•This paper uses building structural attributes such as shapes of buildings, relative compactness(RC), glazing area(GA), roof area(RA), surface area(SA), wall area(WA), orientation(OR), overall ...height(OH) and glazing area distribution(GAD) for prediction of heating and cooling load.•The paper explores the data set and features to get insights with feature correlation, mutual information their association strengths etc and their relation with heating and cooling load.•The paper proposes novel group of methods based on emerging machine learning method extreme learning machine(ELM) which has shallows architecture.•The paper proposes novel method based on ELM variant online sequential extreme learning machine(OSELM) for heating and cooling load prediction in online and adaptive environment. This model also helps when data is trained in chunk by chunk fashion.•The paper demonstrates several models based on combination of learning mode, activation function and features sets and compare their results with existing work extensively.
In the present day environment, smart buildings require optimization of energy consumption through monitoring, consumption prediction and making policy decisions accordingly. Attributes related to building design and structure play a vital role in heating load(HL) and cooling load(CL) of the building which directly affects the energy performance of the buildings. For prediction of HL and CL, emerging machine learning approaches can help in improving accuracy and efficiency in real time. This paper provides improvements in energy load assessment of the buildings. It is the first is the in-depth study and analysis of design and structural attributes and their correlation with HL and CL, the novel methods based on ELM and its variants online sequential ELM(OSELM) to predict HL and CL. This study also proposes OSELM based online/real-time prediction when data is coming in stream The total 24 models have been developed including 12 models based on ELM and 12 models based on OSELM with different feature sets and activation functions. Models have been compared on the basis of accuracy, computational performance and efficiency with few existing models. The experimental results show that the proposed models learn better and outperform other popular machine learning approaches such as the artificial neural network(ANNs), support vector machine(SVM), radial basis function network(RBFN), random forest(RF) and existing work in the energy and building domain.
•This paper proposed the dynamic methods for optimised feature selection for energy and temperature prediction using Particle Swarm Optimization(PSO).•Proposes the hybrid model based on PSO and ELM ...that minimizes the forecasting error of indoor temperature and energy consumption for 30 min ahead and 4 h ahead.•Proposes the hybrid model based on PSO and OSELM for adaptive control of forecasting temperature by reading data in online fashion for 30 min ahead and 4 h ahead prediction.•Comparative study of proposed methods on the same data set.
In modern buildings which are becoming smart day by day, indoor temperature can be forecasted with the data obtained from outfitted sensors. Predictive models based on data can accurately forecast temperature which further saves energy by optimisation of resources and technologies such as heating, ventilation and air conditioners for making the atmosphere conducive and comfortable in the ambient environment. This paper discusses an experiment from such house fitted with sensors for different parameter affecting indoor temperature. We propose a hybrid model which is based on particle swarm optimisation and emerging extreme learning machine to forecast the temperature to optimise the use of energy further. The proposed hybrid model also includes the variant online sequential extreme learning machine(OSELM) which accepts data online and is adaptive to the changing environmental conditions. We perform experiment based on many sensors combinations affecting temperature using particle swarm optimization and statistical tools to determine their relevance and correlation with temperature and compare results with conventional methods. Proposed methods improved the accuracy of the forecasting and also generalisation performance over other methods on the same dataset.
The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect ...of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future.
A chaos-based probabilistic block cipher for image encryption Dhall, Sakshi; Pal, Saibal K.; Sharma, Kapil
Journal of King Saud University. Computer and information sciences,
January 2022, 2022-01-00, 2022-01-01, Letnik:
34, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Traditional encryption is based on secrecy provided by secret-key. But this leads to generation of same cipher text when the encryption scheme is applied to same plaintext with same key. Thus, replay ...of messages can be effortlessly identified by an adversary which can be a weak link in any communication. Probabilistic encryption is an approach to overcome this weakness where different cipher texts are generated each time same plaintext is encrypted using the same key.
Extending the probabilistic approach, which is generally employed in asymmetric encryption, this paper proposes a new chaos-based probabilistic symmetric encryption scheme with customizable block-size suitable for image encryption. It employs a Random Bits Insertion phase followed by four rounds of two-staged diffusion involving simple XOR (exclusive-OR) operation making it computationally efficient. Random Bits Insertion makes the scheme probabilistic. This phase also helps in increasing entropy and making intensity distribution more uniform in cipher. The generated cipher text is twice the size of plain text. An increase in cipher text space is inevitable for probabilistic encryption and it provides an advantage as the apparent message space for the attacker is increased. The observations show that the scheme offers high strength to resist statistical and cryptanalytic attacks.
Common Service Centers (CSCs), which are also known as Tele-centers and Rural Kiosks, are important infrastructural options for any country aiming to provide E-Governance services in rural regions. ...Their main objective is to provide adequate information and services to a country’s rural areas, thereby increasing government-citizen connectivity. Within developing nations, such as India, many CSC allocations are being planned. This study proposes a solution for allocating a CSC for villages in a country according to their E-Governance plan. The Fuzzy C-Means (FCM) algorithm was used for clustering the village dataset and finding a cluster center for CSC allocation, and the Particle Swarm Optimization (PSO) algorithm was used for further optimizing the results obtained from the FCM algorithm based on population. In the context of other studies addressing similar issues, this study highlights the practical implementation of location modeling and analysis. An extensive analysis of the results obtained using a village dataset from India including four prominent states shows that the proposed solution reduces the average traveling costs of villagers by an average of 33 % compared with those of allocating these CSCs randomly in a sorted order and by an average of 11 % relative to centroid allocation using the FCM-based approach only. As compared to traditional approaches like P-Center and P-Median, the proposed scheme is better by 31 % and 14 %, respectively. Therefore, the proposed algorithm yields better results than classical FCM and other types of computing techniques, such as random search & linear programming. This scheme could be useful for government departments managing the allocation of CSCs in various regions. This work should also be useful for researchers optimizing the location allocation schemes used for various applications worldwide.
During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks. Indeed, it is difficult to ...provide secure information systems and to maintain them in a secure state during their lifetime. An IDS is a device or software application that monitors network or system activities for malicious task or policy violations and produces reports to a management station. A metaheuristic is a high-level problem independent algorithmic framework. These are problem-independent techniques and do not take advantage of any specificity of the problem. The main aim of meta-heuristic algorithms is to quickly find solution to a problem. This solution may not be the best of all possible solutions to the problem but still they stand valid as they do not require excessively long time to be solved. Firefly Algorithm is one of the new metaheuristic algorithms for optimization problems inspired by the flashing behavior of fireflies. In this work, a new algorithm for anomaly detection has been introduced which is a hybridization of K-Means and Firefly Algorithm. The algorithm uses clustering to build the training model and uses classification to evaluate on the test set. The subject algorithm is evaluated on the NSL-KDD dataset, which is quite impressive. Further, a comparison study has been performed between the newly developed algorithm with other clustering algorithms including K-Means + Cuckoo, K-Means + Bat, K-Means, K-Means++, Canopy and Farthest First. The results show that K-Means + Firefly and K-Means + Bat outperforms by a huge margin.
Role of technology is improving for COVID-19 management all around the world. Usage of mobile applications, web applications, cloud computing, and related technologies have helped many public ...administrators worldwide manage the current pandemic. Contact tracing applications are such mobile app solutions that are used by more than 100 countries today. This study presents a structured research review-based framework related to multiple contact tracing applications. The various components of the framework are related to technological working, design architecture, and feature analysis of the applications, along with the analysis of the acceptance of such applications worldwide. Also, components focusing on the security features and analysis of these applications based on Data Privacy, Security Vetting, and different attacks have been included in the research framework. Many applications are yet to explore the analytical capabilities of the data generated through contact tracing. The various use-cases identified for these applications are detecting positive case probability, identifying a containment zone in the country, finding regional hotspots, monitoring public events & gatherings, identifying sensitive routes, and allocating resources in various regions during the pandemic. This study will act as a guide for the users researching contact tracings applications using the proposed four-layered framework for their app assessment.
Optimization of energy performance of the buildings has been a great interest of research. Energy performance monitoring and maintenance depend on the several parameters such as temperature, ...humidity, sunlight, roof area, wall area and mostly on heating ventilation and air conditioning(HVAC). Each building has its own patterns and settings obtained through monitoring which depends on many parameters such as users activities, environmental related attributes and building design structural parameters etc. In this respect, an estimation of energy load of the building in real-time efficiently for optimization becomes an important task for cost-effective energy management. The paper proposes an Intra ensemble model based on variants of emerging machine learning approach which includes extreme learning machine(ELM), Online Sequential ELM(OSELM) and Bidirectional ELM(B-ELM). The energy performance estimation requires the model to be real-time and efficient. This asks for use of highly correlated parameters and a very efficient model. For this, OSELM based model for real-time prediction of energy performance has been used. ELM variants are used because of their fast computation and efficiency of prediction over conventional machine learning models. The proposed model has been compared with few state of art methods on accuracy and efficiency criteria and proposed models outperformed existing methods.
Flower pollination algorithm (FPA) is susceptible to local optimum and substandard precision of calculations. Chaotic operator (CO), which is used in local algorithms to optimize the best individuals ...in the population, can successfully enhance the properties of the flower pollination algorithm. A new chaotic flower pollination algorithm (CFPA) has been proposed in this work. Further FPA and its four proposed variants by using different chaotic maps are tested on nine mathematical benchmark functions of high dimensions. Proposed variants of CFPA are CFPA1, CFPA2, CFPA3 and CFPA4. The result of the experiment indicates that the proposed chaotic flower pollination variant CFPA2 could increase the precision of minimization of function value and CPU time to run an algorithm.
Substitution-boxes are important nonlinear components used for achieving strong confusion as well as cryptographic security in a majority of modern symmetric cryptosystems. Designing ...cryptographically strong S-boxes has been a major research domain for the designers of symmetric ciphers. In this research work, Firefly algorithm based technique is proposed for designing S-boxes. The proposed Swarm Intelligence (SI) based technique generates cryptographically strong S-boxes. Furthermore, authors analyze strength of the computed S-boxes by testing: nonlinearity, bijectivity, bit-independence criterion (BIC), linear probability and differential uniformity. For the S-box constructed by the proposed technique; average nonlinearity is 109.25 and average strict avalanche criteria (SAC) value is 0.504. The computed performance results for the S-box are compared with some recently reported S-boxes.
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