The symptoms of plant diseases are evident in different parts of a plant; however leaves are found to be the most commonly observed part for detecting an infection Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images Several works utilized computer vision technologies effectively and contributed a lot in this domain This manuscript summarizes the pros and cons of all such studies to throw light on various important research aspects A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories Discovering a set of acceptable techniques, the manuscript highlights several points of consideration along with the future research directions The survey would help researchers to gain understanding of computer vision applications in plant disease detection

Plants Disease Identification and Classification Through Leaf Images: A Survey

Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop’s low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.

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A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

Multi-biometrics is an exciting and interesting research topic. It is used to recognizing individuals for security purposes; to increase security levels. The recent research trends toward next biometrics generation in real-time applications. Also, integration of biometrics solves some of unimodal system limitations. However, design and evaluation of such systems raises many issues and trade-offs. A state of the art survey of multi-biometrics benefits, limitations, integration strategies, and fusion levels are discussed in this paper. Finally, upon reviewing multi-biometrics approaches and techniques; some open points are suggested to be considered as a future research point of interest.

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Multi-Biometric Systems: A State of the Art Survey and Research Directions

Fingerprint classification and identification algorithms for criminal investigation: A survey

A Data center comprises of servers, storage devices, cooling and power delivery equipment to support other components, exchange data and information to provide general services such as software-as-a-service (SaaS), platform-as-a-service (PaaS), and Internet-as-a-service (IaaS). Data centers require massive amount of computational power to drive complex systems. In return these massive systems bring many challenges and concerns including power dissipation and environmental sustainability. Higher power demand in data centers and changes in computing technology together to maximize data center performance has led to deploying multitude methods to estimate power intensity. Energy cost increment, global economic downturn, and global warming and other concerns have resulted in new research in achieving power efficient data centers.

The research proposed in this paper evaluates three task scheduling algorithms RASA, TPPC, and PALB to get the most energy efficient task scheduling algorithm to be used in data centers for measuring their performance and efficiency. The three algorithms are evaluated for performance using three parameters; power efficiency, cost effectiveness, and amount of CO2 emissions. On top of that data center location and climate conditions are also considered and analyzed as parameters as they directly effect the operating costs, the amount of power consumption and CO2 emission. To minimize the power wasted by data center cooling systems is directly related to data center location and climate change. CloudSim simulator is used to implement the algorithms on an IaaS cloud infrastructure, to calculate the power consumption, and to analyze each algorithm׳s behavior for different parameters. The results generated clearly shows that TPPC is the most efficient algorithm due to less amount of power consumption and low volume of CO2 emission; however its implementation cost is bit higher compare to PALB and RASA.

Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review

Fingerprints are the most widely used biometric feature for person identification and verification in the field of biometric identification. Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) Ridge and furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. In a traditional biometric recognition system, the biometric template is usually stored on a central server during enrollment. The candidate biometric template captured by the biometric device is sent to the server where the processing and matching steps are performed. This paper presents an approach to speed up the matching process by classifying the fingerprint pattern into different groups at the time of enrollment, and improves fingerprint matching while matching the input template with stored template. To solve the problem, we take several aspects into consideration like classification of fingerprint, singular points. The algorithm result indicates that this approach manages to speed up the matching effectively, and therefore prove to be suitable for large database like forensic divisions.

Reducing Process-Time for Fingerprint Identification System

Cloud computing offers a prominent service for data storage known as cloud storage. The flow and storage of data on the cloud environment in plain text format may be main security threat. So, it is the responsibility of cloud service providers to ensure privacy and security of data on storage as well as network level. The following three parameters confidentiality, integrity and availability decide whether security and privacy of data stored on cloud environment is maintained or not. The proposed work is to define cloud architecture with configured samba storage and cryptographic encryption techniques. The cloud architecture deployed with samba storage uses operating system feature specifying permission values for three attributes (User/Owner, Group and Global) and maps it to cryptographic application which performs cryptographic operations. Cryptography application supports symmetric and asymmetric encryption algorithm to encrypt/decrypt data for uploading/downloading within cloud storage. A username and password based authentication mechanism for users and digital signature scheme for data authenticity are defined within cloud architecture.

Enhanced Security Architecture for Cloud Data Security

Template and database are critical parts of biometric systems. Attacker usually afflict biometric template. There can be various attacks on different modules of biometrics system and communication links among them. Securing templates from these attacks have become very important or imperative issue. Security is achieved by methods of cryptography, which deals with encryption of data. Encryption is the process of encoding messages (or information) in such a way that eavesdroppers or hackers cannot read it, but that authorized parties can. In this paper, we present an approach to enhance the invisible watermarking technique with cryptography. The biometric trait is modified using invisible watermark information and is further secured using cryptography. The encryption algorithm which has been used is highly suitable for multimedia as well as text data. We can use different encryption techniques like AES, MAES etc .The template is made more secure using encryption and finally stored in database.

A Novel approach for securing biometric template

Cloud computing is one of the most research hot topics in IT industry nowadays. A lot of startup organizations are adopting cloud eagerly due to massive cloud facilities available with minimal investment; but as every coin has two sides, so with cloud. In the cloud, the user data is stored at some off-site location. So cloud data security is one of the main concerns of any organizations, before shifting to the cloud. The data owners can ensure the data security at its premises using firewalls, VPN (Virtual Private Network) like most used security options. But as data owner stores their sensitive data to remote servers and users access required data from these remote cloud servers, which is not under their control. So storing data outside client premises, raises the issue of data security. Thus, one of the primary research areas in cloud computing is cloud data protection. In this research paper, strategies followed include categorization of the data on the basis of their sensitivity and importance, followed by the various cryptography techniques such as the AES (a Symmetric Cryptography technique), SHA-1 (a Hashing technique), and ECC (Elliptic curve Cryptography (an Asymmetric Cryptography technique). Till date, most of the authors were using a single key for both encryption and decryption which is a weak target of various identified malicious attacks. Hence, in the designed hybrid algorithm, two separate keys are used for each encryption and decryption. The cloud user who wants to access cloud data, need to first register with CSP and cloud owner. After registration, user login id, password and OTP (One Time Password) sent to the user registered mobile number, are required to access the encrypted cloud data.

An Effective Hybrid Encryption Algorithm for Ensuring Cloud Data Security

Biometrics is the utilization of biological characteristics (face, iris, and fingerprint) or behavioral traits (signature, voice) for identity verification of an individual. Biometric authentication is gaining popularity as more trustable alternative to password based security systems as it is comparatively hard to be forgotten, stolen, or guessed. On the other hand, biometric data which can uniquely identify a person (e.g. fingerprints, iris patterns) can be used to track individuals, linking many separate databases (where the person has been, what he has purchased etc.), which raises privacy concerns. This paper discusses about Biometric System and its working. Various biometric technologies are also discussed and compared.

Chander Kant

Papers

Reducing Process-Time for Fingerprint Identification System

Enhanced Security Architecture for Cloud Data Security

A Novel approach for securing biometric template

An Effective Hybrid Encryption Algorithm for Ensuring Cloud Data Security

Biometrics Security Concerns