Archive

Abstract:

This study scrutinizes the correlation between the parameters of energy firm performance and liquidity. The study’s specific objective and central purpose is to gain insights from the context of Malaysia. The process of collecting data focused on the energy firm, with the targeted duration stretching between 2005 and 2017. The database or website from which the data was gained was Thompson Data Stream, Bloomberg, and www.bursamalaysia.com. It is also notable that the study applied or employed a multivariate regression technique to discern the relationship between independent and dependent variables. Particularly, the independent variable constituted the performance of energy companies. From the findings, the study established that the relationship between the performance of energy companies and the parameter of liquidity is statistically significant.

Keywords:

Liquidity,Cash Cycle,Energy Firm,Firm’s performance,

Refference:

I. Ambrose A., Martin S. and, Dave K (2017), Efficient working capital
management as the tool for driving profitability and liquidity: a correlation
analysis of Nigerian companies, International Journal of Business and
Globalisation, 18(2)
II. Busutti, J., (2014), Tackling Cash flow Problems:
III. Deloof, M. (2003), Does working capital management affect profitability of
Belgian firms? 30 (3/4), 73-588.
IV. Ebben, J. J., and Johnson, A. C. (2011). Cash conversion cycle management
in small firms: Relationships with liquidity, invested capital, and firm
performance. Journal of Small Business and Entrepreneurship, 24(3), 381-396
V. Eljelly, A. M.A (2004). Liquidity ‐ profitability tradeoff: An empirical
investigation in an emerging market. International journal of commerce and
management, 14(2), 48-61.
VI. Listed In Nigeria Stock Exchange, International Journal of Management
Sciences and Business Research, 5(7),1-133
VII. Nobanee, H., Abdullatif, M. and AlHajjar, M. (2011), Cash conversion cycle
and firm’s performance of Japanese firms, Asian Review of Accounting,
19(2), 147-156.
VIII. Ofoegbu N.G, Duru N. A and Onodugo,A(2016), Liquidity Management and
Profit Performance of Pharmaceutical Manufacturing Firms Russel,
IX. Prevention is better than cure. Availble fromhttp://www.timesofmalta.com.
Accessed 1st october 2018.
X. Quayyum, S.T. (2011), Relationship between working capital management
and profitability in context of manufacturing industries in Bangladesh,
International journal of Business and Management,7 (1), 58.
XI. NEETHU, J., et al. “A PROSPECTIVE STUDY ON RESPIRATORY
DISTRESS SYNDROME AMONG NEONATES IN NICU &
ASSESSMENT OF KNOWLEDGE, ATTITUDE & PRACTICE ON
NEONATAL CARE AMONG POSTNATAL MOTHERS–A PILOT
STUDY.” International Journal 5.1 (2017): 1.

View Download

Abstract:

Unidimensionality is one of the key considerations for testing items in meeting assumptions in the Rasch Model measurement. However, these assumptions can sometimes be difficult to meet because many factors need to be taken into account during construction of the item. Therefore, this study focuses on empirical evidence for the unidimensional study of TSP teacher effective teaching instruments using Rasch Model testing. Quantitative survey design was used. A total of 203 TSP teachers were selected through targeted sampling. The research instrument was adapted from the Trust School Teacher Handbook 2018, the Performance Management System for Teachers with fourteen competencies including Seven Pedagogical Pillars. Data were analyzed using Rasch Model framework with Winstep 3.68.2 software. The findings show that the value of raw variants is explained by a 45.2 percent measurement above the 40% confidence interval in the Residual Principal Component Analysis (PCA). In addition, the noise level of the item recorded 5.2 percent. Although the eigenvalues cannot be met to meet the four criteria testing the assumption of unidimensionality, the eigenvalues appear to surpass these assumptions when measured by construction. Overall, this instrument fulfills the assumption of unidimensionality and can be used to measure the effective teaching of TSP teachers. Further studies can be made by comparing the effects of Rasch's unidimensionality in factor analysis.

Keywords:

unidimensionality,effective teaching instruments,Trust School Program (TSP),eigenvalues,

Refference:

I. Aziz Abdul Aziz., Masodi, M.S. & Zaharim, A. 2017. Asas Model
Pengukuran Rasch : Pembentukan Skala dan Struktur Pengukuran. Bangi:
Penerbit Universiti Kebangsaan Malaysia.
II. Azrilah Abdul Aziz. 2010. Rasch Model Fundamentals: Scale Construct and
Measurement Structure. Kuala Lumpur: Integrated Advance Planning Sdn
Bhd.
III. Bejar, I.I. 1983. Subject Matter Experts ’ Assessment of Item Statistics.
Applied Psychological Measurement 7(3): 303–310.
IV. Bond, T.G. & Fox, C.M. 2015a. Applying The Rasch Model: Fundamental
Measurement in the Human sciences. Third. New York: Routledge Taylor &
Francis Group.
V. Bond, T.G. & Fox, C.M. 2015b. Applying The Rasch model. Third Edit.
New York: Routledge New York.
VI. Brentari, E. & Golia, S. 2007. Unidimensionality in the Rasch model : how
to detect and interpret. Statistica 3(May 2014).
VII. Christensen, K.B., Bjomer, J.B., Kreiner, S. & Petersen, J.H. 2002. Testing
unidimensionality in poly- tomous Rasch models. Psychometrika 67(4): 563–
574.
VIII. Cochran-Smith, M. 2003. Teaching Quality Matters. Journal of Teacher
Education 54(2): 95–98.
http://journals.sagepub.com/doi/10.1177/0022487102250283.
IX. Cohen, L., Manion, L. & Morrison, K. 2017. Research Methods In
Education. Eight Edit. New TYork: Routledge Taylor & Francis Group.
X. Haberman, S.J. 2008. When can subscores have value? Journal of
Educational and Behavioral Statistics 33(2): 204–229.
XI. Hsieh, F.P., Lin, H. shyang, Liu, S.C. & Tsai, C.Y. 2019. Effect of Peer
Coaching on Teachers’ Practice and Their Students’ Scientific Competencies.
Research in Science Education (70).
XII. Ishak, A.H., Osman, M.R., Mahaiyadin, M.H. & Tumiran, M.A. 2018.
Examining Unidimensionality Of Psychometric Properties Via Rasch Model.
International Journal Of Civil Engineering and Technology 9(9).
XIII. SHAH, ASHISH P., et al. “INSILICO DRUG DESIGN AND MOLECULAR
DOCKING STUDIES OF SOME NATURAL PRODUCTS AS TYROSINE
KINASE INHIBITORS.” International Journal 5.1 (2017): 5.

View Download

Abstract:

The criteria to judge the capacity of computational systems is changing with the advancement in technology. Earlier, they were judged only on the basis of computational capacity but now a day, power and energy optimization is one of the key parameters fortheir selection. The purpose of energy optimization is to prolong the battery life of all the battery operated devices especially in embedded systems. An Offline Scheduling Algorithm technique is proposed that migrate task load to the core that has less thermal values in response to a threshold temperature this technique also considers other thermal problems which affect the power, reliability and performance of multi-core system. Hardware technique on their own is insufficient so it must be combined with other software techniques to decide when and where optimization policies are applied to minimum energy consumption. This paper focusesonmost popular optimization techniques Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Power Management (DPM) and Dynamic Thermal Management (DTM) and their extensions. The paper also includes the thermal issues which are raised due to high temperature in multicore platforms.It also highlights that how energy efficient techniques can be used beyond simple energy saving The simulation results shows that the proposed technique reduces almost 4.3℃ temperatures at 17% utilization and the energy utilization is 364.58 J which is 4.14 % improved as compare to the global EDF Scheduling technique used previously.

Keywords:

Dynamic Voltage and Frequency Scaling,Dynamic Power Management,Dynamic Thermal Management,Earliest Deadline First,Least Laxity First,

Refference:

I. A. Coates, B. Huval, T. Wang, D. Wu, B. Catanzaro, N. Andrew, Deep
learning with COTS HPC systems, in: Proceedings of the 30th International
Conference on Machine Learning, 2013, pp. 1337–1345.
II. C. Lefurgy, K. Rajamani, F. Rawson, W. Felter, M. Kistler And T.
Keller, “Energy Management for Commercial Serv- ers,” In IEEE
Computer, Vol. 36, Issue 12, pages 39 – 48,2003.

III. C.-h. Hsu and W.-c. Feng, “ A power-aware run-time system for highperformance
computing ,” in Proceedings of the 2005 ACM/IEEE
conference e on Supercomputing. IEEE Computer Society,2005.
IV. D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den
Driessche,J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot,
et al., Masteringthe game of Go with deep neural networks and tree search,
Nature 529 (7587)(2016) 484–489.
V. D. Konar, K. Sharma, V. Sarogi and S. Bhattacharyya, “A Multi- Objective
Quantum-Inspired Genetic Algorithm (Mo-QIGA) for Real-Time Tasks
Scheduling in Multiprocessor Environment”, Procedia Computer Science,
vol. 131, pp. 591-599, 2018.
VI. H. Khan, M. U. Hashmi, Z. Khan, R. Ahmad, and A. Saleem, “Performance
Evaluation for Secure DES-Algorithm Based Authentication & Counter
Measures for Internet Mobile Host Protocol,” IJCSNS Int. J. Comput. Sci.
Netw. Secur. VOL.18 No.12, December 2018, vol. 18, no. 12, pp. 181–185,
2018.
VII. H. Khan, Q. Bashir, and M. U. Hashmi, “Scheduling based Energy
Optimization Technique in multiprocessor Embedded Systems,” in 2018
International Conference on Engineering and Emerging Technologies
(ICEET).doi:10.1109/iceet1.2018.8338643, 2018.
VIII. H. Khan, S. Ahmad, N. Saleem, M. U. Hashmi, and Q. Bashir, “Scheduling
Based Dynamic Power Management Technique for offline Optimization of
Energy in Multi Core Processors,” Int. J. Sci. Eng. Res. Vol. 9, Issue 12,
December-2018, vol. 9, no. 12, pp. 6–10, 2018.
IX. H. Khan, M. U. Hashmi, Z. Khan, and R. Ahmad, “Offline Earliest Deadline
first Scheduling based Technique for Optimization of Energy using STORM
in Homogeneous Multi- core Systems,” IJCSNS Int. J. Comput. Sci. Netw.
Secur. VOL.18 No.12, December 2018, vol. 18, no. 12, pp. 125–130, 2018.
X. M. Bohr , R. Chau, T. Ghani , and K. Mistry , “ The High- k Solution,
” IEEE Spectrum, vol. 44, no. 10, pp. 29-35 , Oct. 2007.
XI. N. Fathima, “Website : www.ijirset.com Energy Aware Dynam Slack
Allocation for Multiprocessor System,” pp. 7476–7483, 2017.
XII. Q. Bashir, H. Khan, M. U. Hashmi, and S. Ali zamin, “A Survey on
Scheduling Based Optimization Techniques in Multi-Processor Systems,” in
Proceedings of the 3rd International Conference on Engineering & Emerging
Technologies (ICEET), Superior University, Lahore, PK, 7-8 April, 2016.,
2016.
XIII. R. Ayoub, S. Sharifi and T. Rosing, “GentleCool: Cooling Aware -+
pages 295 – 298, 2010.
XIV. S. Shi, Q. Wang, P. Xu, X. Chu, Benchmarking state-of-the-art deep learning
software tools, in: Proceedings of the 7th IEEE International Conference on
Cloud Computing and Big Data, Macau, China, 2016.

XV. S. Kaxiras, Z. Hu, and M. Martonosi, “Cache Decay: Exploiti-ng
Generational Behavior to Reduce Cache Leakage Pow-er,” Proc.Int’lSymp.
Computer Architecture (ISCA ’01), pp. 240-251, 2001.
XVI. S. Yang, M. Powell, B. Falsafi, K. Roy, and T. Vijaykumar, “ An
Integrated Circuit / Architecture Approach to Reducing Leakage in
Submicron High-PerformanceI-caches, ” Proc. Seventh Int’l Symp.
High- Performan- ce Computer Architecture (HPCA ’01), pp. 147-157, 2001.
XVII. S. Dutta, R. Jensen, and A. Rieckmann, ʺViper: A Multiprocessor SOC for
Advanced Set‐Top Box and Digital TV Systems,ʺ Design and Test of
Computers vol. 18, pp. 21 31, 2001, IEEE.
XVIII. T. J. Semiconductor, B. Doyle, M. Group, and I. Corporation “Transistor
Elements for 30 nm Physical Gate Length And Beyond,” Int’l Technology
J., vol. 6, pp. 42-54, 2002.
XIX. T. Horvath, T. Abdelzaher, K. Skadron, and X. Liu, “Dynamic voltage
scaling in multitier web servers.
XX. V. Shinde, “Comparison of Real Time Task Scheduling Algorithms,” vol.
158, no. 6, pp. 37– 41, 2017.
XXI. Xu. J. &Parnas, D., “Scheduling Processes with Release Times,
Deadlines,Precedence and Exclusion Relations”, IEEE Transactions on
Software Engineering. Vol. 16(3), pp. 360-369, 1990.

View Download

Abstract:

Computational auditory scene analysis (CASA) based speech separation is widely considered in a number speech processing applications and is used to separate a target speech from target-interference mixtures and usually the task of target separation is considered as a signal processing problem. However, target speech separation is formulated as a supervised learning problem and discriminative patterns of speech, speakers and background noises are learned from input training data. In this paper, we present a single channel supervised speech separation approach based on the ideal binary mask (IBM) estimation. In proposed approach, speaker independent speech separation system is trained with sets of the clean speech magnitudes and during separation; SNR is estimated in time-frequency (TF) channels using clean magnitudes and compared to a pre-defined threshold. The TF channels satisfying threshold are hold while TF channels violating the threshold are discarded to construct an IBM. The estimated mask is than applied to the mixtures to reconstruct the target speech, using phase of the mixture speech. The experiments are conducted in three speaker independent mixture’s scenarios: termed as 2-talkers, 3- talkers and 4-talkers mixtures at four input SNRs: -5dB, 0dB, 5dB and 10dB. The experimental outcomes reported that proposed CASA based supervised speaker independent mask estimation outperformed the competing approaches: Nonnegative matrix factorization (NMF), Nonnegative dynamical system (NNDS) and log minimum mean square error (LMMSE) estimation in terms of PESQ, SegSNR, LLR, WSS, SIG, BAK and STOI objective measures.

Keywords:

CASA,IBM,intelligibility,time-frequency masking,supervised speech separation,quality,

Refference:

I. Cauwenberghs, G. (1999). Monaural separation of independent acoustical
components. In Circuits and Systems, 1999. ISCAS’99. Proceedings of the
1999 IEEE International Symposium on (Vol. 5, pp. 62-65). IEEE.
II. Darwin, C. J. (1997). Auditory grouping. Trends in cognitive sciences, 1(9),
327-333.
III. Ellis, D.P.W. (1996). Prediction-driven computational auditory scene
analysis (Doctoral dissertation, Massachusetts Institute of Technology).
IV. Hu, Y., & Loizou, P. C. (2007). Subjective comparison and evaluation of
speech enhancement algorithms. Speech communication, 49(7-8), 588-601.
V. Hyvärinen, A., Hurri, J., & Hoyer, P. O. (2009). Independent component
analysis. In Natural Image Statistics (pp. 151-175). Springer, London.
VI. Li, H., Wang, Y., Zhao, R., & Zhang, X. (2018). An Unsupervised Two-
Talker Speech Separation System Based on CASA. International Journal of
Pattern Recognition and Artificial Intelligence, 32(07), 1858002.
VII. Li, P., Guan, Y., Xu, B., & Liu, W. (2006). Monaural speech separation based
on computational auditory scene analysis and objective quality assessment of
speech. IEEE Transactions on Audio, Speech, and Language
Processing, 14(6), 2014-2023.
VIII. Rix, A. W., Beerends, J. G., Hollier, M. P., & Hekstra, A. P. (2001).
Perceptual evaluation of speech quality (PESQ)-a new method for speech
quality assessment of telephone networks and codecs. In Acoustics, Speech,
and Signal Processing, 2001. Proceedings.(ICASSP’01). 2001 IEEE
International Conference on (Vol. 2, pp. 749-752). IEEE.

IX. O’shaughnessy, D. (1987). Speech communication: human and machine.
Universities press.
X. Roweis, S. T. (2001). One microphone source separation. In Advances in
neural information processing systems (pp. 793-799).
XI. Saleem, N., Shafi, M., Mustafa, E., & Nawaz, A. (2015). A novel binary
mask estimation based on spectral subtraction gain-induced distortions for
improved speech intelligibility and quality. University of Engineering and
Technology Taxila. Technical Journal, 20(4), 36.
XII. Saleem, N., & Irfan, M. (2018). Noise reduction based on soft masks by
incorporating SNR uncertainty in frequency domain. Circuits, Systems, and
Signal Processing, 37(6), 2591-2612.
XIII. Saleem, N. (2017). Single channel noise reduction system in low
SNR. International Journal of Speech Technology, 20(1), 89-98.
XIV. Saleem, N., Mustafa, E., Nawaz, A., & Khan, A. (2015). Ideal binary
masking for reducing convolutive noise. International Journal of Speech
Technology, 18(4), 547-554.
XV. Taal, C. H., Hendriks, R. C., Heusdens, R., & Jensen, J. (2010, March). A
short-time objective intelligibility measure for time-frequency weighted noisy
speech. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE
International Conference on (pp. 4214-4217). IEEE
XVI. Wang, D. L., & Brown, G. J. (1999). Separation of speech from interfering
sounds based on oscillatory correlation. IEEE transactions on neural
networks, 10(3), 684-697.
XVII. Weintraub, M. (1985). A theory and computational model of auditory
monaural sound separation (Doctoral dissertation, Stanford University).

View Download

Abstract:

DDoS attacks are initiated from various locations around the world and can be started very easily. This can be achieved by thwarting access to virtually anything: servers, devices, services, networks, applications, and even specific transactions within applications. In a DoS attack, its one system that is sending the malicious data or requests; a DDoS attack comes from multiple systems. Generally, these attacks work by drowning a system with requests for data. This could be sending a web server so many requests to serve a page that it crashes under the demand, or it could be a database being hit with a high volume of queries. The result is available internet bandwidth, CPU and RAM capacity becomes overwhelmed. Distinguishing between attack traffic and normal traffic is difficult, especially in the case of a application layer attack such as a botnet performing a HTTP Flood attack against a victim’s server. Because each bot in a botnet makes seemingly legitimate network requests the traffic is not spoofed and may appear “normal” in origin. In this research propose DDoS attack mitigation framework, the framework composed two parts proactive approach and reactive approach, proactive approach further contain four components Secure software development life cycle, application load test application stress test and ddos incident response plan, while reactive approach contain eighth components bandwidth management, perimeter firewall, intrusion detection and prevention system, web application firewall, load balancer, endpoint security firewall, Dedicated DDoS mitigation device and monitoring, collectively this framework will help as to design such infrastructure which will be stopping DDoS attack enough so that they attacker cannot be easily breakdown and unavailability of the services should accessible.

Keywords:

DDoS attack,Application Layer Attack,Attack detection,botnet,DDoS framework,,

Refference:

I. Aamir, Muhammad, and Syed Mustafa Ali Zaidi. “Clustering based semisupervised
machine learning for DDoS attack classification.” Journal of
King Saud University-Computer and Information Sciences (2019).
II. Aamir, Muhammad, and Syed Mustafa Ali Zaidi. “DDoS attack detection
with feature engineering and machine learning: the framework and
performance evaluation.” International Journal of Information
Security (2019): 1-25.

III. Alanazi, Sultan T., Mohammed Anbar, Shankar Karuppayah, Ahmed K.
Al-Ani, and Yousef K. Sanjalawe. “Detection Techniques for DDoS
Attacks in Cloud Environment.” In Intelligent and Interactive Computing,
pp. 337-354. Springer, Singapore, 2019.
IV. Amjad, Aroosh, Tahir Alyas, Umer Farooq, and Muhammad Arslan Tariq.
“Detection and mitigation of DDoS attack in cloud computing using
machine learning algorithm.” (2019).
V. Bawany, NarmeenZakaria, and Jawwad A. Shamsi. “SEAL: SDN based
secure and agile framework for protecting smart city applications from
DDoS attacks.” Journal of Network and Computer Applications 145
(2019): 102381.
VI. Chen, Jinyin, Yi-tao Yang, Ke-ke Hu, Hai-bin Zheng, and Zhen Wang.
“DAD-MCNN: DDoS Attack Detection via Multi-channel CNN.”
In Proceedings of the 2019 11th International Conference on Machine
Learning and Computing, pp. 484-488. ACM, 2019.
VII. Cui, Jie, Mingjun Wang, Yonglong Luo, and Hong Zhong. “DDoS
detection and defense mechanism based on cognitive-inspired computing
in SDN.” Future Generation Computer Systems 97 (2019): 275-283.
VIII. Dayanandam, G., T. V. Rao, D. BujjiBabu, and S. Nalini Durga. “DDoS
Attacks—Analysis and Prevention.” In Innovations in Computer Science
and Engineering, pp. 1-10. Springer, Singapore, 2019.
IX. Demir, Kubilay, Ferdaus Nayyer, and Neeraj Suri. “MPTCP-H: A DDoS
attack resilient transport protocol to secure wide area measurement
systems.” International Journal of Critical Infrastructure Protection 25
(2019): 84-101.
X. Dimolianis, Marinos, Adam Pavlidis, Dimitris Kalogeras, and Vasilis
Maglaris. “Mitigation of Multi-vector Network Attacks via Orchestration
of Distributed Rule Placement.” In 2019 IFIP/IEEE Symposium on
Integrated Network and Service Management (IM), pp. 162-170. IEEE,
2019.
XI. Dong, Shi, Khushnood Abbas, and Raj Jain. “A Survey on Distributed
Denial of Service (DDoS) Attacks in SDN and Cloud Computing
Environments.” IEEE Access 7 (2019): 80813-80828.
XII. DORON, Ehud, B. E. N. Yotam, and David Aviv. “System and method
for out of path ddos attack detection.” U.S. Patent Application 16/212,042,
filed June 13, 2019.
XIII. Doron, Ehud, David Aviv, B. E. N. Yotam, and Lev Medvedovsky.
“Multi-tiered network architecture for mitigation of cyber-attacks.” U.S.
Patent Application 16/164,260, filed February 14, 2019.

XIV. Imran, Muhammad, Muhammad HanifDurad, Farrukh Aslam Khan, and
AbdelouahidDerhab. “Toward an optimal solution against denial of
service attacks in software defined networks.” Future Generation
Computer Systems 92 (2019): 444-453.
XV. Jaafar, Ghafar A., Shahidan M. Abdullah, and Saifuladli Ismail. “Review
of Recent Detection Methods for HTTP DDoS Attack.” Journal of
Computer Networks and Communications 2019 (2019).
XVI. Jing, Xuyang, Zheng Yan, Xueqin Jiang, and Witold Pedrycz. “Network
traffic fusion and analysis against DDoS flooding attacks with a novel
reversible sketch.” Information Fusion 51 (2019): 100-113.
XVII. Khalimonenko, Alexander A., Anton V. Tikhomirov, and Sergey V.
Konoplev. “System and method of determining ddos attacks.” U.S. Patent
Application 15/910,616, filed January 17, 2019.
XVIII. Ko, Ili, Desmond Chambers, and Enda Barrett. “Feature dynamic deep
learning approach for DDoS mitigation within the ISP
domain.” International Journal of Information Security (2019): 1-18.
XIX. Lopez, Alma D., Asha P. Mohan, and Sukumaran Nair. “Network Traffic
Behavioral Analytics for Detection of DDoS Attacks.” SMU Data Science
Review 2, no. 1 (2019): 14.
XX. Rahman, Obaid, Mohammad Ali GauharQuraishi, and Chung-Horng
Lung. “DDoS Attacks Detection and Mitigation in SDN Using Machine
Learning.” In 2019 IEEE World Congress on Services (SERVICES), vol.
2642, pp. 184-189. IEEE, 2019.
XXI. Reddy, Tirumaleswar, Daniel Wing, and Prashanth Patil. “Short term
certificate management during distributed denial of service attacks.” U.S.
Patent Application 10/104,119, filed October 16, 2018.
XXII. Saharan, Shail, and Vishal Gupta. “Prevention and Mitigation of DNS
based DDoS attacks in SDN Environment.” In 2019 11th International
Conference on Communication Systems & Networks (COMSNETS), pp.
571-573. IEEE, 2019.
XXIII. Sen, Sajib, Kishor Datta Gupta, and Md Manjurul Ahsan. “Leveraging
Machine Learning Approach to Setup Software-Defined Network (SDN)
Controller Rules During DDoS Attack.” In Proceedings of International
Joint Conference on Computational Intelligence, pp. 49-60. Springer,
Singapore, 2020.
XXIV. Swami, Rochak, Mayank Dave, and Virender Ranga. “Software-defined
Networking-based DDoS Defense Mechanisms.” ACM Computing
Surveys (CSUR) 52, no. 2 (2019): 28.

View Download

Abstract:

The use of energy storage devices and its technology has been the main focus to capture energy from sun and wind. This energy can be used during peak hours or when sun and wind resources are not available. Intermittent sources of energy play a significant part for this solution. Different storage technologies have been discussed in detail in this work. Hybrid Optimization Model for Electric Renewable (HOMER) PC demonstrating programming is being utilized to display the power framework, its physical conduct and its life cycle cost. Eight units of 850 kW wind turbines and 1 MW sunlight based PV modules were recognized as most practical to supply for 3MW load where the payback time of the framework is 3.4 years. Solar Simulink model has been made for graphical representation for its current and voltage relationship.

Keywords:

Solar Energy,Wind Energy,Hybrid System,Renewable Energy,

Refference:

I. Benedek, József, Tihamér-Tibor Sebestyén, and BlankaBartók.
“Evaluation of renewable energy sources in peripheral areas and
renewable energy-based rural development.” Renewable and
Sustainable Energy Reviews 90 (2018): 516-535.
II. Bertolotti, Fabio Paolo. “Wind power system for energy production.”
U.S. Patent 7,719,127, issued May 18, 2010.

III. Díaz-González, Francisco, Andreas Sumper, Oriol Gomis-Bellmunt,
and Roberto Villafáfila-Robles. “A review of energy storage
technologies for wind power applications.” Renewable and sustainable
energy reviews 16, no. 4 (2012): 2154-2171.
IV. European Wind Energy Association. The economics of wind energy.
EWEA, 2009.
V. Gwon, H, Hong J, Kim H, Seo D H, Jeon S, Kang, K., Recent progress
on flexible lithium rechargeable batteries. Energy & Environmental
Science 2014, 7 (2), pp. 538-551.
VI. International Renewable Energy Agency (IRENA), Renewable Energy
Technologies Cost Analysis Series, Volume 1: Power Sector, issue
55, wind Power, June 2012
VII. Kabir, Ehsanul, Pawan Kumar, Sandeep Kumar, Adedeji A. Adelodun,
and Ki-Hyun Kim. “Solar energy: Potential and future prospects.”
Renewable and Sustainable Energy Reviews 82 (2018): 894-900.
VIII. Koutroulis, Eftichios, George Petrakis, DionissiosHristopulos, Achilles
Tripolitsiotis, Nabila Halouani, Arij Ben Naceur, and Panagiotis
Partsinevelos. “Geo-Informatics for Optimal Design of Desalination
Plants Using Renewable Energy Sources: The DESiRES Platform
Paradigm.” In Advances in Remote Sensing and Geo Informatics
Applications, pp. 53-55. Springer, Cham, 2019.
IX. L.A. McFadden, P. Weissman, T. Johnson, Encyclopaedia of the Solar
System (Second Edition), New York: Academic Press, 2007.
X. National Energy Policy and Strategies of Pakistan, Ministry of Power
and Energy Government of Pakistan, October 2006, page 15.
XI. Non-Conventional Renewable Energy Tariff Announcement (Purchase
of electricity to the national grid under standard power purchase
agreements (SPPA) PUCSL for 2012 -2013.
XII. Ogawa, K. Toyota to Start Local Production of NiMH Battery Cells in
CHINA by end of 2016. Available online:
http://techon.nikkeibp.co.jp/atclen/news_en/15mk/030900433/
(accessed on 8 December 2017)
XIII. Solar Resource Assessment for Pakistan and the Maldives, Dave
Renne, Ray George, Bill Marion, Donna Heimiller. National
Renewable Energy Laboratory, August 2003, page 16.

XIV. Tareen, WajahatUllah Khan, ZuhaAnjum, Nabila Yasin, Leenah
Siddiqui, IfzanaFarhat, Suheel Abdullah Malik, SaadMekhilef et al.
“The prospective non-conventional alternate and renewable energy
sources in Pakistan—a focus on biomass energy for power generation,
transportation, and industrial fuel.” Energies 11, no. 9 (2018): 2431.
XV. Wind Energy Resource Atlas of Pakistan and the Maldives, D. Elliott,
M.Schwartz, G. Scott, S. Haymes, D. Heimiller, R. George. National
Renewable Energy Laboratory, August 2003, page 49.

View Download

Abstract:

Human body should resist against infections and also against infection causing organisms that enter our human body. In a human body for every microliter of blood, white blood cells has to range from 4,000 to 11,000 approximately. The main category of white blood cells are Neutrophils, Eosinophils, Basophils, Lymphocytes & Monocytes. In this paper, we are studying the characteristics of these different types of white blood cells & determining their Quality factors as well as transmission power analysis in a suitable Waveguide using Simulation results. The main immunity for the human body is provided by Neutrophils. It becomes very much necessary to know the properties , Information of these wbc’s which is a very important factor. This is determined by using an optical Biosensor.

Keywords:

Poweranalysis,Optical Biosensor,Waveguide,White Blood cells,Quality Factors,

Refference:

I. C.-F. So, Kup.-Sze. Choi, Tura. K. S. Wong, J. W. Y. Chung,
‘Recent advances in noninvasive glucose monitoring’, Medial.
Devices Evidence Res., vol. 5, pp. 45-52, 2012.
II. G Ongun, U Halici, K Atalay V Leblebicioglu, M Beksac, S. Beksac,
“Feature extraction and classificationof blood cells for an automated
differential blood count system”, Proc. IJCNN, vol. 4, pp. 2461-
2466, 2001.
III. Handin Robert I. Samuel E. Lux, Thomas P. Stossel, Blood:
‘Principles and Practice of Hematology, Philadelphia’:Lippincott
Williams and Wilkins, pp. 471, ISBN 9780781719933

IV. H. Michael. Heise, R. Winzen, Heinz. W. Siesler, Yukihiro. Ozaki,
S. Kawata, H. M. Heise, “Fundamental chemometric methods” in
Near Infrared Spectroscopy: Principles Instruments Applications,
Weinheim, Germany:Wiley, pp. 125-162, 2002.
V. Omid. Sarrafzadeh, H. Rabbani, A. M. Dehnavi, A. Talebi,
“Detecting different sub-types of acute myelogenous leukemia using
dictionary learning and sparse representation”, Image Processing
(ICIP) 2015 IEEE International Conference on, pp. 3339-3343,
2015.
VI. O. Olguin, Peter A. Gloor, Pentland, “Wearable sensors for
pervasive healthcare management”, Proc. 3rd International. Conf.
Pervasive Comput. Technol. Healthcare, pp. 1-4, 2009.
VII. M. Caldara, Claudio Colleoni, E. Guido, GluseppeRosace, Valerio.
Re, A. Vitali, “A wearable sensor platform to monitor sweat pH and
skin temperature”, Proc. IEEE Intl. Conf. Body Sensor Netw., pp. 1-
6, 2013.
VIII. S.S. Savkare, S.P. Narote, “Blood Cell Segmentation from
Microscopic Blood Images”, IEEE 2015 ICIP, pp. 502-505, 2015
IX. T. Anan et al., “High-speed 1.1-μm-range InGaAs VCSELs”, Proc.
Opt. Fiber Commun./Nat. Fiber Opt. Engineers Conf., pp. 1-3, 2008.
X. Z. J. V. Cohen, Shyqri. Haxha, A. Aggoun, “Pulse oximetry optical
sensor using oxygen-bound Haemoglobin”, Opt. Exp., volume. 24,
pp. 10115-10131, 2016.

View Download

Abstract:

The escalating number of novel network attacks warrants an approach where network data is processed in real-time for anomaly detection. Clustering is one of the foremost unsupervised learning algorithms in this domain that can detect outliers without prior knowledge of the data. However, cluster analysis precludes with it many challenges that need to be overcome for it to be adapted for real-time computation. This research paper outlines these challenges and the possible solutions to mitigate these challenges. We have also explored on a brief overview of clustering algorithms to give a high-level idea of cluster analysis.

Keywords:

Clustering methods,Intrusion detection,Network security,

Refference:

I. T. Report, “2015 INFORMATION SECURITY,” (2015).
II. B. S. Everitt, S. Landau, M. Leese, and D. Stahl, Cluster Analysis. Wiley,
(2011).
III. A. Fahad, N. Alshatri, Z. Tari, A. Alamri, I. Khalil, A. Y. Zomaya, S.
Foufou, and A. Bouras, “IEEE TRANSACTIONS ON A Survey of
Clustering Algorithms for Big Data : Taxonomy and Empirical Analysis,”
vol. 2, no. 3, (2014).
IV. J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of Massive Datasets,
2nd ed. New York, NY, USA: Cambridge University Press, (2014).
V. M. Daszykowski and B. Walczak, “Density-Based Clustering Methods,”
Compr. Chemom., vol. 2, pp. 635–654, (2010).
VI. Z. Ruijuan, C. Jing, Z. Mingchuan, Z. Junlong, and W. Qingtao, “User
abnormal behavior analysis based on neural network clustering,” J. China
Univ. Posts Telecommun., vol. 23, no. 3, pp. 29–44, (2016).
VII. P. K. R. Oger and A. Zimek, “Clustering High-Dimensional Data : A
Survey on Subspace Clustering , Pattern-Based Clustering , and
Correlation Clustering,” vol. 3, no. 1, (2009).
VIII. G. Chandrashekar and F. Sahin, “A survey on feature selection methods,”
Comput. Electr. Eng., vol. 40, no. 1, pp. 16–28, (2014).

IX. P. Chaovalit, A. Gangopadhyay, G. Karabatis, and Z. Chen, “Discrete
wavelet transform-based time series analysis and mining,” ACM Comput.
Surv., vol. 43, no. 2, pp. 1–37, (2011).
X. G. E. Hinton and R. R. Salakhutdinov, “Reducing the Dimensionality of
Data with Neural Networks,” vol. 504, (2006).
XI. C. Visual, N. Index, C. Vni, and C. Vni, “The Zettabyte Era : Trends and
Analysis,” no. June, pp. 2016–2021, (2017).
XII. M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, “Mining Data
Streams : A Review,” vol. 34, no. 2, pp. 18–26, (2005).
XIII. R. Perdisci, D. Ariu, and G. Giacinto, “Scalable fine-grained behavioral
clustering of HTTP-based malware,” Comput. Networks, vol. 57, no. 2, pp.
487–500, (2013).
XIV. L. Khan, M. Awad, and B. Thuraisingham, “A new intrusion detection
system using support vector machines and hierarchical clustering,” VLDB
J., vol. 16, no. 4, pp. 507–521, (2007).
XV. Y. Dang, B. Wang, R. Brant, Z. Zhang, M. Alqallaf, and Z. Wu, “Anomaly
Detection for Data Streams in Large-Scale Distributed Heterogeneous
Computing Environments,” in ICMLG2017 5th International Conference
on Management Leadership and Governance, (2017), p. 121.
XVI. J. Song, H. Takakura, Y. Okabe, and K. Nakao, “Toward a more practical
unsupervised anomaly detection system,” Inf. Sci. (Ny)., vol. 231, pp. 4–
14, (2013).
XVII. J. Dromard, G. Roudière, and P. Owezarski, “Online and Scalable
Unsupervised Network Anomaly Detection Method,” IEEE Trans. Netw.
Serv. Manag., vol. 14, no. 1, pp. 34–47, (2017).
XVIII. Ã. C. Ning, C. An, and Z. Long-Xiang, “An Incremental Grid Density-Based
Clustering Algorithm,” vol.1313, no. 101, pp. 1–7, (2002).

View Download