) under the Decisional Bi-linear Diffie-Hellman (DBDH) assumption (2) Existentially Unforgeable Chosen Message Attack (EUF-CMA) under the Computational Diffie Hellman (CDH) assumption. This paper first provides a security analysis to check the correctness and validity of the said PSC scheme. Furthermore, it proves PSC is vulnerable to the launched cryptanalysis attacks. It is established that the PSC is neither semantically secured against IND-CCA nor existentially secured against EUF-CMA in its defined security model. Secondly, we propose an improved new proxy signcryption scheme (N-PSC) based on Elliptic Curve Cryptosystem (ECC) without bi-linear pairing secure against IND-CCA and EUF-CMA for Type-1 adversary A1 in the standard computational model. It is also proved that the new proposed N-PSC scheme achieves an extra security property of judge verification in case of signature dispute between the proxy correspondents, as well as it outperforms the existing states of the art schemes including the Ming scheme in terms of cost efficiency which makes the new proposed scheme suitable for scarce resources constraint proxy enabled communication applications.

To recognize the basis of disease, it is essential to determine its underlying genes. Understanding the association between underlying genes and genetic disease is a fundamental problem regarding human health. Identification and association of genes with the disease require time consuming and expensive experimentations of a great number of potential candidate genes. Therefore, the alternative inexpensive and rapid computational methods have been proposed that can identify the candidate gene associated with a disease. Most of these methods use phenotypic similarities due to the fact that genes causing same or similar diseases have less variation in their sequence or network properties of protein-protein interactions based on-premises that genes lie closer in protein interaction network that causes the similar or same disease. However, these methods use only basic network properties or topological features and gene sequence information or biological features as a prior knowledge for identification of gene-disease association, which restricts the identification process to a single gene-disease association. In this study, we propose and analyze some novel computational methods for the identification of genes associated with diseases. Some advance topological and biological features that are overlooked currently are introducing for identifying candidate genes. We evaluate different computational methods on disease-gene association data from DisGeNET in a 10-fold cross-validation mode based on TP rate, FP rate, precision, recall, F-measure, and ROC curve evaluation parameters. The results reveal that various computational methods with advanced feature set outperform previous state-of-the-art techniques by achieving precision up to 93.8%, recall up to 93.1%, and F- measure up to 92.9%. Significantly, we apply our methods to study four major diseases: Thalassemia, Diabetes, Malaria, and Asthma. Simulation results show that the proposed Deep Extreme Learning Machine (DELM) gives more accurate results as compared to previously published approaches.

A Long-Range Wide Area Network (LoRaWAN) is one of the most efficient technologies and is widely adopted for the Internet of Things (IoT) applications. The IoT consists of massive End Devices (EDs) deployed over large geographical areas, forming a large environment. LoRaWAN uses an Adaptive Data Rate (ADR), targeting static EDs. However, the ADR is affected when the channel conditions between ED and Gateway (GW) are unstable due to shadowing, fading, and mobility. Such a condition causes massive packet loss, which increases the convergence time of the ADR. Therefore, we address the convergence time issue and propose a novel ADR at the network side to lower packet losses. The proposed ADR is evaluated through extensive simulation. The results show an enhanced convergence time compared to the state-of-the-art ADR method by reducing the packet losses and retransmission under dynamic mobile LoRaWAN network.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

Introduction Retinoblastoma (Rb) is the most common intraocular malignant tumor of childhood. The different modes of Rb presentation comprise proptosis, anterior chamber inflammatory signs, spontaneous hyphema, secondary glaucoma, and strabismus. The primary aim of this study was to investigate the different clinical presentations and stages of Rb that may help in early detection and timely diagnosis to prevent the advancement of the disease and increase rates of survival in children. Materials and methods This was a descriptive cross-sectional study conducted between December 2019 and May 2020 over a period of six months at a tertiary care hospital in Karachi, Pakistan. The sample size included 68 eyes of children with lesions of Rb at the time of presentation to the hospital. Brightness scans (B-scans), computed tomography (CT) scans, and magnetic resonance imaging (MRI) were performed. The International Intraocular Retinoblastoma Classification (IIRC) was used to stage each eye. In case of enucleation (if necessary) of the eye, the biopsy was performed to evaluate the histological features of cancer. All statistical analysis was performed using Statistical Package for Social Sciences version 17.0 (IBM Corp., Armonk, New York). Results The mean age of the children was 3.21 ± 1.75 years. Leukocoria was the most common clinical presentation observed in more than half (n = 35, 51.47%) of the sample population followed by proptosis reported in nearly two-fifths (n = 25, 36.76%), strabismus and phthisis bulbi observed in equal proportions (n = 3, 4.41%), and hypopyon documented in a minor proportion (n = 2, 2.94%) of patients. Regarding stages of the Rb disease, the most common stages were observed to be stage C and stage E. Conclusions This study concludes that the early detection of Rb is possible through a better understanding of presenting features of the disease. It can prevent the progression of the disease to the advanced stages and decrease morbidity and mortality. The early detection of Rb can be made possible through the examination of red reflex on the regular check-ups of children as leukocoria is the most common clinical presentation.

One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.

Information-centric networking is the developing model envisioned by an increasing body of the data communication research community, which shifts the current network paradigm from host centric to data centric, well-known to information-centric networking (ICN). Further, the ICN adopts different types of architectures to extend the growth of the Internet infrastructure, e.g., name-based routing and in-network caching. As a result, the data can be easily routed and accessed within the network. However, when the producer generates contents for authentic consumers, then it is necessary for him/her to have a technique for content confidentiality, privacy, and access control. To provide the previously mentioned services, this paper presents a certificateless proxy reencryption scheme (CPRES) based on the hyperelliptic curve for access control in the content-centric network (CCN). Using certificateless PRE, the power of the key generation center (KGC) is limited to only the generation of partial keys to secure the access to the content. With the help of these partial keys, the producer further calculates keys for encryption and reencryption process. The simulation results show that the proposed scheme provides secure access to content during end-to-end communication. Moreover, the proposed CPRES scheme outperforms in terms of low computational energy and efficient utilization of communication bandwidth.

Mesoporous CuS nanospheres (CuS-NS) decorated reduced graphene oxide (rGO) aerogel composite (3D CuS-NS/rGO) was prepared by chemical reduction process and used for the synergistic removal of Cr(VI) and cationic dyes. The porosity of the as prepared samples was determined by Bruner-Emmet-Teller (BET) surface Area. Structural and morphological properties were studied by Scanning electron microscopy (SEM) and Transmission electron microscope (TEM). These analysis revealed that the as obtained hybrid CuS-NS/rGO composite with three dimensional (3D) structure was composed of mesoporous CuS nanospheres clearly induced onto the interconnected network of rGO sheets. The photocatalytic performance of 3D CuS-NS/rGO composites was studied against the reduction of Cr(VI) and degradation of cationic dyes (MB and RhB) under visible light spectrum. Excellent photocatalytic performance was observed with 3D CuS/rGO hybrid composites as compared to the as prepared CuS nanospheres. This high photocatalytic activity was attributed to the efficient charge transfer from the mesoporous CuS nanospheres to nanosheets of rGO, which was confirmed by UV–Vis spectrometry (UV–Vis). Electrical conductivity of the prepared samples was also investigated using electrochemical impedance spectroscopy (EIS). Additionally, the as prepared hybrid composites was easy to recycle by using simple tweezers and can be a best candidate for industrial applications.

For a long time, many techniques have been used for Optical Character Recognition in image processing. But Pashto optical character recognition is less developed area. Because of Pashto cursive nature, writing direction from right to left, and its character change its shape when it is placed at the start, middle and end makes the development of Pashto OCR a complex task. The proposed system takes an image with Pashto text input. After that process it and pass through its modules and generate an editable Pashto text file. More than 5000 images in dataset is use to classify the characters using Sequential Minimal Optimization classifier. An accuracy of our proposed system is approximately 92 Percent.

3D graphene and its derivatives have attracted widespread attention as novel material for the adsorption and photocatalysis of wastewater contaminants. Because of unique physicochemical properties and easy regeneration abilities, it can be an excellent and most efficient material for the treatment of water pollutants. 3D graphene aerogels have been proven to be very powerful removing agents of wastewater contaminants like heavy metals, chemicals, dyes, oils and solvents. Literature reports that the mesoporous and macroporous structure of 3D graphene based materials (3DGBMs) provide better and efficient results compared to conventional graphene in adsorption and photodegradation of water contaminants. There are several other applications of 3DGBMs, however this review focuses on the removal of wastewater contaminants. Discussions, commentary and analyses have been added for the synthesis and application of 3DGBMs in wastewater only. This review will help to find out all the latest work on 3DGBMs towards the decontamination of wastewater pollutants.

Jabran, Khawar/0000-0001-8512-3330; WOS: 000436879200076 PubMed: 29691745 Plants in Brassica genus have been found to possess strong allelopathic potential. They may inhibit seed germination and emergence of subsequent crops following them in a rotation system. Series of laboratory and greenhouse experiments were conducted to determine the allelopathic impacts of Brassica napus L. against mung bean. We studied (1) the effects of aqueous extract (5%) of different plant parts (root, stem, leaf, flower, and whole plant) of B. napus, (2) the effects of leaf and flower extracts of B. napus at 0, 1, 2, 3, and 4% concentrations, and (3) the effect of residues of different B. napus plant parts and decomposition periods (0, 7, 14, and 21 days) on germination and seedling growth of mung bean. Various types of phenolics including quercitin, chlorogenic acid, p-coumeric acid, m-coumaric acid, benzoic acid, caffeic acid, syringic acid, vanillic acid, ferulic acid, cinamic acid, and gallic acid were identified in plant parts of B. napus. Among aqueous extracts of various plant parts, leaf and flower were found to have stronger inhibitory effects on germination and seedling growth traits of mung bean, higher concentrations were more toxic. The decomposition period changed the phtotoxic effect of residues, more inhibitory effect was shown at 14 days decomposition while decomposition for 21 days reduced inhibitory effect. The more total water-soluble phenolic was found in 5% (w/v) aqueous extract and 5% (w/w) residues of B. napus flowers at 14 days of decomposition (89.80 and 10.47 mg L-1), respectively. The strong inhibitory effects of B. napus should be managed when followed in rotation.

Cloud computing has played much important role in comparison to other fields of IT, in providing Data storage, Data security, Quality of Services (QoS) etc. In the last few years, it had emerged and evolved so quickly due to its number of facilities and advantages to the organizations and end users. Many data security factors have also increased due to this fast evolution of cloud in the IT industry. Therefore several security models and trust establishing techniques have been deployed and are been in execution for providing more security to the data, especially the sensitive data. Despite of that much security, many of the models/techniques lacks in one or more security threat measures. In this paper a new model have been designed and proposed which introduces Security Aware Cloud. First the trust of the user or organization is established successfully on cloud than the security to the data is granted through privacy and encryption module. Level of quality of service and security are achieved under the Contract Trust layer while the Authentication and Key Management are covered under Internal Trust layer. For critical data privacy and encryption, Homomorphism mechanism is used. By the use of proposed trust and security model, we can enhance Return on Investment factor in the cloud for the data security and service provided by it.