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Face recognition is one of the most interesting and challenging research area in the past decades. The face recognition system has used facial databases which can verify whether the given facial image is found in the database or not. It is very popular in biometric authentications and surveillance and they do not require the user intervention. Face recognition technique is definitely an emerging multi-disciplinary subject that facilitates discovering of previous unknown patterns from large amount of data. This paper provides the basic concepts of Facial Recognition and its essential characteristics. Face recognition in IoT, biometric system, crowd detection are also discussed in this paper.

Face Detection, Face Recognition, Biometrics, IoT based face recognition, Applications

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A. Sakila, S. Vijayarani, "A Survey on Facial Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.313-319, 2019.

An intrusion detection system is a computer system monitors network traffic for suspicious activity, now, Computer Network is essential to give a high-level security to protect highly sensitive data and information. Network Technology and Internet related Application sector plays a essential role in today’s trend. Many intrusion detection techniques, methods and algorithms help to detect these attacks, hackers use different types of attacks for getting the valuable information. Numbers of clients are being connected with the technology day by day. It`s being done hacked by junction or evil intruder. A very effective tool in this is Intrusion Detection System , which detects the attacks and analysis it to take proper decision against it. Intrusion Detection System has a great impact on cyber security and network vulnerability. Once the detection is scored, the initialed can be taken by IDS. Intrusion detection system is a software and hardware device. This paper will notify us overview of IDS and to create a secure zone in the sector of networking. Numerous intrusion detection techniques, methods and algorithms assist to detect these attacks. Furthermore, appropriate problems and challenges in this field are consequently illustrated and discussed.

Security, Intrusion detection, Network Attacks, Prevention System, Analyzer, Dos, Misuse detection, Anomaly detection

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P. Kaliraj, B. Subramani, "Taxonomy of Intrusion Detection System –A Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.320-324, 2019.

Content Based Image Retrieval (CBIR) is the technique of retrieving the similar images from the large database as per user query by matching the contents of images. CBIR is widely used in various computer vision applications such as medical field, E-commerce, Satellite Imaging, and Art Collections and so on. Different types of contents which can be used for retrieving images are Color, Texture, Shape, and/or Spatial Information. The performance of CBIR depends vitally on Feature Extraction, Feature Reduction, Feature Selection, Similarity Measure, Classification, and Ranking. This paper presents the review of different feature extraction strategies used recently for CBIR. Literature review of different Feature Extraction methods used for evaluating the performance CBIR are discussed in order to grasping details about the domain. This review article mainly focuses on the feature extraction which is most crucial part of the CBIR system.

CBIR, Feature Extraction, Color, Texture, Shape

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[4]. Joshi, Chandani, GN Purohit, and Saurabh Mukherjee. "Impact of CBIR journey in satellite imaging." Communication and Computing Systems: Proceedings of the International Conference on Communication and Computing Systems. CRC Press, 2017. 341.
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[6]. Khare, Ashish, and Prashant Srivastava. "Utilizing multiscale local binary pattern for content-based image retrieval." Multimedia Tools and Applications 77, no. 10 (2017): 12377–12403.
[7]. Kumar, Arun T, and Naaz Effat. "Enhanced Content Based Image Retrieval Using Machine Learning Techniques." Innovations in Information, Embedded and Communication Systems, International Conference on. 2017. 1-12.
[8]. Liu, Y, D Zhang, G Lu, and W Y Ma. "A survey of content-based image retrieval with high-level semantics." Pattern Recognition 40, no. 1 (2007): 262-282.
[9]. Nagarajan, V, and T G Subash Kumar. "Local curve pattern for content-based image retrieval." Pattern Analysis and Applications In Press (2018): 1-10.
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S. M. Chavda, M. M. Goyani, "Recent evaluation on Content Based Image Retrieval," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.325-329, 2019.

Face recognition is the capability of identifying and authenticating the dominating and leading features of the face from the dataset images. It`s important in Access and Security, Healthcare, Banking, Criminal Identification, Payment, Advertising, and in many other fields. In this paper, we have assessment important basic phases of face recognition like Pre-processing, Face Detection, Feature Extraction, Optimal Feature Selection, and Classification. Feature Extraction, Feature Selection, and Classification play a major role in face recognition. The research area of statistical texture classification is widely investigated in several computer vision and pattern recognition problems. A general framework for face recognition with statistical and geometrical approaches and classification presented in this survey paper.

Face Recognition, Feature Extraction Approaches, Optimal Feature Reduction, Classification

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Payal P. Parekh, M. M. Goyani, "Face Recognition: Modern Assessment of Features Extraction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.330-335, 2019.

Keyword search on confidential data in a cloud environment is the main focus of this research. The cloud providers are not fully trusted. So, it is necessary to outsource data in the encrypted form. In the attribute-based keyword search schemes, the authorized users can generate some search tokens and send them to the cloud for running the search operation. A new cryptographic primitive called key-policy attribute-based temporary keyword search provides this property. To evaluate the security of our scheme, we formally prove that our proposed scheme achieves the keyword secrecy property and is secure against selectively chosen keyword attack both in the random oracle model and under the hardness of Decisional Bilinear Diffie-Hellman assumption.

Cipher text, Token, Encrypted form, leakage information, Temporary keyword

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[4] M. Abdalla, M. Bellare, D. Catalano, E. Kiltz, T. Kohno, T. Lange, J. Malone-Lee, G. Neven, P. Paillier, and H. Shi, “Searchable encryption revisited: Consistency properties, relation to anonymous ibe, and exten- sions,” in Advances in Cryptology–CRYPTO 2005. Springer, 2005, pp. 205–222.
[5] X. Boyen and B. Waters, “Anonymous hierarchical identity-based en- cryption (without random oracles),” in Annual International Cryptology Conference. Springer, 2006, pp. 290–307.
[6] Y. Yu, J. Ni, H. Yang, Y. Mu, and W. Susilo, “Efficient public key encryption with revocable keyword search,” Security and Communication Networks, vol. 7, no. 2, pp. 466–472, 2014.
[7] Z. Fu, X. Wu, C. Guan, X. Sun, and K. Ren, “Toward efficient multikeyword fuzzy search over encrypted outsourced data with accuracy improvement,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 12, pp. 2706–2716, 2016.
[8] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacypreserving multi- keyword ranked search over encrypted cloud data,” IEEE Transactions on parallel and distributed systems, vol. 25, no. 1, pp. 222–233, 2014.
[9] H. Li, Y. Yang, T. H. Luan, X. Liang, L. Zhou, and X. S. Shen, “Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data,” IEEE Transactions on Dependable and Secure Computing, vol. 13, no. 3, pp. 312–325, 2016.

M. Prasanna Lakshmi, V. Esther Jyothi, M. Venkata Rao, "Keyword Search on Confidential Data in A Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.336-338, 2019.

In current era, the trend of application of data mining is widely used because of health sector is rich in information and data mining has become its necessity. In the healthcare organizations many data and information is generated on daily basis. Use of data mining and knowledge that help bring some interesting patterns which means eliminate manual tasks and easy data extraction from any electronic records, through that will secure medical records, save patient’s lives and also reduce the cost of medical services as well as early detection of any infectious disease on the basis of historical and advanced data collection. Data mining can enable healthcare organizations to predict trends in the patient’s medical condition and behavior proved by analysis of different prospects and by making connections between totally unrelated data and information. Generally the raw data from the healthcare organizations are tremendous and heterogeneous. These all data can be gathered from various sources or different components. Data mining has great importance for area of healthcare and also it represents comprehensive process that demands through understanding of requirement of the healthcare organization. Knowledge gained with the use of techniques of data mining can be used to make successful decisions that will improve success of healthcare organizations and also health of the patients. Data mining once started, represents continuous cycle of knowledge discovery. In this paper, I wish to discuss that how data mining is used in infectious disease like cervical cancer.

Data Mining, Knowledge Discovery Database, Cervical cancer, Classification, Clustering

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Dipti N. Punjani, Kishor H. Atkotiya, "Data Mining Techniques in Biological Research," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.339-343, 2019.

A huge amount of data has been exchanged over various types of networks due to the rapid growth of computer networks and information technology. In major part of these exchanged data, they need security mechanisms to offer various degree of protection. The common solution to protect digital data from eavesdropping and intercepting is to encrypt the message which needs some knowledge of cryptography. Cryptography is a technique used today hiding any confidential information from the attack of an intruder and used to create authentication, integrity, availability and confidentiality. Nowadays, Digital data communication requires data security, so that data should reach to the intended user in safe manner. The protection of confidential data from unauthorized access can be done with many encryption techniques. Encryption is used to protect data from being accessed by unauthorized users. It is very important to communicate images over networks. The important image transfer will takes place over the unsecured internet network. Continuing development of the various electronic image processing technologies has produced faster means of transmission. Compression is often used to save disk space and reduce the time needed to transfer images over the networks. Compressing data can save storage capacity, speed up file transfer and reduce cost for hardware storage and network bandwidth.

Data Compression, Data Encryption, Cryptography, Image Processing and Security Mechanism

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G.Elavarasi, M. Vanitha , "Enhanced Compression and Cryptographic Techniques for Securing Images- A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.344-348, 2019.

Learning Management Systems (LMS) introduced in 1990’s support online learner-centered model by complete management of teaching, learning and assessment. These LMS help teachers to deliver course related material to their students, manage quizzes, exams and other course related tasks, evaluate student performance and manage other activities. The LMS tools like Moodle, Blackboard, Sakai, EvalTool, Dokeos etc., have produced outstanding results for both teachers as well as students. Since most of students use laptops and smart phones to access LMS for their learning related activities, therefore, their online activities generate huge volume of data that educational institutions can use to improve the performance of their teaching and learning activities. This huge volume of data can give a deep insight into the teaching and learning activities if analyzed properly. Learning Analytics(LA) is a new emerging field that analyzes this big data and develop the models that can predict the performance of student, detect the students who are most likely to drop out from the courses, prepare the reports, provide the intelligent and instant feedback to students, recommend the courses to students based on their interests and assesses the skills developed by the students. Therefore, by using learning analytics, teachers, students, faculty, and administrators can develop more engaged and effective teaching and learning techniques. Keeping in view the importance of LA, this paper discusses the LMS, LA and a framework for using LA on the LMS data.

Learning Analytics (BA); Data Mining; Dashboard; Scorecard; Learning Management Systems (LMS)

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Rafi Ahmad Khan, "Learning Analytics with Big Data: A Framework," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.349-353, 2019.

In a distributed computing, the serious problem from the starting days is the distribution of load among servers in the commercial internet. Cloud computing faces some challenges in Load balancing and it requires the energetic workload among various nodes and it assure that not a single node get affected. The important aim is the minimization of the load balance from the sources utilization which would be decreased by the release rate of the carbon and energy consumption would be extremely required in cloud computing.

Cloud computing, Green computing, Load Balancing, Carbon footprint and Energy efficiency

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Lubna Kalam, Ihtiram Raza Khan, "Effort of Load Balancing to achieve Green Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.354-359, 2019.

The motivation for this work arises from the need of Automatic Document Classification (ADC) which is necessary when the task involves business specific contexts which cannot be fulfilled via querying on any search engine. Since a large number of websites are available over the internet nowadays therefore users generally search information from different websites via search engines now. But search engines require appropriate keywords from users in order to give relevant information from the web and sometimes users have obtained irrelevant results if he or she is not sound enough to provide keywords correctly. Thus, we need a proper document classification for the material of our wish so that the one which is required can be obtained easily instead of wasting time in searching. To understand this, we have discussed the automatic document classification in news domain where we classify news articles into four distinct categories: business, science & technology, entertainment and health using Linear SVM with SGD training and multinomial NB classifier and compare their performance. The classification is based on the title of the news article taken as feature.

Automatic Document Classification, Linear SVM, Stochastic Gradient Descent, multinomial NB

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Feroz Ahmed, Shabina Ghafir, "Linear Support Vector Machine (SVM) with Stochastic Gradient Descent (SGD) training & multinomial Naïve Bayes (NB) in News Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.360-363, 2019.